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500 articles matched your search for the keywords:
Catchment, Institution, Learning, South Africa, Water, Agent

Kinship Based Demographic Simulation of Societal Processes

Dwight Read
Journal of Artificial Societies and Social Simulation 1 (1) 1

Kyeywords: Multi-Agent Simulation, Cultural Knowledge, Hunting and Gathering Societies
Abstract: The social boundaries of small scale human societies are defined through culturally defined kin relations that transcend the specifics of the genealogical relationships produced through procreation. Kinship knowledge is culturally defined, distributed knowledge that provides structure for the persons produced through demographic processes. However, the interplay between the demographic system and the cultural system has been difficult to model. Genealogical data are static and do not show how the vagaries of demographic processes affect implementation of a culturally defined, conceptual system. Demographic simulations can provide the dynamic dimension, but usually lack information on how the changing demographic makeup of a population affects application of culturally defined rules relating to marriage, reproduction, residence and the like. This paper presents results obtained from implementation of a multi-agent, demographically driven, simulation of a hunting and gathering group in which each agent is imbued with cultural knowledge that affects decisions to be made about marriage, reproduction and place of residence. The goal is to assess the implications of demographic processes, ego-centered decision making, and culturally determined structures (kin relations, social groupings and the like) for the resulting social system. Questions addressed in the simulation are based on ethnographic observations and it is shown that the simulation provides an effective means to assess the validity of hypotheses about the ethnographic observations.

Social Order in Artificial Worlds

Michael Macy
Journal of Artificial Societies and Social Simulation 1 (1) 4

Kyeywords: Cooperation, Evolutionary Models, Artificial Agents, Altruism
Abstract: How does social order emerge among autonomous but interdependent agents? The expectation of future interaction may explain cooperation based on rational foresight, but the "shadow of the future" offers little leverage on the problem of social order in "everyday life" -- the habits of association that generate unthinking compliance with social norms. Everyday cooperation emerges not from the shadow of the future but from the lessons of the past. Rule-based evolutionary models are a promising way to formalize this process. These models may provide new insights into emergent social order -- not only prudent reciprocity, but also expressive and ritual self-sacrifice for the welfare of close cultural relatives.

Societies, Cultures and Fisheries from a Modeling Perspective

Gérard Weisbuch and Guillemette Duchateau-Nguyen
Journal of Artificial Societies and Social Simulation 1 (2) 2

Kyeywords: Fisheries, Learning, Institutions, Beliefs, Dynamics, Environment
Abstract: Cultures can be viewed as sets of beliefs and techniques allowing societies to cope with their environment. We here propose simple and explicit schemes showing how fishermen could encode beliefs about a renewable resource, fish. We then discuss the dynamics of the society, represented by economic and cultural variables, coupled to the fishery represented by fish abundance. According to different coding schemes and sets of parameters, several dynamical regimes are observed, including one with endogenous crises.

Simulation Tools for Social Scientists: Building Agent Based Models with SWARM

Pietro Terna
Journal of Artificial Societies and Social Simulation 1 (2) 4

Kyeywords: Agent Based Models (ABM), Chaos, Intelligent Agents, Social Simulation, Swarm
Abstract: Social scientists are not computer scientists, but their skills in the field have to become better and better to cope with the growing field of social simulation and agent based modelling techniques. A way to reduce the weight of software development is to employ generalised agent development tools, accepting both the boundaries necessarily existing in the various packages and the subtle and dangerous differences existing in the concept of agent in computer science, artificial intelligence and social sciences. The choice of tools based on the object oriented paradigm that offer libraries of functions and graphic widgets is a good compromise. A product with this kind of capability is Swarm, developed at the Santa Fe Institute and freely available, under the terms of the GNU license. A small example of a model developed in Swarm is introduced, in order to show directly the possibilities arising from the use of these techniques, both as software libraries and methodological guidelines. With simple agents - interacting in a Swarm context to solve both memory and time simulation problems - we observe the emergence of chaotic sequences of transaction prices.

Simulation of Order Fulfillment in Divergent Assembly Supply Chains

Troy J Strader, Fu-ren Lin and Michael J Shaw
Journal of Artificial Societies and Social Simulation 1 (2) 5

Kyeywords: Supply Chain Management, Multi-Agent Simulation, Swarm, Decision Support Systems, Electronic Commerce, Computer Industry, Electronics Industry
Abstract: Management of supply chains is a difficult task involving coordination and decision-making across organizational boundaries. Computational modeling using multi-agent simulation is a tool that can provide decision support for supply chain managers. We identify the components of a supply chain model and implement it in the Swarm multi-agent simulation platform. The model is used to study the impact of information sharing on order fulfillment in divergent assembly supply chains (commonly associated with the computer and electronics industries). We find that efficient information sharing enables inventory costs to be reduced while maintaining acceptable order fulfillment cycle times. This is true because information, which provides the basis for enhanced coordination and reduced uncertainty, can substitute for inventory.

Design Versus Cognition: the Interaction of Agent Cognition and Organizational Design on Organizational Performance

Kathleen Carley, Michael J. Prietula and Zhiang (John) Lin
Journal of Artificial Societies and Social Simulation 1 (3) 4

Kyeywords: Organization Theory, Agent Cognition, Validation
Abstract: The performance of organizations with different structures are examined using multiple computer simulation models, experimental data, and archival data focused on the relation between the way in which the organization is coordinated and its performance. These variations enable the exploration of the role of agent capabilities, and the way in which agent capability and coordination interact to effect performance. Both micro and macro organizational behavior are examined. Results suggest that simpler models of agents are needed at macro levels and more detailed, more cognitively accurate models are needed at micro or small group levels, to generate the same predictive accuracy.

SOCIONICS: Introduction and Potential

Heinz-Jürgen Müller, Thomas Malsch and Ingo Schulz-Schaeffer
Journal of Artificial Societies and Social Simulation 1 (3) 5

Kyeywords: Socionics, Agent Technology, Multi-Agents Systems, Sociology
Abstract: SOCIONICS is an interdisciplinary research framework which has been recently established for six years by the German Research Foundation (DFG). Up to 16 projects cooperating in a tandem-structure with at least one partner from Computer Science and one from Sociology will form a virtual research unit powered by the DFG. This report gives a brief introduction to Socionics and its basic research questions. A short discussion about the potentials and applications of a socionic based technology is presented.

Critical Incident Management: an Empirically Derived Computational Model

Scott Moss
Journal of Artificial Societies and Social Simulation 1 (4) 1

Kyeywords: Crisis Management, Agent Cognition, Model Verification, Simulation Methodology
Abstract: The main purpose of this paper is to demonstrate an empirical approach to social simulation. The systems and the behaviour of middle-level managers of a real company are modelled. The managers' cognition is represented by problem space architectures drawn from cognitive science and an endorsements mechanism adapted from the literature on conflict resolution in rulebased systems. Both aspects of the representation of cognition are based on information provided by domain experts. Qualitative and numerical results accord with the views of domain experts.

Extending Ascribed Intensional Ontologies with Taxonomical Relations in Anthropological Descriptions of Multi-Agent Systems

Rafael H Bordini, John A. Campbell and Renata Vieira
Journal of Artificial Societies and Social Simulation 1 (4) 3

Kyeywords: Interoperability of Multi-Agent Systems, Pragmatic Intensionality, Cultural Anthropology, Inference of Taxonomies
Abstract: The paper presents an approach to the description of ontologies used in Multi-Agent Systems as a means to allow interoperability of such systems. It is inspired by a pragmatic theory of intensionality worked out as part of an anthropological approach to agent migration. A new formalisation of how an intensional ontology can be ascribed to a society of agents is presented, together with a first formalisation of the recovery of taxonomical relations from such ontologies. This process of discovering taxonomies is inspired by ethnographic studies in social anthropology. The formalisations are developed using a framework for agent theories, based on the Z specification language. Further, the approach is illustrated by the ascription of an ontology and associated taxonomies for an exotic application: the game of cricket. Finally, several issues related to this approach are discussed.

Special Interest Group on Agent-Based Social Simulation

Rosaria Conte and Scott Moss
Journal of Artificial Societies and Social Simulation 2 (1) 4

Kyeywords: Agent-Based Social Simulation, Special Interest Group, AgentLink
Abstract:

The Origin of Institutions: Socio-Economic Processes, Choice, Norms and Conventions

José Castro Caldas and Helder Coelho
Journal of Artificial Societies and Social Simulation 2 (2) 1

Kyeywords: Institutional Economics, Agent Modelling, Socio-Economic Simulation, Evolutionary Algorithms
Abstract: Institutions, the way they are related to the behaviour of the agents and to the aggregated performance of socio-economic systems, are the topic addressed by this essay. The research is based on a particular concept of a bounded rational agent living in society and by a population based simulation model that describes the processes of social learning. From simple co-ordination problems, where conventions spontaneously emerge, to situations of choice over alternative constitutional rules, simulation was used as a means to test the consistency and extract the implications of the models. Institutions, as solutions to recurring problems of social interaction, are both results and preconditions for social life, unintended outcomes and human devised constraints. In an evolutionary setting no support is found for the deep rooted beliefs about the 'naturally' beneficial outcomes generated by 'invisible-hand' processes or by any alternative Hobbesian meta-agency.

An Integrated Approach to Simulating Behavioural Processes: a Case Study of the Lock-in of Consumption Patterns

Marco A. Janssen and Wander Jager
Journal of Artificial Societies and Social Simulation 2 (2) 2

Kyeywords: Lock-In, Multi-Agent Modelling, Social Psychology, Need Satisfaction, Consumer Behaviour
Abstract: Lock-in denotes a phenomenon of monopolistic dominating technologies or consumer goods in a certain market. These lock-ins cannot be explained by superior characteristics of the good or technology. Previous studies mainly used probabilistic models to study lock-in effects. In this paper an integrated conceptual model of consumer behaviour is used to identify relevant processes of lock-in dynamics of consumption patterns. An agent-based model is developed to simulate consumats, artificial consumers, who are confronted with two similar products. We found two types of lock-in, namely, a spatial lock-in and a global level lock-in. The spatial lock-in related to the spatial patterns that occur in consumption patterns and relates to the satisfaction of the need for identity. The global lock-in relates to price effects and occurs only if individual preferences are not significantly weighted in the cognitive processing.

Concepts for an Agent-Based Framework for Interdisciplinary Social Science Simulation

Elke Mentges
Journal of Artificial Societies and Social Simulation 2 (2) 4

Kyeywords: Modelling Language, Software System, Multi-Agent System, Modelling Interactions, Toolkit
Abstract:

The Multi-Agent Modelling Language and the Model Design Interface

László Gulyás, Tamás Kozsik and John B. Corliss
Journal of Artificial Societies and Social Simulation 2 (3) 8

Kyeywords: Social Science Simulation, Agent-Based Modelling, Integrated Modelling Environment
Abstract: While computer models provide many advantages over traditional experimental methods, they also raise several problems. The process of software development is a complicated task with high potential for errors, especially when it is carried out by scientists holding their expertise in other fields than computer science. On the other hand, the process of creating computer simulations of social systems which reflect the reality of such systems requires insights considerably beyond expertise in computer science. The Multi-Agent Modelling Language (MAML) is one of the efforts to ease these difficulties. In its current version, MAML is a macro-language for Swarm (a freely distributed toolset under development at SFI), but it is also part of a larger Swarm-independent framework. Also, the design of MAML, while influenced by concepts from Swarm, is general enough to allow for later extension of the supported simulation kernels. This paper gives an overview of the mentioned larger framework, with special emphasis on MAML and its graphical CASE tool, the Model Design Interface.

Agent-Based Modelling of Collective Identity: Testing Constructivist Theory

Ian Lustick
Journal of Artificial Societies and Social Simulation 3 (1) 1

Kyeywords: Agent-Based Modeling, Identity, Constructivism
Abstract: Agent-based modeling is an alternative and complementary approach to the study of political identities, including ethnicity and nationalism. By generating many runs with different initial conditions large data sets of virtual histories can be accumulated. This paper presents the ABIR (Agent-Based Identity Repertoire) model which seeks to refine, elaborate, and test constructivist theories of identity and identity change. In this model agents with activated identities interact on a landscape. These agents have repertoires of latent identities. A simple set of micro rules, conforming to constructivist theory's standard propositions about the fluidity, multiplicity, and institutionalizability of identities, as well as their responsiveness to changing incentive structures, determines in any particular interaction what identities will be activated, deactivated, or maintained. Macro-patterns that emerge from these myriad micro-interactions can then be systematically studied. Experiments reported in this paper focus how variation in the size of agent repertoires can affect tension reduction and aggregation across the landscape. Results suggest that tipping and cascade effects are much more likely when a small number of exclusivist identities are present in a population.

Liberal Order for Software Agents? an Economic Analysis

Dirk Nicolas Wagner
Journal of Artificial Societies and Social Simulation 3 (1) forum/2

Kyeywords: Software Agents, Multi-Agent Systems, Economics, Liberalism, Social Order, Spontaneous Order, Adaptation, Unpredictability
Abstract: Computer science and economics face a common problem, the unpredictability of individual actors. Common problems do not necessarily imply a common understanding so that it is important to note that the agent-paradigm can function as an interface between Computer science and economics. On this basis, economics is able to provide valuable insights for the design of artificial societies that are intended to constructively deal with individual unpredictability. It is argued that liberal rules and adaptive actors are promising concepts in order to achieve spontaneous social order among software-agents

Modelling Social Systems As Complex: Towards a Social Simulation Meta-Model

Chris Goldspink
Journal of Artificial Societies and Social Simulation 3 (2) 1

Kyeywords: Complex Systems, Autopoiesis, Social Simulation, Cognition, Agents, Modelling. Meta-Model, Ontology
Abstract: There is growing interest in extending complex systems approaches to the social sciences. This is apparent in the increasingly widespread literature and journals that deal with the topic and is being facilitated by adoption of multi-agent simulation in research. Much of this research uses simple agents to explore limited aspects of social behaviour. Incorporation of higher order capabilities such as cognition into agents has proven problematic. Influenced by AI approaches, where cognitive capability has been sought, it has commonly been attempted based on a 'representational' theory of cognition. This has proven computationally expensive and difficult to implement. There would be some benefit also in the development of a framework for social simulation research which provides a consistent set of assumptions applicable in different fields and which can be scaled to apply to simple and more complex simulation tasks. This paper sets out, as a basis for discussion, a meta-model incorporating an 'enactive' model of cognition drawing on both complex system insights and the theory of autopoiesis. It is intended to provide an ontology that avoids some of the limitation of more traditional approaches and at the same time providing a basis for simulation in a wide range of fields and pursuant of a wider range of human behaviours.

Simulating Common Pool Resource Management Experiments with Adaptive Agents Employing Alternate Communication Routines

Peter Deadman, Edella Schlager and Randy Gimblett
Journal of Artificial Societies and Social Simulation 3 (2) 2

Kyeywords: Common Pool Resources, Intelligent Agents, Simulation, Bounded Rationality, Communication
Abstract: This paper describes the development of a series of intelligent agent simulations based on data from previously documented common pool resource (CPR) experiments. These simulations are employed to examine the effects of different institutional configurations and individual behavioral characteristics on group level performance in a commons dilemma. Intelligent agents were created to represent the actions of individuals in a CPR experiment. The agents possess a collection of heuristics and utilize a form of adaptation by credit assignment in which they select the heuristic that appears to yield the highest return under the current circumstances. These simulations allow the analyst to specify the precise initial configuration of an institution and an individual's behavioral characteristics, so as to observe the interaction of the two and the group level outcomes that emerge as a result. Simulations explore settings in which there is no communication between agents, as well as the relative effects on overall group behavior of two different communication routines. The behavior of these simulations is compared with documented CPR experiments. Future directions in the development of the technology are outlined for natural resource management modeling applications.

Using AgentSheets to Teach Simulation to Undergraduate Students

Joaquim Carvalho
Journal of Artificial Societies and Social Simulation 3 (3) forum/2

Kyeywords: Simulation, Teaching, User Interfaces to Agent Based Models
Abstract: The AgentSheets simulation software has been used for the last two years in a course for undergraduate students. The ease of use and extreme care put into the interface makes this tool a classroom success, allowing students to have hands-on experience of model construction without the overhead of learning complicated frameworks or programming languages. The limitations of the tool, in particular those that make difficult the construction of more complex models, are reviewed.

Some Strategies for the Simulation of Vocabulary Agreement in Multi-Agent Communities

Juan de Lara Jaramillo and Manuel Alfonseca
Journal of Artificial Societies and Social Simulation 3 (4) 2

Kyeywords: Multi-Agent Systems, Agent-Based Simulation, Self-Organization, Language
Abstract: In this paper, we present several experiments of belief propagation in multi-agent communities. Each agent in the simulation has an initial random vocabulary (4 words) corresponding to each possible movement (north, south, east and west). Agents move and communicate the associated word to the surrounding agents, which can be convinced by the 'speaking agent', and change their corresponding word by 'imitation'. Vocabulary uniformity is achieved, but strong interactions and competition can occur between dominant words. Several moving and trusting strategies as well as agent roles are analyzed.

Narrative Intelligence from the Bottom Up: a Computational Framework for the Study of Story-Telling in Autonomous Agents

Kerstin Dautenhahn and Steve Coles
Journal of Artificial Societies and Social Simulation 4 (1) 1

Kyeywords: Autobiographic Agents, Narrative Intelligence, Autonomous Robots
Abstract: This paper addresses Narrative Intelligence from a bottom up, Artificial Life perspective. First, different levels of narrative intelligence are discussed in the context of human and robotic story-tellers. Then, we introduce a computational framework which is based on minimal definitions of stories, story-telling and autobiographic agents. An experimental test-bed is described which is applied to the study of story-telling, using robotic agents as examples of situated, autonomous minimal agents. Experimental data are provided which support the working hypothesis that story-telling can be advantageous, i.e. increases the survival of an autonomous, autobiographic, minimal agent. We conclude this paper by discussing implications of this approach for story-telling in humans and artifacts.

Introducing Emotions into the Computational Study of Social Norms: a First Evaluation

Alexander Staller and Paolo Petta
Journal of Artificial Societies and Social Simulation 4 (1) 2

Kyeywords: Social Norms, Appraisal Theory of Emotions ,Process Model of Emotions, Layered Agent Architecture, Simulation, JAM (BDI Agent Architecture), Micro-Macro Link, Aggression Control Case Study, Deontic Reasoning and Human Behaviour Models
Abstract: It is now generally recognised that emotions play an important functional role within both individuals and societies, thereby forming an important bond between these two levels of analysis. In particular, there is a bi-directional interrelationship between social norms and emotions, with emotions playing an instrumental role for the sustenance of social norms and social norms being an essential element of regulation in the individual emotional system. This paper lays the foundations for a computational study of this interrelationship, drawing upon the functional appraisal theory of emotions. We describe a first implementation of a situated agent architecture, TABASCOJAM, that incorporates a simple appraisal mechanism and report on its evaluation in a well-known scenario for the study of aggression control as a function of a norm, that was suitably extended. The simulation results reported in the original aggression control study were successfully reproduced, and consistent performances were achieved for extended scenarios with conditional norm obeyance. In conclusion, it is argued that the present effort indicates a promising lane towards the necessary abandonment of logical models for the explanation and simulation of human social behaviour.

Intelligent Social Learning

Rosaria Conte and Mario Paolucci
Journal of Artificial Societies and Social Simulation 4 (1) 3

Kyeywords: Social Learning, Social Facilitation, Cognitive Modeling
Abstract: One of the cognitive processes responsible for social propagation is social learning, broadly meant as the process by means of which agents' acquisition of new information is caused or favoured by their being exposed to one another in a common environment. Social learning results from one or other of a number of social phenomena, the most important of which are social facilitation and imitation. In this paper, a general notion of social learning will be defined and the main processes that are responsible for it, namely social facilitation and imitation, will be analysed in terms of the social mental processes they require. A brief analysis of classical definitions of social learning is carried on, showing that a systematic and consistent treatment of this notion is still missing. A general notion of social learning is then introduced and the two main processes that may lead to it, social facilitation and imitation, will be defined as different steps on a continuum of cognitive complexity. Finally, the utility of the present approach is discussed. The analysis presented in this paper draws upon a cognitive model of social action (cf. Conte & Castelfranchi 1995; Conte 1999). The agent model that will be referred to throughout the paper is a cognitive model, endowed with mental properties for pursuing goals and intentions, and for knowledge-based action. To be noted, a cognitive agent is not to be necessarily meant as a natural system, although many examples examined in the paper are drawn from the real social life of humans. Cognitive agents may also be artificial systems endowed with the capacity for reasoning, planning, and decision-making about both world and mental states. Finally, some advantages of intelligent social learning in agent systems applications are discussed. Keywords:

What is Ascape and Why Should You Care?

Miles Parker
Journal of Artificial Societies and Social Simulation 4 (1) 5

Kyeywords: Agent-Based Simulation, Computer Modelling, Software Frameworks, Java
Abstract: Ascape is a framework designed to support the development, visualization, and exploration of agent based models. In this article I will argue that agent modeling tools and Ascape, in particular, can contribute significantly to the quality, creativity, and efficiency of social science simulation research efforts. Ascape is examined from the perspectives of use, design, and development. While Ascape has some unique design advantages, a close examination should also provide potential tool users with more insight into the kinds of services and features agent modeling toolkits provide in general.

"ArrierosAlife" a Multi-Agent Approach Simulating the Evolution of a Social System: Modeling the Emergence of Social Networks with "Ascape"

Klaus Auer and Timothy Norris
Journal of Artificial Societies and Social Simulation 4 (1) 6

Kyeywords: Cellular Automata, Multi-Agent Model, Evolution, Social Networks, Object Oriented Programming Language, Artificial Landscape
Abstract: The behavior of cellular automata is a very close representation of the evolution of complex social systems. We developed the simulation model "ArrierosAlife" to explore the behavior of changes in social networks over time. The model is based on empirical data, a result out of a longitudinal field work. The focus of this research is a comparison of network changes over time in the "real world" compared with the emergence of social networks in an artificial society. "Ascape" was used as a modeling frame work to facilitate the development and analysis of the simulation model. We will give a brief overview of the developed model and describe the experiences using "Ascape" as a framework.

Game Theory: Limitations and an Alternative

Scott Moss
Journal of Artificial Societies and Social Simulation 4 (2) 2

Kyeywords: Game Theory, Agent, Multi Agent System, Simulation, Market, Intermediation
Abstract: The purpose of this paper is to describe current practice in the game theory literature, to identify particular characteristics that ensure the literature is remote from anything we observe and to demonstrate an alternative drawn from agent based social simulation. The key issue is the process of social interaction among agents. A survey of game theoretic models found no models representing interaction among more than three agents, though sometimes more agents were involved in a round robin tournament. An ABSS model is reported in which there is a dense pattern of interaction among agents and outputs from the model are shown to have the same statistical signature as high-frequency data from competitive retail and financial markets. Moreover, the density of agent interaction is seen to be necessary both to obtain the validating statistical signature and for simulated market efficiency. As far as competitive markets are concerned, game theoretic models evidently assume away the source of the properties observed in real high frequency data and also the properties required for market efficiency.

Modelling the Emergence of Resource-Sharing Conventions: an Agent-Based Approach

Olivier Thebaud and Bruno Locatelli
Journal of Artificial Societies and Social Simulation 4 (2) 3

Kyeywords: Conventions, Natural Resources, Multi-Agent Systems
Abstract: This paper presents an agent-based simulation framework for the analysis of the emergence of resource-sharing conventions. The model is based on Sugden's article entitled "Spontaneous order", which looks at the conditions under which conventions regarding access to a natural resource become established. The aim of the model is to explore the potential of agent-based modelling for the analysis of these questions. First, the structure of a simulation model based on the example of driftwood collection used by Sugden is presented. Second, simulations of various scenarios about the behavioural rules followed by agents are described, and simulation results are presented. The paper concludes with a brief discussion of the advantages of agent-based models for analysing social processes such as the emergence of conventions regulating access to natural resources.

Intervening to Achieve Co-Operative Ecosystem Management: Towards an Agent Based Model

Jim Doran
Journal of Artificial Societies and Social Simulation 4 (2) 4

Kyeywords: Software Agent, Agent-Based Modelling, Integrated Watershed Management, Sustainability, Fraser River, Intervention Strategy
Abstract: We propose an advanced agent-based modelling approach to ecosystem management, informed and motivated by consideration of the Fraser River watershed and its management problems. Agent-based modelling is introduced, and a three-stage computer-based research programme is formulated, the focus of which is on how best to intervene to cause stakeholders to co-operate effectively in ecosystem management, and on the objective discovery and comparison of intervention strategies by way of computer experimentation. The agent-based model outlined is technically relatively complex, and several potential difficulties in its detailed development are discussed. Types of ecosystem intervention strategy that might plausibly be discovered or recommended by the model are projected and compared with those currently advocated in the literature.

Role-Playing Games for Opening the Black Box of Multi-Agent Systems: Method and Lessons of Its Application to Senegal River Valley Irrigated Systems

Olivier Barreteau, François Bousquet and Jean-Marie Attonaty
Journal of Artificial Societies and Social Simulation 4 (2) 5

Kyeywords: Multi-Agent Systems, Role-Playing Games, Validation, Negotiation Support Tool, Legitimisation, Irrigated Systems
Abstract: Multi-agent systems and role playing games have both been developed separately and offer promising potential for synergetic joint use in the field of renewable resource management, for research, training and negotiation support. While multi-agent systems may give more control over the processes involved in role playing games, role playing games are good at explaining the content of multi-agent systems. The conversion of one tool to another is quite easy but organisation of game sessions is more difficult. Both these tools have been used jointly in a fully described experiment in the Senegal river valley for issues of co-ordination among farmers. Role-playing games first enabled us to work on the validation of the MAS. Subsequently, the combination of both tools has proved to be an effective discussion support tool.

A Bargaining Model to Simulate Negotiations Between Water Users

Sophie Thoyer, Sylvie Morardet, Patrick Rio, Leo Simon, Rachael Goodhue and Gordon Rausser
Journal of Artificial Societies and Social Simulation 4 (2) 6

Kyeywords: Game Theory, Bargaining, Water Management, Negotiation, Decentralisation
Abstract: The French water law of 1992 requires that regulations on water use and water management be negotiated collectively and locally in each river sub-basin. Decision-makers therefore need new tools to guide the negotiation process which will take place between water users. A formal computable bargaining model of multilateral negotiations is applied to the Adour Basin case, in the South West of France, with seven aggregate players (three "farmers", two "environmental lobbies", the water manager, the taxpayer) and seven negotiation variables (three individual irrigation quotas, the price of water, the sizes of three dams). The farmers' utility functions are estimated with hydraulic and economic models. A sensibility analysis is conducted to quantify the impact of the negotiation structure (political weights of players, choice of players...) on game outcomes. The relevance of the bargaining models as negotiation-support tools is assessed.

From Social Monitoring to Normative Influence

Rosaria Conte and Frank Dignum
Journal of Artificial Societies and Social Simulation 4 (2) 7

Kyeywords: Norms, Multi Agent Systems, Imitation, Social Control, Social Cognition
Abstract: This paper is intended to analyse the concepts involved in the phenomena of social monitoring and norm-based social influence for systems of normative agents. These are here defined as deliberative agents, representing norms and deciding upon them. Normative agents can use the norms to evaluate others' behaviours and, possibly, convince them to comply with norms. Normative agents contribute to the social dynamics of norms, and more specifically, of norm-based social control and influence. In fact, normative intelligence allows agents to Check the efficacy of the norms (the extent to which a norm is applied in the system in which it is in force), and possibly Urge their fellows to obey the norms. The following issues are addressed: What is norm-based control? Why and how do agents exercise control on one another? What role does it play in the spread of norms?

The Creation of a Reputation in an Artificial Society Organised by a Gift System

Juliette Rouchier, Martin O'Connor and François Bousquet
Journal of Artificial Societies and Social Simulation 4 (2) 8

Kyeywords: Gift, Reputation, Multi-Agent Systems
Abstract: This paper describes simulations in an artificial society in which autonomous agents exchange gifts. In this society agents perform simple acts that are looked at by the others and are analysed so that a common image is created for each agent (a reputation). The model is based on numerous descriptions of non-merchant exchange systems, which are very interesting for ethnologists as well as for economists: they appear to be important for circulation of goods and to insure the reproduction of social links and values. In the system built the agents must make a gift at each time-step. There exist two kinds of gifts and two corresponding kinds of reputation: the agents either give to share or to be prestigious. Since gifts are received according to status, receiving a gift is as important for a reputation as making one. Each agent is characterised by its ''motivation'' to acquire the reputation of being a sharing agent or a prestigious agent. It is also characterised by its ''esteem'', to decide if it will be able to do the gift it wants to do for a time-step. These two characteristics of an agent can be stable during the simulation, but can also evolve according to its history. We study here the different patterns that can appear in the societies, in terms of generation of reputation, and of histories over time. A huge range of these patterns can be observed, depending on the choice made for the parameters. In some cases the agents cannot be individually distinguished, in other cases they can: but, in any case any individual behaviours that emerge have to be sustained by a collective specification that points out more or less the way agents value each reputation.

Lake Anderson Revisited by Agents

Michael Moehring and Klaus G. Troitzsch
Journal of Artificial Societies and Social Simulation 4 (3) 1

Kyeywords: Water Management, Stepwise Refinement, Multilevel Modelling
Abstract: In our paper, we replicate simulation experiments carried out some 30 years ago by Jay M. Anderson who then tried to find out which measures should be taken to avoid the eutrophication of a lake. In his DYNAMO model, he simulated the development of a lake under cultural eutrophication, i.e. mainly by the discharge of fertilisers from agriculture. He designed a number of policies and applied them as an experimenter. In our model, some of the policies suggested by Anderson are taken by a government (or, alternatively, by a number of regional governments) who are in charge of the region(s) around the lake. We define rules which the authorities apply when they find that some of the variables which describe the state of the lake exceed (or fall below) certain thresholds. In a next step of refinement of the model, authorities will still define thresholds, but will not take all possible measures themselves, but charge the farmers with taxes when they exceed the fertiliser discharge limit. Farmers will then be endowed with rules which tell them whether it is better for them to pay the taxes or take appropriate measures against eutrophication themselves. The rationale of our paper is to show how stepwise refinement of a model can contribute to our understanding of the interactions between water resource decision makers of different levels and the natural environment. It is part of our efforts to develop agent-based models for application to issues of water treatment - as is done in the FIRMA project

Modelling Strategies for Water Supply Companies to Deal with Nitrate Pollution

Yvonne Haffner and Stefan Gramel
Journal of Artificial Societies and Social Simulation 4 (3) 11

Kyeywords: Nitrate, Water Supply, Object-Oriented Simulation, Environmental Sustainability
Abstract: The computer based model presented in this paper regards strategies for water supply companies to deal with nitrate pollution of groundwater aquifers. In Germany, as well as in many other European regions, nitrate pollution is one of the most important problems for water protection and water supply. The simulation of an existing water supply company shows a high level of conformance between simulation results and economic data of the company. The simulation of scenarios with high nitrate pollution shows important differences between the strategies of using deeper aquifers, of technical treatment of raw water, and of co-operation with the agriculture regarding costs and environmental sustainability. Also these results reflect fairly well the situation in Germany.

Economic Performance, Inter-Firm Relations and Local Institutional Engineering in a Computational Prototype of Industrial Districts

Flaminio Squazzoni and Riccardo Boero
Journal of Artificial Societies and Social Simulation 5 (1) 1

Kyeywords: Agent-Based Computational Model, Industrial Districts, Technological Change, Local Institutional Engineering.
Abstract: Industrial districts can be conceived as complex systems characterised by a network of interactions amongst heterogeneous, localised, functionally integrated and complementary firms. In a previous paper, we have introduced an industrial district computational prototype, showing that the economic performance of an industrial district proceeds to the form through which firms interact and co-ordinate each others. In this paper, we use such computational framework to experiment different options of “local institutional engineering”, trying to understand how specific “supporting institutions” could perform macro-collective activities, such as, i.e., technology research, transfer and information, improving the technological adaptation of firms. Is a district more than a simple aggregation of localised firms? What can explain the economic performance of firms localised into the same space? Could some options of “local institutional engineering” improve the performance of a district? Could such options set aside the problem of how firms dynamically interact? These are questions explored in this paper.

Agent Based Social Simulation: a Computer Science View

Paul Davidsson
Journal of Artificial Societies and Social Simulation 5 (1) 7

Kyeywords: Agent-based social simulation, agent-based computing, computer simulation, social science
Abstract: A description of the area of Agent Based Social Simulation (ABSS) from a computer scientist’s perspective is presented. We begin by defining ABSS by positioning it with respect to the three research areas that it is related to, i.e., agent-based computing, the social sciences, and computer simulation. We then discuss the role of ABSS and how it may aid cross-fertilisation between these areas.

The Role of Oblivion, Memory Size and Spatial Separation in Dynamic Language Games

Juan de Lara Jaramillo and Manuel Alfonseca
Journal of Artificial Societies and Social Simulation 5 (2) 1

Kyeywords: Agent-based simulation, language games, self-organisation, communication
Abstract: In this paper we present some multiagent simulations in which the individuals try to reach a uniform vocabulary to name spatial movements. Each agent has initially a random vocabulary that can be modified by means of interactions with the other agents. As the objective is to name movements, the topic of conversation is chosen by moving. Each agent can remember a finite number of words per movement, with certain strength. We show the importance of the forgetting process and memory size in these simulations, discuss the effect of the number of agents on the time to agree and present a few experiments where the evolution of vocabularies takes place in a divided range.

An Agent-Based Model of Ethnic Mobilisation

Armano Srbljinovic, Drazen Penzar, Petra Rodik and Kruno Kardov
Journal of Artificial Societies and Social Simulation 6 (1) 1

Kyeywords: Agent-Based Modelling; Ethnic Identity; Ethnic Mobilisation
Abstract: In this paper we used the methodology of agent-based modelling to help explaining why populations with very similar socio-demographic characteristics sometimes exhibit great differences in ethnic mobilisation levels during mobilisation processes. This agent-based model of ethnic mobilisation was inspired and developed by combining and extending several theories, ideas and modelling constructs that were already used in agent-based modelling of social processes. The model has been specifically adapted to account for some of the most important characteristics of ethnic mobilisation processes that took place in the former Yugoslavia. Results obtained by experimenting with the model indicate that the observed differences in mobilisation levels across populations may sometimes not be related to the variations within any particular socio-demographic factor, but merely to random differences in the initial states of the individuals. In this model these random differences primarily relate to the degrees of importance that individuals attach to their ethnic identity, as well as to the layout of social networks.

Evolutionary Development and Learning: Two Facets of Strategy Generation

Ilan Fischer
Journal of Artificial Societies and Social Simulation 6 (1) 7

Kyeywords: Strategy, Evolution, Learning, Genetic-algorithm, Tit-For-Tat, Noise, Errors
Abstract: The study examines two approaches to the development of behavioral strategies: i) the evolutionary approach manifested in a Genetic Algorithm, which accounts for gradual development and simultaneous refinement of an entire population; and ii) the behavioral learning approach, which focuses on reinforcements at the individual's level. The current work is part from an ongoing project dealing with the development of strategic behavior. The reported study evaluates the potential of differential reinforcements to provide probabilistic noisy Tit-For-Tat strategies with the motivation to adopt a pure Tit-For-Tat strategy. Results show that provocability and forgiveness, the traits that account for Tit-For-Tat's successes, also prevent it from gaining relative fitness and become an attractor for noisy (non-perfect) Tit-For-Tat strategies.

A Step-By-Step Approach to Building Land Management Scenarios Based on Multiple Viewpoints on Multi-Agent System Simulations

Michel Etienne, Christophe Le Page and Mathilde Cohen
Journal of Artificial Societies and Social Simulation 6 (2) 2

Kyeywords: Natural resource management; Management scenarios; Agent-Based Model, Viewpoints
Abstract: A multi-agent system was developed to simulate strategies of natural resource management in the Causse Méjan, a limestone plateau dominated by a rare grassland-dominated ecosystem endangered by pine invasion. To stimulate the emergence of alternative long-term management strategies for the sheep farms and the woodlands, contrasting dynamic viewpoints on land resources were designed at different space scales. To begin with, they were individually used to validate the model with each type of main stakeholders (foresters, farmers and the National Park of Cévennes rangers), to improve it and to propose individual scenarios of natural resource management. Once the model improved, the set of viewpoints made it possible to assess the impact of the individual scenarios on the main productive (sheep stocking rate, timber growth) and environmental (endangered species, landscape value) stakes on any spatial entity considered as relevant by any stakeholder. As the different opinions were collectively viewed and confronted, the need to agree to a compromise was highlighted and led to new scenarios based on more collective management of the pine woodlands. The results of these alternative scenarios were collectively evaluated anew and it was then possible to select a set of feasible scenarios stemming from current actors? perceptions and practices and to suggest alternative sylvopastoral management based on innovative practices. The paper underlines the usefulness of the representation of viewpoints in that it allowed for scenario description and impact assessment of the compared management strategies. It also shows how the step-by-step approach contributed to improve decision-making by National Park managers.

SYLVOPAST: a Multiple Target Role-Playing Game to Assess Negotiation Processes in Sylvopastoral Management Planning

Michel Etienne
Journal of Artificial Societies and Social Simulation 6 (2) 5

Kyeywords: Role-playing game, Negotiation, Sylvopastoral management, Agent-based modelling, Multi-agent system
Abstract: After a brief description of the framework of the model developed to simulate vegetation dynamics, fire propagation and agents’ behaviour, the role-playing game rules are presented and related to the different points they are supposed to deal with: climatic hazard, animal grazing, forest management, grazing duty, financial support. The results of several sets of game sessions are analysed according to markers based on the main stakeholders viewpoints, leading to an evaluation of the negotiation process and to the way land was structured as a result of a step-by-step compromise between the players. General conclusions are drawn on how such type of role-playing games can provide a methodological framework to build up negotiation support tools and can be used with different kinds of persons.

Agent-Based Approach to Investors? Behavior and Asset Price Fluctuation in Financial Markets

Hiroshi Takahashi and Takao Terano
Journal of Artificial Societies and Social Simulation 6 (3) 3

Kyeywords: Agent-Based Approach, Financial Engineering, Behavioral Finance, natural selection, Society dynamics, Self-organizing systems and emergent organization
Abstract: In this paper, we use Agent-Based Approach to analyze how asset prices are affected by investors and investment systems that are based on Behavioral Finance. We build a virtual financial market that contains two types of investors: fundamentalists and non-fundamentalists. As a result of intensive experiments in the market, we find that (1) the traded price agrees with the fundamental value and the fundamentalists survive according to the principle of natural selection in the case that the market contains the same number of fundamentalists and trend predictors (investors who make trend prediction), (2) the traded price largely deviates from the fundamental value and the non-fundamentalists frequently obtain excess returns and therefore the fundamentalists are eliminated according to the principle of natural selection in the case that the proportion of trend predictors is extremely high or in the case that the investment ratio of the risk asset is restricted, and (3) the traded price largely deviates from the fundamental value in the case that the non-fundamentalists estimate the losses excessively, as pointed in Prospect Theory. These results indicate that the non-fundamentalists affect the traded prices and obtain excess returns also in real markets.

Using Self-Designed Role-Playing Games and a Multi-Agent System to Empower a Local Decision-Making Process for Land Use Management: the SelfCormas Experiment in Senegal

Patrick D'aquino, Christophe Le Page, François Bousquet and Alassane Bah
Journal of Artificial Societies and Social Simulation 6 (3) 5

Kyeywords: Local Planning; Participatory; Land Use; Resources Management; Role Playing Games;Agent Based Modeling
Abstract: As agricultural and environmental issues are more and more inter-linked, the increasing multiplicity of stakeholders, with differing and often conflicting land use representations and strategies, underlines the need for innovative methods and tools to support their coordination, mediation and negotiation processes aiming at an improved, more decentralized and integrated natural resources management. But how can technology fit best with such a novel means of support? Even the present participatory modeling method is not really designed to avoid this technocratic drift and encourage the empowerment of stakeholders in the land use planning process. In fact, to truly integrate people and principals in the decision-making process of land use management and planning, information technology should not only support a mere access to information but also help people to participate fully in its design, process and usage. That means allow people to use the modeling support not to provide solutions, but to help people to steer their course within an incremental, iterative, and shared decision-making process. To this end, since 1997 we have experimented at an operational level (2500 km_) in the Senegal River valley a Self-Design Method that places modeling tools at stakeholders? and principals' disposal, right from the initial stages. The experiment presented here links Multi-Agent Systems and Role-Playing Games within a self-design and use process. The main objective was to test direct modeling design of these tools by stakeholders, with as little prior design work by the modeler as possible. This "self-design" experiment was organized in the form of participatory workshops which has led on discussions, appraisals, and decisions about planning land use management, already applied two years after the first workshops.

My Kingdom for a Function: Modeling Misadventures of the Innumerate

Michael Agar
Journal of Artificial Societies and Social Simulation 6 (3) 8

Kyeywords: Agent-based models, Ethnography, Epidemiology, Drug Use, Netlogo
Abstract: In this tongue-in-cheek commentary the author takes a serious look at the problem of translating ethnographic conclusions into simple functions as a means to the end of building an agent-based simulation in the Netlogo language. Specifically, the goal is to take the simple fact that stories about illicit drugs have a lot to do with whether or not they will be used and see if an agent-based model can produce an epidemic incidence curve under the appropriate conditions. This commentary has less to do with the model and more to do with figuring out what kinds of numbers make sense. Based on the principle that mathematical ignorance is bliss, the author concludes that the most important thing is that number construction reflects the differences that make a difference in the ethnographic work, where the discovery of what the significant differences in fact were was a major result of the research. Support by NIH/NIDA grant DA 10736 is gratefully acknowledged.

The Significance of Initial Conditions in Simulations

K. K. Fung and Shekhar Vemuri
Journal of Artificial Societies and Social Simulation 6 (3) 9

Kyeywords: initial conditions, model sensitivity, AgentSheets, Maruyama, second cybernetics, tissue growth, cell growth
Abstract: Although initial conditions often significantly affect simulation results, little attention has been paid to test model sensitivity to them. A visual demonstration of the significance of initial conditions using a simplified tissue-growth model may bring overdue attention to this common omission.

Adaptive Agents, Political Institutions and Civic Traditions in Modern Italy

Ravi Bhavnani
Journal of Artificial Societies and Social Simulation 6 (4) 1

Kyeywords: Social Capital, Italy, Agent-Based Model
Abstract: Long duration historical studies have been formative in shaping comparative analysis. Yet historical processes are notoriously difficult to study, and their findings equally difficult to validate empirically. In this paper, I take Robert Putnam’s work on Civic Traditions in Modern Italy and attempt to bridge the gap between the study’s historical starting point and contemporary observations, using an agent-based model of social interaction. My use of a computational model to study historical processes—in this case the inculcation and spread of social capital—supports Putnam's claim of path dependence. Moving beyond Putnam’s study, my results indicate that the formation of civic (or uncivic) communities is not deterministic, that their emergence is sensitive to historical shocks, and that the absence of political boundaries lowers aggregate levels of civicness in regions characterized by effective institutions. In addition, the simulation suggests that minor improvement to ineffective institutions—making them moderately effective—constitute a mid-level equilibrium trap with the least desirable social consequences.

Coordination Mechanisms Based on Cooperation and Competition Within Industrial Districts: an Agent-Based Computational Approach

Vito Albino, Nunzia Carbonara and Ilaria Giannoccaro
Journal of Artificial Societies and Social Simulation 6 (4) 3

Kyeywords: Industrial districts, Agent-based computational approach, Coordination mechanisms.
Abstract: In this paper we propose a computational approach based on multi-agent systems to study the multiple forms of cooperative and competitive relationships within Industrial Districts (IDs). In particular, we develop a computational model by using AgentBuilder software, which is referred to a specific kind of cooperative relationships, namely that aimed at both balancing the utilization of supplier production capacity and minimizing the customer unsatisfied demand. Then we carry out a simulation analysis to prove the benefits of the selected kind of cooperation for the IDs and to evaluate the benefits of the cooperation in different competitive scenarios and diverse ID organizational structures.

Social Attitudes: Investigations with Agent Simulations Using Webots

Ivica Mitrovic and Kerstin Dautenhahn
Journal of Artificial Societies and Social Simulation 6 (4) 4

Kyeywords: Agent simulation, Social simulation, Social attitudes, Social behaviour, Socio-political attitudes, Webots
Abstract: This article presents a multiagent simulation environment for studying agents' socio-political attitudes. It departs from a previously proposed concept of agents with socio-political attitudes, a high-level theoretical and conceptual model proposed by Petric et al (2002) that was intended for conversational agents. In contrast, our work pursues a bottom-up simulation philosophy where attitudes are grounded in sensory-motor behaviour of spatially distributed autonomous agents, modelled in Webots simulation software. The original model was extended by defining an agent's socio-political type by means of weighting the three components found in the Petric et al. (2002) model (neo-liberal, alternative and fundamentalist), thus allowing the creation of mixed socio-political types. Also, in the simulations performed, issues were modelled as agents with variable levels of importance. Moreover, we introduced inter-agent communication capable of causing changes in socio-political types. Results are presented and discussed with respect to the initial research questions. According to our experimental results the following parameters did not have any significant impact on the simulation outcomes: initial physical position and orientation of the agents, positions of the issues, the issues' dynamics, and inter-agent communication. Experiments with different initial agent types showed that agents with indeterminate socio-political types tended to change to neo-liberal, alternative or fundamentalist agents. We conclude by proposing future extensions of the model. Our work is related to a trend in the Artificial Intelligence community which is not primarily task or problem-solving oriented, but rather focuses on the study of the embodied and situated nature of social behaviour in humans.

Route Decision Behaviour in a Commuting Scenario: Simple Heuristics Adaptation and Effect of Traffic Forecast

Franziska Klügl and Ana L. C. Bazzan
Journal of Artificial Societies and Social Simulation 7 (1) 1

Kyeywords: Self-Organising System, Adaptation and Learning, Game-Theoretic Approaches, Traffic Simulation
Abstract: One challenge to researchers dealing with traffic management is to find efficient ways to model and predict traffic flow. Due to the social nature of traffic, most of the decisions are not independent. Thus, in traffic systems the inter-dependence of actions leads to a high frequency of implicit co-ordination decisions. Although there are already systems designed to assist drivers in these tasks (broadcast, Internet, etc.), such systems do not consider or even have a model of the way drivers decide. Our research goal is the study of commuting scenarios, drivers' decision-making, its influence on the system as a whole, and how simulation can be used to understand complex traffic systems. The present paper addresses two key issues: simulation of driver decision-making, and the role of a traffic forecast component. The former is realised by a naïve model for the route choice adaptation, where commuters behaviour is based on heuristics they evolve. The second issue is realised via a traffic control system which perceives drivers' decisions and returns a forecast, thus allowing drivers to decide the actual route selection. For validation, we use empirical data from real experiments and show that the heuristics drivers evolve lead to a situation similar to that obtained in the real experiments. As for the forecast scenario, our results confirm that a traffic system in which a large share of drivers reacts to the forecast will not develop into equilibrium. However, a more stable situation arises by introducing some individual tolerance to sub-optimal forecasts.

Evaluation of free Java-libraries for social-scientific agent based simulation

Robert Tobias and Carole Hofmann
Journal of Artificial Societies and Social Simulation 7 (1) 6

Kyeywords: Evaluation, Simulation Framework, Agent Based Modeling, Java, Theory Based Modeling, Data Based Modeling, Social Intervention Planning
Abstract: This paper compares four freely available programming libraries for support of social scientific agent based computer simulation: RePast, Swarm, Quicksilver, and VSEit. Our aim is evaluation to determine the simulation framework that is the best suited for theory and data based modeling of social interventions, such as information campaigns. Our first step consisted in an Internet search for programming libraries and the selection of suitable candidates for detailed evaluation on the basis of 'knock out' criteria. Next, we developed a rating system and assessed the selected simulation environments on the basis of the rating criteria. The evaluation was based on official program documentation, statements by developers and users, and the experiences and impressions of the evaluators. The evaluation results showed the RePast environment to be the clear winner. In a further step, the evaluation results were weighted according to effort/time/energy saved by social scientists by using the particular ready-made programming library as compared to doing their own programming. Once again, the weighted results show RePast to win out over the other Java based programming libraries examined.

Micro Behavioural Attitudes and Macro Technological Adaptation in Industrial Districts: an Agent-Based Prototype

Riccardo Boero, Marco Castellani and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 7 (2) 1

Kyeywords: Agent Based Computational Models, Industrial Districts, Technological Adaptation, Cognitive Information Processing
Abstract: Industrial Districts (IDs) are complex productive systems based on an evolutionary network of heterogeneous, functionally integrated and complementary firms, which are within the same market and geographical space. Setting up a prototype, able to reproduce an idealised ID, we model cognitive processes underlying the behaviour of ID firms. ID firms are bounded rationality agents, able to process information coming from technology and market environment and from their relational contexts. They are able to evaluate such information and to transform it into courses of action, routinising their choices, monitoring the environment, categorising, typifying and comparing information. But they have bounded cognitive resources: attention, time and memory. We test two different settings: the first one shows ID firms behaving according to a self-centred attitude, while the second one shows ID firms behaving according to a social centred attitude. We study how such a strong difference at micro-level can affect at macro-level the technological adaptation of IDs.

Small World Dynamics and The Process of Knowledge Diffusion: The Case of The Metropolitan Area of Greater Santiago De Chile

Piergiuseppe Morone and Richard Taylor
Journal of Artificial Societies and Social Simulation 7 (2) 5

Kyeywords: Agent-based, Chile, Inequality, Knowledge, Network, Small world
Abstract: This paper aims to understand some of the mechanisms which dominate the phenomenon of knowledge diffusion in the process that is called ‘interactive learning’. We examine how knowledge spreads in a network in which agents have ‘face-to-face’ learning interactions. We define a social network structured as a graph consisting of agents (vertices) and connections (edges) and situated on a grid which resembles the geographical characteristics of the metropolitan area of Greater Santiago de Chile. The target of this simulation is to test whether knowledge diffuses homogeneously or whether it follows some biased path generating geographical divergence between a core area and a periphery. We also investigate the efficiency of our ‘preference’ model of agent decision-making and show that this system evolves towards a small-world type network.

Simulation of The Dynamic Interactions Between Terror and Anti-Terror Organizational Structures

Stanislaw Raczynski
Journal of Artificial Societies and Social Simulation 7 (2) 8

Kyeywords: Simulation, Modeling, Terrorism, Discrete Event, Agent-Oriented, Social Simulation, Soft Systems
Abstract: A discrete-event model of the dynamics of certain social structures is presented. The structures include terrorist organizations, anti-terrorism and terrorism-supporting structures. The simulation shows the process of creating the structures and their interactions. As a result, we can see how the structure size changes and how the interactions work, and the process of destroying terrorist organization links by the anti-terrorist agents. The simulation is agent-oriented and uses the PASION simulation system.

Case-Based Reasoning, Social Dilemmas, and a New Equilibrium Concept

Luis R. Izquierdo, Nicholas M. Gotts and Gary Polhill
Journal of Artificial Societies and Social Simulation 7 (3) 1

Kyeywords: Social Dilemmas, Case-Based Reasoning, Prisoner's Dilemma, Agent-Based Simulation, Analogy, Game Theory, Aspiration Thresholds, Equilibrium
Abstract: In this paper social dilemmas are modelled as n-player games. Orthodox game theorists have been able to provide several concepts that narrow the set of expected outcomes in these models. However, in their search for a reduced set of solutions, they had to pay a very high price: they had to make disturbing assumptions such as instrumental rationality or common knowledge of rationality, which are rarely observed in any real-world situation. We propose a complementary approach, assuming that people adapt their behaviour according to their experience and look for outcomes that have proved to be satisfactory in the past. These ideas are investigated by conducting several experiments with an agent-based simulation model in which agents use a simple form of case-based reasoning. It is shown that cooperation can emerge from the interaction of selfish case-based reasoners. In determining how often cooperation occurs, aspiration thresholds, the agents' representation of the world, and their memory all play an important and interdependent role. It is also argued that case-based reasoners with high enough aspiration thresholds are not systemically exploitable, and that if agents were sophisticated enough to infer that other players are not exploitable either, they would eventually cooperate.

From Classroom Experiments to Computer Code

Arianna Dal Forno and Ugo Merlone
Journal of Artificial Societies and Social Simulation 7 (3) 2

Kyeywords: Agent Behavior, Experiments, Prisoner Dilemma, Harvesting Dilemma, Bounded Rationality
Abstract: A carefully designed experimental procedure may be an invaluable source for gathering empirical data and a key to grasping the heterogeneity of human behavior, which is of the utmost importance when modeling artificial agents. This paper proposes an alternative way of inferring models of behavior through a different use of data gathered in classroom experiments. By way of example, we report and then discuss the results and the computer code obtained from the analysis of the behavior of subjects in two classroom experiments.

VIR-POX: An Agent-Based Analysis of Smallpox Preparedness and Response Policy

Benjamin M. Eidelson and Ian Lustick
Journal of Artificial Societies and Social Simulation 7 (3) 6

Kyeywords: Smallpox, Bioterrorism, Agent-Based Modeling, Stochastic Simulation, Vaccination Policy
Abstract: Because conjectural 'thought experiments' can be formalized, refined, and conducted systematically using computers, computational modeling is called for in situations that demand robust quantitative study of phenomena which occur only rarely, or may never occur at all. In light of mounting concerns regarding the threats of bioterrorism in general and smallpox in specific, we developed a stochastic agent-based model, VIR-POX, in order to explore the viability of available containment measures as defenses against the spread of this infectious disease. We found the various vaccination and containment programs to be highly interdependent, and ascertained that these policy options vary not only in their mean effects, but also in their subordination to factors of chance or otherwise uncontrollable interference, relationships which themselves fluctuate across ranges of the counterfactual distribution. Broadly speaking, ring vaccination rivaled mass vaccination if a very substantial proportion of smallpox cases could be detected and isolated almost immediately after infection, or if residual herd immunity in the population was relatively high. Pre-attack mass vaccination and post-attack mass vaccination were equivalent in their capacities to eliminate the virus from the population within five months, but the pre-attack strategy did so with significantly fewer deaths in the process. Our results suggest that the debate between ring and mass vaccination approaches may hinge on better understanding residual herd immunity and the feasibility of early detection measures.

Responsibility for Societies of Agents

Rosaria Conte and Mario Paolucci
Journal of Artificial Societies and Social Simulation 7 (4) 3

Kyeywords: Responsibility, Agents, Cognitive Modeling, E-Governance, Organisation Theory
Abstract: This paper presents a pre-formal social cognitive model of social responsibility as implying the deliberative capacity of the bearer but not necessarily her decision to act or not. Also, responsibility is defined as an objective property of agents, which they cannot remit at their will. Two specific aspects are analysed: (a) the action of "counting upon" given agents as responsible entities, and (b) the consequent property of accountability: responsibility allows to identify the locus of accountability, that is, which agents are accountable for which events and to what extent. Agents responsible for certain events, and upon which others count, are asked to account or respond for these events. Two types of responsibility are distinguished and their commonalities pointed out: (a) a primary form of responsibility, which is a consequence of mere deliberative power, and (b) a task-based form, which is a consequence of task commitment. Primary responsibility is a relation between deliberative agents and social harms, whether these are intended and believed or not, and whether they are actually caused by the agent or not. The boundaries of responsibility will be investigated, and the conceptual links of responsibility with obligation and guilt will be examined. Task-based responsibility implies task- or role-commitment. Furthermore, individual Vs. shared Vs. collective responsibility are distinguished. Considerations about the potential benefits and utility of the analysis proposed for in the field of e-governance are highlighted. Concluding remarks and ideas for future works are discussed in the final section.

Reasoning About Other Agents: a Plea for Logic-Based Methods

Wendelin Reich
Journal of Artificial Societies and Social Simulation 7 (4) 4

Kyeywords: Formal Logic, Social Interaction, Social Simulation, Agents, Social Meta-Reasoning, Reasoning About Reasoning
Abstract: Formal logic has become an invaluable tool for research on multi-agent systems, but it plays a minor role in the more applied field of agent-based social simulation (ABSS). We argue that logical languages are particularly useful for representing social meta-reasoning, that is, agents' reasoning about the reasoning of other agents. After arguing that social meta-reasoning is a frequent and important social phenomenon, we present a set of general criteria (functional completeness, understandability, changeability, and implementability/executability) to compare logic to two alternative formal methods: black box techniques (e.g., neural networks) and decision-theoretical models (e.g., game theory). We then argue that in terms of functional completeness, understandability and changeability, logical representations of social meta-reasoning compare favorably to these two alternatives.

Formal Systems and Agent-Based Social Simulation Equals Null?

Maria Fasli
Journal of Artificial Societies and Social Simulation 7 (4) 7

Kyeywords: Formal Systems, Social Interactions, Social Agents, Commitments, Roles, Obligations
Abstract: This paper discusses some of the merits of the use of formal logic in multi-agent systems and agent-based simulation research. Reasons for the plethora of formal systems are discussed as well as how formal systems and agent-based social simulation can work together. As an example a formal system for describing social relationships and interactions in a multi-agent system is presented and how this could benefit from agent-based social simulation as well as make a contribution is discussed.

The Use of Logic in Agent-Based Social Simulation

Frank Dignum, Bruce Edmonds and Liz Sonenberg
Journal of Artificial Societies and Social Simulation 7 (4) 8

Kyeywords: Logic, Agent Technology, Formal Systems, Social Theory
Abstract: [No abstract for this editorial]

SISTER: a Symbolic Interactionist Simulation of Trade and Emergent Roles

Deborah Duong and John Grefenstette
Journal of Artificial Societies and Social Simulation 8 (1) 1

Kyeywords: Agent-Based Model, Computational Social Theory, Economics Simulation, Symbolic Interactionism, Emergent Language, Sociological Roles
Abstract: SISTER, a Symbolic Interactionist Simulation of Trade and Emergent Roles, captures a fundamental social process by which macro level roles emerge from micro level symbolic interaction. The knowledge in a SISTER society is held culturally, suspended in the mutual expectations agents have of each other based on signs (tags) that they read and display. In this study, this knowledge includes how to create composite goods. The knowledge of coordinating their creation arises endogenously. A symbol system emerges to denote these tasks. In terms of information theory, the degree of mutual information between the agent\'s signs (tags) and their behavior increases over time. The SISTER society of this study is an economic simulation, in which agents have the choice of growing all the goods they need by themselves, or concentrating their efforts in making more of fewer goods and trading them for other goods. They induce the sign of an agent to trade with, while at the same time, they induce a sign to display. The signs come to mean sets of behaviors, or roles, through this double induction. A system of roles emerges, holding the knowledge of social coordination needed to distribute tasks among the agents.

Agents in Living Color: Towards Emic Agent-Based Models

Michael Agar
Journal of Artificial Societies and Social Simulation 8 (1) 4

Kyeywords: Agent-Based Models, Ethnography, Substance Use, Emic/etic, Validity, Netlogo
Abstract: The link between agent-based models and social research is a foundational concern of this journal. In this article, the anthropological concept of 'emic' or 'insider's view' is used to foreground the value of learning what differences make a difference to actual human agents before building a model of those agents and their world. The author's Netlogo model of the epidemiology of illicit drug use provides the example case. In the end, the emic does powerfully inform and constrain the model, but etic or 'outsider' views are required as well. At the same time, the way the model motivates these etic frameworks offers a strong test of theoretical relevance and a potential avenue towards theory integration.

The Ghost in the Model (and Other Effects of Floating Point Arithmetic)

Gary Polhill, Luis R. Izquierdo and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 8 (1) 5

Kyeywords: Agent Based Modelling, Floating Point Arithmetic, Interval Arithmetic, Replication
Abstract: This paper will explore the effects of errors in floating point arithmetic in two published agent-based models: the first a model of land use change (Polhill et al. 2001; Gotts et al. 2003), the second a model of the stock market (LeBaron et al. 1999). The first example demonstrates how branching statements with floating point operands of comparison operators create a high degree of nonlinearity, leading in this case to the creation of 'ghost' agents -- visible to some parts of the program but not to others. A potential solution to this problem is proposed. The second example shows how mathematical descriptions of models in the literature are insufficient to enable exact replication of work since mathematically equivalent implementations in terms of real number arithmetic are not equivalent in terms of floating point arithmetic.

Deception and Convergence of Opinions

André C. R. Martins
Journal of Artificial Societies and Social Simulation 8 (2) 3

Kyeywords: Opinion Dynamics, Deception, Confirmation Theory, Epistemology, Rational Agents
Abstract: This article studies what happens when someone tries to decide between two com¬peting ideas simply by reading descriptions of experiments done by others. The agent is modeled as rational person, adopting Bayesian rules and the effect that the possibility that each article might be a deception is analyzed.

A Model for a Simple Luhmann Economy

Anselm Fleischmann
Journal of Artificial Societies and Social Simulation 8 (2) 4

Kyeywords: Agent-Based Modelling, Luhmann Economy, Fuzzy Clustering
Abstract: The core of this work is the definition of an agent-based model for a simple Luhmann economy based on publications of Niklas Luhmann. • Using an implementation on a default personal computer the behaviour of the model is studied when assumptions regarding initial conditions are made. Fuzzy-c-means clustering is used as visualisation aid. The impact of the observation horizon (a model parameter determining how far agents can see) is studied interactively. • Solution paths of the Luhmann economy originating from an initial endowment to equilibrium (when the economy settles down) are studied. • The impact of model parameters determining the unevenness regarding the initial distribution of wealth is studied by Monte Carlo simulation. Niklas Luhmann\'s hypothesis, that the economy starts from and produces further inequality in order to continue (see Luhmann 1988, p. 112) could be reproduced by computer simulation. The main characteristic of the approach is the consideration of the cohesive structure of communication (i.e. one communicative act - many understanding observers) also prominent in (Dunbar 1996, pp. 192-207). The model gives directions how to model further aspects of Niklas Luhmann\'s theory.

Increasing Learner Retention in a Simulated Learning Network Using Indirect Social Interaction

Rob E.J.R. Koper
Journal of Artificial Societies and Social Simulation 8 (2) 5

Kyeywords: Self-Organisation, Education, Distance Learning, Lifelong Learning, Learning Network
Abstract: A learning network is a network of persons who create, share, support and study units of learning (courses, workshops, lessons, etc.) in a specific knowledge domain. Such networks may consist of a large number of alternative units of learning. One of the problems learners face in a learning network is to select the most suitable path through the units of learning in order to build the required competence in an effective and efficient way. This study explored the use of indirect social interaction to solve this problem. Units of learning that have been completed successfully by other comparable learners are presented to the learners as navigational support. A learning network is simulated in which learners search for, enrol in and study units of learning, subject to a variety of constraints: a) variable quality of the different units of learning, b) disturbance, i.e. interference by priorities other than learning and c) matching errors that occur when the entry requirements of the selected unit of learning do not align with the pre-knowledge of the learner. Two conditions are explored in the network: the selection of units of learning with and without indirect social interaction. It was found that indirect social interaction increases the proportion of learners who attain their required competence in the simulated learning network.

The Fate of Spatial Dilemmas with Different Fuzzy Measures of Success

Hugo Fort and Nicolás Pérez
Journal of Artificial Societies and Social Simulation 8 (3) 1

Kyeywords: Complex Adaptive Agents, Cooperation, Artificial Societies, Spatial Game Theory
Abstract: Cooperation among self-interested individuals pervades nature and seems essential to explain several landmarks in the evolution of live organisms, from prebiotic chemistry through to the origins of human societies. The iterated Prisoner's Dilemma (IPD) has been widely used in different contexts, ranging from social sciences to biology, to elucidate the evolution of cooperation. In this work we approach the problem from a different angle. We consider a system of adaptive agents, in a two dimensional grid, playing the IPD governed by Pavlovian strategies. We investigate the effect of different possible measures of success (MSs) used by the players to assess their performance in the game. These MSs involve quantities such as: the utilities of a player in each round U, his cumulative score (or "capital" or \'wealth\') W, his neighbourhood "welfare" and combinations of them. The agents play sequentially with one of their neighbours and the two players update their "behaviour" (C or D) using fuzzy logic which seems more appropriate to evaluate an imprecise concept like "success" than binary logic. The steady states are characterised by different degrees of cooperation, "economic geographies" (population structure and maps of capital) and "efficiencies" which depend dramatically on the MS. In particular, some MSs produce patterns of "segregation" and "exploitation".

Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms'

José Manuel Galán and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 8 (3) 2

Kyeywords: Replication, Agent-Based Modelling, Evolutionary Game Theory, Social Dilemmas, Norms, Metanorms
Abstract: In this paper we try to replicate the simulation results reported by Axelrod (1986) in an influential paper on the evolution of social norms. Our study shows that Axelrod's results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times for many periods, exploring the parameter space adequately, complementing simulation with analytical work, and being aware of the scope of our simulation models.

Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach

Francesca Borrelli, Cristina Ponsiglione, Luca Iandoli and Giuseppe Zollo
Journal of Artificial Societies and Social Simulation 8 (3) 4

Kyeywords: Firm Networks, Collective Memory, Agent Based Models, Uncertainty
Abstract: Literature about Industrial Districts has largely emphasized the importance of both economic and social factors in determining the competitiveness of these particular firms\' clusters. For thirty years, the Industrial District productive and organizational model represented an alternative to the integrated model of fordist enterprise. Nowadays, the district model suffers from competitive gaps, largely due to the increase of competitive pressure of globalization. This work aims to analyze, through an agent-based simulation model, the influence of informal socio-cognitive coordination mechanisms on district\'s performances, in relation to different competitive scenarios. The agent-based simulation approach is particularly fit for this purpose as it is able to represent the Industrial District\'s complexity. Furthermore, it permits to develop dynamic analysis of district\'s performances according to different types of environment evolution. The results of this work question the widespread opinion that cooperative districts can answer to environmental changes more effectively that non-cooperative ones. In fact, the results of simulations show that, in the presence of turbulent scenarios, the best performer districts are those in which cooperation and competition, trust and opportunism balance out.

A Formal Model for the Fifth Discipline

Lourival Paulino da Silva
Journal of Artificial Societies and Social Simulation 8 (3) 6

Kyeywords: Multi-Agent Systems, Formal Methods, the Fifth Discipline, Organizational Modeling, Learning Organization, Organizational Learning, z
Abstract: In this paper we present the main results of our research concerning the development of a formal model for the theory called The Fifth Discipline. Our model is based on a Multi-Agent Systems framework. The contributions of this work include a formal model for the Fifth Discipline, and analyses that highlight key features of that theory, namely the pressupositions that agents must be honest, cooperative, tenacious, and that trust is fundamental in the agents' interactions.

How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges

Josep M. Pujol, Andreas Flache, Jordi Delgado and Ramon Sangüesa
Journal of Artificial Societies and Social Simulation 8 (4) 12

Kyeywords: Complex Networks, Power-Law, Scale-Free, Small-World, Agent-Based Modeling, Social Exchange Theory, Structural Emergence
Abstract: Small-world and power-law network structures have been prominently proposed as models of large networks. However, the assumptions of these models usually lack sociological grounding. We present a computational model grounded in social exchange theory. Agents search attractive exchange partners in a diverse population. Agent use simple decision heuristics, based on imperfect, local information. Computer simulations show that the topological structure of the emergent social network depends heavily upon two sets of conditions, harshness of the exchange game and learning capacities of the agents. Further analysis show that a combination of these conditions affects whether star-like, small-world or power-law structures emerge.

Towards Good Social Science

Scott Moss and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 8 (4) 13

Kyeywords: Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application; History of Science
Abstract: The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are taken from recent issues of leading social science journals. We argue that agent based social simulations can be used as a tool to constrain the development of a new social science by direct (what economists dismiss as anecdotal) evidence and that to do so would make social science relevant to the understanding and influencing of social processes. We argue that such a development is both possible and desirable. We do not argue that it is likely.

The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs

Nuno David, Jaime Simão Sichman and Helder Coelho
Journal of Artificial Societies and Social Simulation 8 (4) 2

Kyeywords: Computer and Social Sciences, Agent-Based Simulation, Intentional Computation, Program Verification, Intentional Verification, Scientific Knowledge
Abstract: The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences.

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

Riccardo Boero and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 8 (4) 6

Kyeywords: Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models
Abstract: The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.

It Pays to Be Popular: a Study of Civilian Assistance and Guerilla Warfare

Scott Wheeler
Journal of Artificial Societies and Social Simulation 8 (4) 9

Kyeywords: Peacekeeping, Insurgency, Agent-Based
Abstract: This paper presents a study into the benefits imparted by friendly civilian populaces in assisting peacekeepers to conduct operations under the threat of guerrilla warfare. In this study, civilians report observed insurgent activity to peacekeepers with varying levels of enthusiasm depending on the reputation of the peacekeepers with the local populace. A simulation model is developed using an agent-based approach and a statistically significant number of Monte Carlo simulations conducted to measure the success of the peacekeeping operations and the benefits of civilian assistance.

The Viability of Cooperation Based on Interpersonal Commitment

István Back and Andreas Flache
Journal of Artificial Societies and Social Simulation 9 (1) 12

Kyeywords: Interpersonal Commitment, Fairness, Reciprocity, Agent-Based Simulation, Help Exchange, Evolution
Abstract: A prominent explanation of cooperation in repeated exchange is reciprocity (e.g. Axelrod, 1984). However, empirical studies indicate that exchange partners are often much less intent on keeping the books balanced than Axelrod suggested. In particular, there is evidence for commitment behavior, indicating that people tend to build long-term cooperative relationships characterised by largely unconditional cooperation, and are inclined to hold on to them even when this appears to contradict self-interest. Using an agent-based computational model, we examine whether in a competitive environment commitment can be a more successful strategy than reciprocity. We move beyond previous computational models by proposing a method that allows to systematically explore an infinite space of possible exchange strategies. We use this method to carry out two sets of simulation experiments designed to assess the viability of commitment against a large set of potential competitors. In the first experiment, we find that although unconditional cooperation makes strategies vulnerable to exploitation, a strategy of commitment benefits more from being more unconditionally cooperative. The second experiment shows that tolerance improves the performance of reciprocity strategies but does not make them more successful than commitment. To explicate the underlying mechanism, we also study the spontaneous formation of exchange network structures in the simulated populations. It turns out that commitment strategies benefit from efficient networking: they spontaneously create a structure of exchange relations that ensures efficient division of labor. The problem with stricter reciprocity strategies is that they tend to spread interaction requests randomly across the population, to keep relations in balance. During times of great scarcity of exchange partners this structure is inefficient because it generates overlapping personal networks so that often too many people try to interact with the same partner at the same time.

Deception and Convergence of Opinions Part 2: the Effects of Reproducibility

Victor Palmer
Journal of Artificial Societies and Social Simulation 9 (1) 14

Kyeywords: Opinion Dynamics, Epistemology, Rational Agents, Deception, Confirmation Theory
Abstract: Recently Martins (Martins 2005) published an article in this journal analyzing the opinion dynamics of a neutral observer deciding between two competing scientific theories (Theory A and Theory B). The observer could not perform any experiments to verify either theory, but instead had to form its opinion solely by reading published articles reporting the experimental results of others. The observer was assumed to be rational (modeled with simple Bayesian rules) and the article examined how the observer\'s confidence in the correctness of the two theories changed as a function of number of articles read in support of each theory, and how much, if any, deception was believed to be present in the published articles. A key (and somewhat disturbing) result of this work was that for even relatively small amounts of perceived deception in the source articles, the observer could never be reasonably sure of which theory (A or B) was correct, even in the limit of the observer reading an infinite number of such articles. In this work we make a small extension to the Martins article by examining what happens when the observer only considers experimental results which have been reproduced by multiple parties. We find that even if the observer only requires that the articles he or she reads be verified by one additional party, its confidence in one of the two theories can converge to unity, regardless of the amount of amount of deception believed to be present in the source articles.

A Common Protocol for Agent-Based Social Simulation

Matteo Richiardi, Roberto Leombruni, Nicole J. Saam and Michele Sonnessa
Journal of Artificial Societies and Social Simulation 9 (1) 15

Kyeywords: Agent-Based, Simulations, Methodology, Calibration, Validation, Sensitivity Analysis
Abstract: Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.

Introduction to the Special Section on Reputation in Agent Societies

Mario Paolucci and Jordi Sabater-Mir
Journal of Artificial Societies and Social Simulation 9 (1) 16

Kyeywords: Reputation, Agent Systems
Abstract: This special section includes papers from the 'Reputation in Agent Societies' workshop held as part of 2004 IEEE/WIC/ACM International Joint Conference on Intelligent Agent Technology (IAT'04) and Web Intelligence (WI'04), September 20, 2004 in Beijing, China. The purpose of this workshop was to promote multidisciplinary collaboration for Reputation Systems modeling and implementation. Reputation is increasingly at the centre of attention in many fields of science and domains of application, including economics, organisations science, policy-making, (e-)governance, cultural evolution, social dilemmas, socio-dynamics, innofusion, etc. However, the result of all this attention is a great number of ad hoc models and little integration of instruments for the implementation, management and optimisation of reputation. On the one hand, entrepreneurs and administrators manage corporate and firm reputation without contributing to or accessing a solid, general and integrated body of scientific knowledge on the subject matter. On the other hand, software designers believe they can design and implement online reputation reporting systems without investigating what the properties, requirements and dynamics of reputation in natural societies are and why it evolved. We promoted the workshop and this special section with the hope of setting the first steps in the direction of a new, cross-disciplinary approach to reputation, accounting for the social cognitive mechanisms and processes that support it and working towards t a consensus on essential guidelines for designing or shaping reputation technologies.

An Agent-Based Spatially Explicit Epidemiological Model in MASON

Jill Bigley Dunham
Journal of Artificial Societies and Social Simulation 9 (1) 3

Kyeywords: Epidemiology, Social Networks, Agent-Based Simulation, MASON Toolkit
Abstract: This paper outlines the design and implementation of an agent-based epidemiological simulation system. The system was implemented in the MASON toolkit, a set of Java-based agent-simulation libraries. This epidemiological simulation system is robust and extensible for multiple applications, including classroom demonstrations of many types of epidemics and detailed numerical experimentation on a particular disease. The application has been made available as an applet on the MASON web site, and as source code on the author\'s web site.

Simulating the Emergence of Task Rotation

Kees Zoethout, Wander Jager and Eric Molleman
Journal of Artificial Societies and Social Simulation 9 (1) 5

Kyeywords: Organisation, Task Rotation, Work Groups, Psychological Theory, Multi Agent Simulation
Abstract: In work groups, task rotation may decrease the negative consequences of boredom and lead to a better task performance. In this paper we use multi agent simulation to study several organisation types in which task rotation may or may not emerge. By looking at the development of expertise and motivation of the different agents and their performance as a function of self-organisation, boredom, and task rotation frequency, we describe the dynamics of task rotation. The results show that systems in which task rotation emerges perform better than systems in which the agents merely specialise in one skill. Furthermore, we found that under certain circumstances, a task that leads to a high degree of boredom was performed better than a task causing a low level of boredom.

The AtollGame Experience: from Knowledge Engineering to a Computer-Assisted Role Playing Game

Anne Dray, Pascal Perez, Natalie Jones, Christophe Le Page, Patrick D'aquino, Ian White and Titeem Auatabu
Journal of Artificial Societies and Social Simulation 9 (1) 6

Kyeywords: Knowledge Elicitation, Associative Network, Ontology, Water Management, Pacific, Tarawa
Abstract: This paper presents the methodology developed to collect, understand and merge viewpoints coming from different stakeholders in order to build a shared and formal representation of the studied system dealing with groundwater management in the low-lying atoll of Tarawa (Republic of Kiribati). The methodology relies on three successive stages. First, a Global Targeted Appraisal focuses on social group leaders in order to collect different standpoints and their articulated mental models. These collective models are partly validated through Individual Activities Surveys focusing on behavioural patterns of individual islanders. Then, these models are merged into a single conceptual one using qualitative analysis software. This conceptual model is further simplified in order to create a computer-assisted role-playing game.

Uncertainty and Cooperation: Analytical Results and a Simulated Agent Society

Peter Andras, John Lazarus, Gilbert Roberts and Steven J Lynden
Journal of Artificial Societies and Social Simulation 9 (1) 7

Kyeywords: Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty
Abstract: Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.

Consensus Strikes Back in the Hegselmann-Krause Model of Continuous Opinion Dynamics Under Bounded Confidence

Jan Lorenz
Journal of Artificial Societies and Social Simulation 9 (1) 8

Kyeywords: Continuous Opinion Dynamics, Bounded Confidence, Interactive Markov Chain, Bifurcation, Number of Agents, Onesided Dynamics
Abstract: The agent-based bounded confidence model of opinion dynamics of Hegselmann and Krause (2002) is reformulated as an interactive Markov chain. This abstracts from individual agents to a population model which gives a good view on the underlying attractive states of continuous opinion dynamics. We mutually analyse the agent-based model and the interactive Markov chain with a focus on the number of agents and onesided dynamics. Finally, we compute animated bifurcation diagrams that give an overview about the dynamical behavior. They show an interesting phenomenon when we lower the bound of confidence: After the first bifurcation from consensus to polarisation consensus strikes back for a while.

Multi-Agent Simulation of Emergence of Schwa Deletion Pattern in Hindi

Monojit Choudhury, Anupam Basu and Sudeshna Sarkar
Journal of Artificial Societies and Social Simulation 9 (2) 2

Kyeywords: Language Change, Linguistic Agent, Language Game, Multi-Agent Simulation, Schwa Deletion
Abstract: Recently, there has been a revival of interest in multi-agent simulation techniques for exploring the nature of language change. However, a lack of appropriate validation of simulation experiments against real language data often calls into question the general applicability of these methods in modeling realistic language change. We try to address this issue here by making an attempt to model the phenomenon of schwa deletion in Hindi through a multi-agent simulation framework. The pattern of Hindi schwa deletion and its diachronic nature are well studied, not only out of general linguistic inquiry, but also to facilitate Hindi grapheme-to-phoneme conversion, which is a preprocessing step to text-to-speech synthesis. We show that under certain conditions, the schwa deletion pattern observed in modern Hindi emerges in the system from an initial state of no deletion. The simulation framework described in this work can be extended to model other phonological changes as well.

Repage: REPutation and ImAGE Among Limited Autonomous Partners

Jordi Sabater-Mir, Mario Paolucci and Rosaria Conte
Journal of Artificial Societies and Social Simulation 9 (2) 3

Kyeywords: Reputation, Agent Systems, Cognitive Design, Fuzzy Evaluation
Abstract: This paper introduces Repage, a computational system that adopts a cognitive theory of reputation. We propose a fundamental difference between image and reputation, which suggests a way out from the paradox of sociality, i.e. the trade-off between agents' autonomy and their need to adapt to social environment. On one hand, agents are autonomous if they select partners based on their social evaluations (images). On the other, they need to update evaluations by taking into account others'. Hence, social evaluations must circulate and be represented as "reported evaluations" (reputation), before and in order for agents to decide whether to accept them or not. To represent this level of cognitive detail in artificial agents' design, there is a need for a specialised subsystem, which we are in the course of developing for the public domain. In the paper, after a short presentation of the cognitive theory of reputation and its motivations, we describe the implementation of Repage.

Formal Interpretation of a Multi-Agent Society As a Single Agent

Tibor Bosse and Jan Treur
Journal of Artificial Societies and Social Simulation 9 (2) 6

Kyeywords: Collective Intelligence, Simulation, Logical Formalisation, Single Vs. Multi-Agent Behaviour
Abstract: In this paper the question is addressed to what extent the collective processes in a multi-agent society can be interpreted as single agent processes. This question is answered by formal analysis and simulation. It is shown for an example process how it can be conceptualised, formalised and simulated in two different manners: from a single agent (or cognitive) and from a multi-agent (or social) perspective. Moreover, it is shown how an ontological mapping can be formally defined between the two formalisations, and how this mapping can be extended to a mapping of dynamic properties. Thus it is shown how collective behaviour can be interpreted in a formal manner as single agent behaviour.

An Agent-Based Model of Mortality Shocks, Intergenerational Effects, and Urban Crime

Michael Makowsky
Journal of Artificial Societies and Social Simulation 9 (2) 7

Kyeywords: Agent-Based Model, Crime, Bounded Rationality, Life Expectancy, Rational Choice
Abstract: Rational criminals choose crime over lawfulness because it pays better; hence poverty correlates to criminal behavior. This correlation is an insufficient historical explanation. An agent-based model of urban crime, mortality, and exogenous population shocks supplements the standard economic story, closing the gap with an empirical reality that often breaks from trend. Agent decision making within the model is built around a career maximization function, with life expectancy as the key independent variable. Rational choice takes the form of a local information heuristic, resulting in subjectively rational suboptimal decision making. The effects of population shocks are explored using the Crime and Mortality Simulation (CAMSIM), with effects demonstrated to persist across generations. Past social trauma are found to lead to higher crime rates which subsequently decline as the effect degrades, though \'aftershocks\' are often experienced.

Emerging Artificial Societies Through Learning

Nigel Gilbert, Matthijs den Besten, Akos Bontovics, Bart G.W. Craenen, Federico Divina, A.E. Eiben, Robert Griffioen, György Hévízi, Andras Lõrincz, Ben Paechter, Stephan Schuster, Martijn C. Schut, Christian Tzolov, Paul Vogt and Lu Yang
Journal of Artificial Societies and Social Simulation 9 (2) 9

Kyeywords: Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning
Abstract: The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.

Votes and Lobbying in the European Decision-Making Process: Application to the European Regulation on GMO Release

Juliette Rouchier and Sophie Thoyer
Journal of Artificial Societies and Social Simulation 9 (3) 1

Kyeywords: Lobbying, Europe, GMO, Multi-Agent Simulation, Public Choice, Politician, Voter, Group Contest
Abstract: The paper presents a multi-agent model simulating a two-level public decision game in which politicians, voters and interest groups interact. The objective is to model the political market for influence at the domestic level and at the international level, and to assess how new consultation procedures affect the final decision. It is based on public choice theory as well as on political science findings. We consider in this paper that lobbying groups have different strategies for influencing voters and decision-makers, with long-term and short-term effects. Our computational model enables us to represent the situation as an iterative process, in which past decisions have an impact on the preferences and choices of agents in the following period. In the paper, the model is applied to the European decision-making procedure for authorizing the placing on the market of Genetically Modified Organisms (GMO). It illustrates the political links between public opinions, lobbying groups and elected representatives at the national scale in the 15 country members, and at the European scale. It compares the procedure which was defined by the European 1990/220 Directive in 1990 with the new procedure, the 2001/18 Directive, which replaced it in 2001. The objective is to explore the impact of the new decision rules and the reinforced public participation procedures planned by the 2001/18 Directive on the lobbying efficiency of NGOs and biotechnology firms, and on the overall acceptability of the European decision concerning the release of new GMOs on the European territory.

Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets

Alison Heppenstall, Andrew Evans and Mark Birkin
Journal of Artificial Societies and Social Simulation 9 (3) 2

Kyeywords: Agents, Spatial Interaction Model, Retail Markets, Networks
Abstract: One emerging area of agent-based modelling is retail markets; however, there are problems with modelling such systems. The vast size of such markets makes individual-level modelling, for example of customers, difficult and this is particularly true where the markets are spatially complex. There is an emerging recognition that the power of agent-based systems is enhanced when integrated with other AI-based and conventional approaches. The resulting hybrid models are powerful tools that combine the flexibility of the agent-based methodology with the strengths of more traditional modelling. Such combinations allow us to consider agent-based modelling of such large-scale and complex retail markets. In particular, this paper examines the application of a hybrid agent-based model to a retail petrol market. An agent model was constructed and experiments were conducted to determine whether the trends and patterns of the retail petrol market could be replicated. Consumer behaviour was incorporated by the inclusion of a spatial interaction (SI) model and a network component. The model is shown to reproduce the spatial patterns seen in the real market, as well as well known behaviours of the market such as the "rocket and feathers" effect. In addition the model was successful at predicting the long term profitability of individual retailers. The results show that agent-based modelling has the ability to improve on existing approaches to modelling retail markets.

Simulation of the Categorization-Elaboration Model of Diversity and Work-Group Performance

Victor Palmer
Journal of Artificial Societies and Social Simulation 9 (3) 3

Kyeywords: Workgroup Performance, Diversity, Categorization-Elaboration Model, Multi-Agent System, Market Forces
Abstract: The relationship between the diversity of work-groups and their performance continues to be a key concern in the study of organizational behavior. Several models have been proposed to explain this relationship, generally concentrating on the interplay between two main factors: diversity as a source of varied knowledge and viewpoints that a group can draw upon to increase its performance, and diversity as a source of dissention in groups, causing group fracturing and bias, leading to decreases in performance. Recently a model called the categorization-elaboration model (CEM) (van Knippenburg, et. al. 2004) was proposed which integrates existing research in diversity and group performance into a unified framework. We perform an agent-based simulation of the CEM where groups are modeled as coalitions of rational agents which draw from distinct experience pools and which collectively try and solve a simple forecasting problem. We simulate how the performance of the coalition varies with the diversity of the agents\' background experiences, and find that the resulting performance/diversity relationship is curvilinear in nature (specifically, inversely u-shaped), as predicted anecdotally in the van Knippenburg work. Additionally, we find a point of unstable equilibrium in the performance/diversity curve at the no-diversity point, such that at the no-diversity point, small increases in diversity have little or no effect on performance. We point out a connection between the existence of this feature, which would seem to highlight the importance of external diversity-encouraging efforts such as affirmative action-type initiatives and early economic work which suggests that market-based forces should be sufficient to ensure high levels of diversity in organizations.

Spatial Behavior in Groups: an Agent-Based Approach

Francesc S. Beltran, Laura Salas and Vicenç Quera
Journal of Artificial Societies and Social Simulation 9 (3) 5

Kyeywords: Spatial Behavior, Proxemics, Agent-Based Modeling, Minimum Dissatisfaction Model, Small Groups, Social Interaction
Abstract: We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent\'s personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macro-level behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents\' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

A Generic Approach to an Object-Oriented Learning Classifier System Library

Matthias Meyer and Klaus Hufschlag
Journal of Artificial Societies and Social Simulation 9 (3) 9

Kyeywords: Learning Classifier System, Modularisation, Experimentation with Assumptions, JAVA
Abstract: Learning Classifier Systems (LCS) have gained popularity in the realm of social science simulation. However, when it comes to actually constructing a LCS for a particular modelling purpose, it seems that every researcher must currently "reinvent the wheel". Taking this situation as a starting point, the objective of this paper is to present the basic ideas behind a LCS library, which can relieve simulation researchers of some of the technical work and can provide a generic structure for modelling LCS. The library is based on a strictly object-oriented approach. This provides flexibility in the process of constructing a LCS for a specific modelling purpose and encourages experimentation with various different assumptions. The paper is supported with examples based on experience in using the library.

Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

Fu-ren Lin and Shyh-ming Lin
Journal of Artificial Societies and Social Simulation 9 (4) 1

Kyeywords: Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery
Abstract: As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.

Cultural Differences and Economic Incentives: an Agent-Based Study of Their Impact on the Emergence of Regional Autonomy Movements

Dan Miodownik
Journal of Artificial Societies and Social Simulation 9 (4) 2

Kyeywords: Autonomy Movements, Ethno-Regional Mobilization, Constructivism, Agent-Based Modeling, Collective Identity
Abstract: Explanations of the emergence of regional autonomy movements - political organizations seeking to express sub-state affinities and interests - often highlight cultural differences and economic incentives as important reasons driving regional elites and local politicians to form such organization and explain the support regional autonomy movements receive. In this paper I employ a specialized agent-based computer simulation as a laboratory for 'thought experiments' to evaluate alternative theoretical expectations of the independent and combined consequences of regional economic and cultural circumstances on the likelihood of regional mobilization. The simulations suggest that pronounced cultural differences and strong economic incentives contribute to the emergence of three independent yet related aspects of autonomy mobilization: the emergence of political boundaries, minority support, and minority clustering. Furthermore, these experiment indicate that the impact of cultural differences on the emergence of political boundaries may be contingent on the strength of the economic incentives, and visa versa.

Self-Organizing Traffic at a Malfunctioning Intersection

Sujai Kumar and Sugata Mitra
Journal of Artificial Societies and Social Simulation 9 (4) 3

Kyeywords: Self-Organizing Systems, Complex Systems, Traffic, Emergent Behaviour, Agent-Based Modelling, Rule-Breaking
Abstract: Traffic signals and traffic flow models have been studied extensively in the past and have provided valuable insights on the design of signalling systems, congestion control, and punitive policies. This paper takes a slightly different tack and describes what happens at an intersection where the traffic signals are malfunctioning and stuck in some configuration. By modelling individual vehicles as agents, we were able to replicate the surprisingly organized traffic flow that we observed at a real malfunctioning intersection in urban India. Counter-intuitively, the very lawlessness that normally causes jams was causing traffic to flow smoothly at this intersection. We situate this research in the context of other research on emergent complex phenomena in traffic, and suggest further lines of research that could benefit from the analysis and modelling of rule-breaking behaviour.

Is Your Model Susceptible to Floating-Point Errors?

Luis R. Izquierdo and Gary Polhill
Journal of Artificial Societies and Social Simulation 9 (4) 4

Kyeywords: Floating Point Arithmetic, Floating Point Errors, Agent Based Modelling, Computer Modelling, Replication
Abstract: This paper provides a framework that highlights the features of computer models that make them especially vulnerable to floating-point errors, and suggests ways in which the impact of such errors can be mitigated. We focus on small floating-point errors because these are most likely to occur, whilst still potentially having a major influence on the outcome of the model. The significance of small floating-point errors in computer models can often be reduced by applying a range of different techniques to different parts of the code. Which technique is most appropriate depends on the specifics of the particular numerical situation under investigation. We illustrate the framework by applying it to six example agent-based models in the literature.

Agent-Based Participatory Simulations: Merging Multi-Agent Systems and Role-Playing Games

Paul Guyot and Shinichi Honiden
Journal of Artificial Societies and Social Simulation 9 (4) 8

Kyeywords: Agent-Based Participatory Simulations, Multi-Agent Systems, Role-Playing Games, Validation, Negotiation Support Tool
Abstract: In 2001, Olivier Barreteau proposed to jointly use multi-agent systems and role-playing games for purposes of research, training and negotiation support in the field of renewable resource management. This joint use was later labeled the "MAS/RPG methodology" and this approach is one of the foundation stones of the ComMod movement. In this article, we present an alternative method called "agent-based participatory simulations". These simulations are multi-agent systems where human participants control some of the agents. The experiments we conducted prove that it is possible to successfully merge multi-agent systems and role-playing games. We argue that agent-based participatory simulations are also a significant improvement over the MAS/RPG approach, opening new perspectives and solving some of the problems generated by the joint use of role-playing games and multi-agent systems. The advantages are at least threefold. Because all interactions are computer mediated, they can be recorded and this record can be processed and used to improve the understanding of participants and organizers alike. Because of the merge, agent-based participatory simulations decrease the distance between the agent-based model and the behavior of participants. Agent-based participatory simulations allow for computer-based improvements such as the introduction of eliciting assistant agents with learning capabilities.

Cascades of Failure and Extinction in Evolving Complex Systems

Paul Ormerod and Rich Colbaugh
Journal of Artificial Societies and Social Simulation 9 (4) 9

Kyeywords: Agent-Based Model; Connectivity; Complex Systems; Networks
Abstract: There is empirical evidence from a range of disciplines that as the connectivity of a network increases, we observe an increase in the average fitness of the system. But at the same time, there is an increase in the proportion of failure/extinction events which are extremely large. The probability of observing an extreme event remains very low, but it is markedly higher than in the system with lower degrees of connectivity. We are therefore concerned with systems whose properties are not static but which evolve dynamically over time. The focus in this paper, motivated by the empirical examples, is on networks in which the robustness or fragility of the vertices is not given, but which themselves evolve over time We give examples from complex systems such as outages in the US power grid, the robustness properties of cell biology networks, and trade links and the propagation of both currency crises and disease. We consider systems which are populated by agents which are heterogeneous in terms of their fitness for survival. The agents are connected on a network, which evolves over time. In each period agents take self-interested decisions to increase their fitness for survival to form alliances which increase the connectivity of the network. The network is subjected to external negative shocks both with respect to the size of the shock and the spatial impact of the shock. We examine the size/frequency distribution of extinctions and how this distribution evolves as the connectivity of the network grows. The results are robust with respect to the choice of statistical distribution of the shocks. The model is deliberately kept as parsimonious and simple as possible, and refrains from incorporating features such as increasing returns and externalities arising from preferential attachment which might bias the model in the direction of having the empirically observed features of many real world networks. The model still generates results consistent with the empirical evidence: increasing the number of connections causes an increase in the average fitness of agents, yet at the same time makes the system as whole more vulnerable to catastrophic failure/extinction events on an near-global scale.

Socionics: Sociological Concepts for Social Systems of Artificial (and Human) Agents

Thomas Malsch and Ingo Schulz-Schaeffer
Journal of Artificial Societies and Social Simulation 10 (1) 11

Kyeywords: Socionics, Sociology, Multi-Agent Systems, Artificial Social Systems, Hybrid Systems, Social Simulation
Abstract: Socionics is an interdisciplinary approach with the objective to use sociological knowledge about the structures, mechanisms and processes of social interaction and social communication as a source of inspiration for the development of multi-agent systems, both for the purposes of engineering applications and of social theory construction and social simulation. The approach has been spelled out from 1998 on within the Socionics priority program funded by the German National research foundation. This special issue of the JASSS presents research results from five interdisciplinary projects of the Socionics program. The introduction gives an overview over the basic ideas of the Socionics approach and summarizes the work of these projects.

Social Reputation: a Mechanism for Flexible Self-Regulation of Multiagent Systems

Christian Hahn, Bettina Fley, Michael Florian, Daniela Spresny and Klaus Fischer
Journal of Artificial Societies and Social Simulation 10 (1) 2

Kyeywords: Reputation; Institution; Electronic Market; Self-Regulation; Multiagent System
Abstract: In this paper, we use multiagent technology for social simulation of sociological micro-macro issues in the domain of electronic marketplaces. We argue that allowing self-interested agents to enable social reputation as a mechanism for flexible self-regulation during runtime can improve the robustness and \'social order\' of multiagent systems to cope with various perturbations that arise when simulating open markets (e.g. dynamic modifications of task profiles, scaling of agent populations, agent drop-outs, deviant behaviour). Referring to the sociological theory of Pierre Bourdieu, we provide a multi-level concept of reputation that consists of three different types (image, social esteem, and prestige) and considers reputation as a kind of \'symbolic capital\'. Reputation is regarded to be objectified as an observable property and to be incorporated into the agents\' mental structures through social practices of communication on different aggregation levels of sociality. We present and analyse selected results of our social simulations and discuss the importance of reputation with regard to the robustness of multiagent simulations of electronic markets.

Socionic Multi-Agent Systems Based on Reflexive Petri Nets and Theories of Social Self-Organisation

Michael Köhler, Roman Langer, Rolf von Lüde, Daniel Moldt, Heiko Rölke and Rüdiger Valk
Journal of Artificial Societies and Social Simulation 10 (1) 3

Kyeywords: Multi-Agents Systems, Petri Nets, Self-Organisation, Social Theories
Abstract: This contribution summarises the core results of the transdisciplinary ASKO project, part of the German DFG's programme Sozionik, which combines sociologists' and computer scientists' skills in order to create improved theories and models of artificial societies. Our research group has (a) formulated a social theory, which is able to explain fundamental mechanisms of self-organisation in both natural and artificial societies, (b) modelled this in a mathematical way using a visual formalism, and (c) developed a novel multi-agent system architecture which is conceptually coherent, recursively structured (hence non-eclectic) and based on our social theory. The article presents an outline of both a sociological middle-range theory of social self-organisation in educational institutions, its formal, Petri net based model, including a simulation of one of its main mechanisms, and the multi-agent system architecture SONAR. It describes how the theory was created by a re-analysis of some grand social theories, by grounding it empirically, and finally how the theory was evaluated by modelling its concepts and statements.

Construction and Evaluation of Social Agents in Hybrid Settings: Approach and Experimental Results of the INKA Project

Martin Meister, Kay Schröter, Diemo Urbig, Eric Lettkemann, Hans-Dieter Burkhard and Werner Rammert
Journal of Artificial Societies and Social Simulation 10 (1) 4

Kyeywords: Socionics, Negotiation, Roles, Mixed Human-Agent Systems
Abstract: We present an integrated approach to the modelling, implementation and examination of social agents as consecutive steps in an interdisciplinary research process. The multi-agent system developed is inspired by sociological role concepts to provide agents with the capability to negotiate on the basis of social expectations. The overall goal of our system is to allow direct interaction between agents and humans. In order to examine these hybrid constellations we developed an experimental approach, termed \'Interactivity Experiment\'. An initial experiment showed that human settings, agent settings and mixed settings produce very different results, and that heterogeneous settings are superior to homogeneous settings.

The Empirical Semantics Approach to Communication Structure Learning and Usage: Individualistic Vs. Systemic Views

Matthias Nickles, Michael Rovatsos, Marco Schmitt, Wilfried Brauer, Felix Fischer, Thomas Malsch, Kai Paetow and Gerhard Weiss
Journal of Artificial Societies and Social Simulation 10 (1) 5

Kyeywords: Agent Communication, Open Multiagent Systems, Social Systems Theory, Symbolic Interactionism, Pragmatism, Computational Pragmatics
Abstract: In open systems of artificial agents, the meaning of communication in part emerges from ongoing interaction processes. In this paper, we present the empirical semantics approach to inductive derivation of communication semantics that can be used to derive this emergent semantics of communication from observations. The approach comes in two complementary variants: One uses social systems theory, focusing on system expectation structures and global utility maximisation, and the other is based on symbolic interactionism, focusing on the viewpoint and utility maximisation of the individual agent. Both these frameworks make use of the insight that the most general meaning of agent utterances lies in their expectable consequences in terms of observable events, and thus they strongly demarcate themselves from traditional approaches to the semantics and pragmatics of agent communication languages.

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

Gary Polhill, Edoardo Pignotti, Nicholas M. Gotts, Pete Edwards and Alun Preece
Journal of Artificial Societies and Social Simulation 10 (2) 2

Kyeywords: Agent-Based Social Simulation, Experiments, Ontologies, Replication, Semantic Grid
Abstract: Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.

CARDS: Case-Based Reasoning Decision Support Mechanism for Multi-Agent Negotiation in Mobile Commerce

Kun Chang Lee and Namho Lee
Journal of Artificial Societies and Social Simulation 10 (2) 4

Kyeywords: Mobile Commerce, Case-Based Reasoning, Multi-Agents, Negotiation
Abstract: Recent advent of mobile commerce or m-commerce suggests a need to incorporate intelligent techniques capable of providing decision support consistent with past instances as well as coordination support for conflicting goals and preferences among mobile users. Since m-commerce allows users to move around while doing business transactions, it seems imperative for the m-commerce users to be given high quality of decision support which should be timely and consistent with past instances. For this purpose, this paper presents two schemes – (1) both buyers and sellers engaged in m-commerce are represented by B-agents and S-agents so that the multi-agent framework can be applied, and (2) a case-based reasoning decision support (CARDS) mechanism is developed to provide a robust and consistent support for negotiation among the multi-agents. The primary mission of CARDS here is to match buyers and sellers all of whom want to maximize their own utilities. A real example of m-commerce was chosen to verify the validity of the proposed CARDS, in which perishable products should be sold to those buyers on time. Experiments were performed on the Netlogo, a multi-agent simulation platform running on Windows XP. Statistical tests were also conducted to see whether the experimental results are statistically valid.

Peer-Allocated Instant Response (PAIR): Computational Allocation of Peer Tutors in Learning Communities

Wim Westera
Journal of Artificial Societies and Social Simulation 10 (2) 5

Kyeywords: Distance Learning, Computational Simulations, System Dynamics, Education and Application, Peer Support, Peer Allocation
Abstract: This paper proposes a computational model for the allocation of fleeting peer tutors in a community of learners: a student\'s call for support is evaluated by the model in order to allocate the most appropriate peer tutor. Various authors have suggested peer tutoring as a favourable approach for confining the ever-growing workloads of teachers and tutors in online learning environments. The model\'s starting point is to serve two conflicting requirements: 1) the allocated peers should have sufficient knowledge to guarantee high quality support and 2) tutoring workload of peers should be fairly distributed over the student population. While the first criterion is likely to saddle a small number of very bright students with all the tutoring workload, the unconditional pursuit of a uniform workload distribution over the students is likely to allocate incompetent tutors. In both cases the peer support mechanism is doomed to failure. The paper identifies relevant variables and elaborates an allocation procedure that combines various filter types. The functioning of the allocation procedure is tested through a computer simulation program that has been developed to represent the student population, the students curriculum and the dynamics of tutor allocation. The current study demonstrates the feasibility of the self-allocating peer tutoring mechanism. The proposed model is sufficiently stable within a wide range of conditions. By introducing an overload tolerance parameter which stretches the fair workload distribution criteria, substantial improvements of the allocation success rate are effected. It is demonstrated that the allocation algorithm works best at large population sizes. The results show that the type of curriculum (collective route or individualised routes) has only little influence on the allocation mechanism.

Higher-Order Simulations: Strategic Investment Under Model-Induced Price Patterns

Gilbert Peffer and Barbara Llacay
Journal of Artificial Societies and Social Simulation 10 (2) 6

Kyeywords: Financial Markets, Multi-Agent Simulation, Performativity, Higher-Order Strategies
Abstract: The trading and investment decision processes in financial markets become ever more dependent on the use of valuation and risk models. In the case of risk management for instance, modelling practice has become quite homogeneous and the question arises as to the effect this has on the price formation process. Furthermore, sophisticated investors who have private information about the use and characteristics of these models might be able to make superior gains in such an environment. The aim of this article is to test this hypothesis in a stylised market, where a strategic investor trades on information about the valuation and risk management models used by other market participants. Simulation results show that under certain market conditions, such a \'higher-order\' strategy generates higher profits than standard fundamental and momentum strategies that do not draw on information about model use.

Social Simulation of Stock Markets: Taking It to the Next Level

Arvid Oskar Ivar Hoffmann, Wander Jager and J. H. Von Eije
Journal of Artificial Societies and Social Simulation 10 (2) 7

Kyeywords: Agent-Based Computational Finance, Artificial Stock Markets, Behavioral Finance, Micro-Macro Links, Multi-Agent Simulation, Stock Market Characteristics
Abstract: This paper studies the use of social simulation in linking micro level investor behaviour and macro level stock market dynamics. Empirical data from a survey on individual investors\' decision-making and social interaction was used to formalize the trading and interaction rules of the agents of the artificial stock market SimStockExchange. Multiple simulation runs were performed with this artificial stock market, which generated macro level results, like stock market prices and returns over time. These outcomes were subsequently compared to empirical macro level data from real stock markets. Partial qualitative as well as quantitative agreement between the simulated asset returns distributions and the asset returns distributions of the real stock markets was found.

Empirical Validation of Agent-Based Models: Alternatives and Prospects

Paul Windrum, Giorgio Fagiolo and Alessio Moneta
Journal of Artificial Societies and Social Simulation 10 (2) 8

Kyeywords: Methodology, Empirical Validation, Agent-Based Models, Simulation, Calibration, History-Friendly Models
Abstract: This paper addresses a set of methodological problems arising in the empirical validation of agent-based (AB) economics models and discusses how these are currently being tackled. These problems are generic for all those engaged in AB modelling, not just economists. The discussion is therefore of direct relevance to JASSS readers. The paper has two objectives. The first objective is the identification of a set of issues that are common to all modellers engaged in empirical validation. This gives rise to a novel taxonomy that captures the relevant dimensions along which AB modellers differ. The second objective is a focused discussion of three alternative methodological approaches being developed in AB economics - indirect calibration, the Werker-Brenner approach, and the history-friendly approach – and a set of (as yet) unresolved issues for empirical validation that require future research.

Dynamic Agent Compression

Stephen Wendel and Catherine Dibble
Journal of Artificial Societies and Social Simulation 10 (2) 9

Kyeywords: Agent-Based Modeling, Scaling, Homogeneity, Compression
Abstract: We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its use of memory and computational cycles. Modelers can use this increased efficiency to speed up the execution times, to conserve memory, or to scale up the complexity or number of agents in their simulations. We describe in detail an implementation of Dynamic Agent Compression that is lossless, i.e., no model detail is discarded during the compression process. We also contrast lossless compression to lossy compression, which promises greater efficiency gains yet may introduce artifacts in model behavior. The advantages outweigh the overhead of Dynamic Agent Compression in models where agents are unevenly heterogeneous — where a set of highly heterogeneous agents are intermixed with numerous other agents that fall into broad internally homogeneous categories. Dynamic Agent Compression is not appropriate in models with few, exclusively complex, agents.

Research on Multi-Agent Simulation of Epidemic News Spread Characteristics

Xiaoguang Gong and Renbin Xiao
Journal of Artificial Societies and Social Simulation 10 (3) 1

Kyeywords: Multi-Agent Simulation, News Spread, Small World Network , Epidemic
Abstract: The spread of news about an epidemic can easily lead to a social panic. In order to devise measures to control such a panic, it is necessary to consider characteristics of the spread of epidemic news, based on mechanisms at the individual level. In this paper, first, some features of multi-agent simulation are reviewed. Then a multi-agent simulation model of epidemic news spread (ENS) is designed and realized. Based on simulation experiments and sensitivity analyses, the influence of social relationships, the degree of trust in news of the epidemic, the epidemic spread intensity and the network structure of the epidemic news spread are studied. The research results include: (1) As the number of social relationships increases, the rate of spread of epidemic news rapidly rises, and the ratio of people who have heard the news directly decreases. The result is that the \'radiation effect\' of the epidemic news spread will be enhanced when the number of social relationships increases. (2) With the increase of the degree of trust in the news, the rate of spread of the news will also rapidly increase, but variation in the ratio of the people who have heard the news directly is not significant. This means that the \'radiation effect\' of the spread of the news does not change much more in relation to the degree of trust in the epidemic news. (3) The ratio of the people who have heard the news directly increases when the infection range increases (i.e. the spread intensity of epidemic increases), and vice versa. But the variation of the speed of the epidemic news spread is not significant. (4) When the network structure is assumed to be a small world network, the spread speed will be slower than that in a random network with the same average vertex degree and the forgetting speed will be faster than that in a random network with the same average vertex degree.

Open Access for Social Simulation

Gary Polhill and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 10 (3) 10

Kyeywords: Agent-Based Social Simulation, Replication, Software Licences, Documentation, Archiving
Abstract: We consider here issues of open access to social simulations, with a particular focus on software licences, though also briefly discussing documentation and archiving. Without any specific software licence, the default arrangements are stipulated by the Berne Convention (for those countries adopting it), and are unsuitable for software to be used as part of the scientific process (i.e. simulation software used to generate conclusions that are to be considered part of the scientific domain of discourse). Without stipulating any specific software licence, we suggest rights that should be provided by any candidate licence for social simulation software, and provide in an appendix an evaluation of some popularly used licences against these criteria.

Finite Neighborhood Binary Games: a Structural Study

Jijun Zhao, Ferenc Szidarovszky and Miklos N. Szilagyi
Journal of Artificial Societies and Social Simulation 10 (3) 3

Kyeywords: Agent-Based Simulation, N-Person Games, Structure Analysis, Equilibrium
Abstract: The purpose of this study is to present a systematic analysis of the long-term behavior of the agents of an artificial society under varying payoff functions in finite neighborhood binary games. By assuming the linearity of the payoffs of both cooperating and defecting agents, the type of the game is determined by four fundamental parameters. By fixing the values of three of them and systematically varying the fourth one we can observe a transition from Prisoner\'s Dilemma to Leader Game through Chicken and Benevolent Chicken Games. By using agent-based simulation we are able to observe the long-term behavior of the artificial society with different and gradually changing payoff structure. The difference between different games is explored and the effect of the transition from one game to the other on the society is investigated. The results depend on the personality types of the agents. In this study greedy and Pavlovian agents are considered. In the first case, we observe the most significant change in trajectory structure between Prisoner\'s Dilemma and Chicken Games showing significant difference in the behavioral patterns of the agents. Almost no changes can be observed between Benevolent Chicken and Leader Games, and only small change between Chicken and Benevolent Chicken. The trajectories change from always converging to regularly oscillating patterns with systematically altering amplitude and central values. The results are very similar whether the agents consider themselves as members of their neighborhoods or not. With Pavlovian agents no significant difference can be observed between the four games, the trajectories always converge and the limits smoothly and monotonically depend on the value of the varying parameter.

How Realistic Should Knowledge Diffusion Models Be?

Jean-Philippe Cointet and Camille Roth
Journal of Artificial Societies and Social Simulation 10 (3) 5

Kyeywords: Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks
Abstract: Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.

Information Feedback and Mass Media Effects in Cultural Dynamics

Juan Carlos González-Avella, Mario G. Cosenza, Konstantin Klemm, Víctor M. Eguíluz and Maxi San Miguel
Journal of Artificial Societies and Social Simulation 10 (3) 9

Kyeywords: Agent Based Model, Culture, Dissemination, Mass Media
Abstract: We study the effects of different forms of information feedback associated with mass media on an agent-agent based model of the dynamics of cultural dissemination. In addition to some processes previously considered, we also examine a model of local mass media influence in cultural dynamics. Two mechanisms of information feedback are investigated: (i) direct mass media influence, where local or global mass media act as an additional element in the network of interactions of each agent, and (ii) indirect mass media influence, where global media acts as a filter of the influence of the existing network of interactions of each agent. Our results generalize substantiate previous findings showing that cultural diversity builds-up by increasing the strength of the mass media influence. We find that this occurs independently of the mechanisms of action (direct or indirect) of the mass media message. However, through an analysis of the full range of parameters measuring cultural diversity, we establish that the enhancement of cultural diversity produced by interaction with mass media only occurs for strong enough mass media messages. In comparison with previous studies a main different result is that weak mass media messages, in combination with agent-agent interaction, are efficient in producing cultural homogeneity. Moreover, the homogenizing effect of weak mass media messages are more efficient for direct local mass media messages than for global mass media messages or indirect global mass media influences.

Groups of Agents with a Leader

Onofrio Gigliotta, Orazio Miglino and Domenico Parisi
Journal of Artificial Societies and Social Simulation 10 (4) 1

Kyeywords: Agent Based Models, Leaders, Social Simulation, Social Structure, Communication Topologies
Abstract: We describe simulations of groups of agents that have to reach a target in a two dimensional environment, the performance criterion being the time taken by the last agent to reach the target. If the target is within a given distance from the agent, the agent moves towards the target; otherwise it moves randomly. The simulations contrast groups with and without a leader, where a leader is a member of the group which other members of the group follow as it moves through the environment. We investigate three factors that affect group performance: (1) group size; (2) the presence or absence of an individual agent with the ability to detect targets at a greater distance than those \'visible\' to its companions; (3) the existence of a communication network among group members. The results show that, in groups without communication, leaders have a beneficial effect on group performance, especially in large groups and if the individual with better than average sensory capabilities is the leader of the group. However, in situations where group members can communicate, these results are reversed, with leaders being detrimental, rather than beneficial, to group performance

Making Models Match: Replicating an Agent-Based Model

Uri Wilensky and William Rand
Journal of Artificial Societies and Social Simulation 10 (4) 2

Kyeywords: Replication, Agent-Based Modeling, Verification, Validation, Scientific Method, Ethnocentrism
Abstract: Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent-based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.

Cultural Learning in a Dynamic Environment: an Analysis of Both Fitness and Diversity in Populations of Neural Network Agents

Dara Curran and Colm O'Riordan
Journal of Artificial Societies and Social Simulation 10 (4) 3

Kyeywords: Cultural Learning, Dynamic Environments, Diversity, Multi-Agent Systems, Artificial Life
Abstract: Evolutionary learning is a learning model that can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher/pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. This paper examines the effects of cultural learning on the evolutionary process of a population of neural networks. In particular, the paper examines the genotypic and phenotypic diversity of a population as well as its fitness. Using these measurements, it is possible to examine the effects of cultural learning on the population's genetic makeup. Furthermore, the paper examines whether cultural learning provides a more robust learning mechanism in the face of environmental changes. Three benchmark tasks have been chosen as the evolutionary task for the population: the bit-parity problem, the game of tic-tac-toe and the game of connect-four. Experiments are conducted with populations employing evolutionary learning alone and populations combining evolutionary and cultural learning in an environment that changes dramatically.

Simulating the Effect of Social Influence on Decision-Making in Small, Task-Oriented, Groups

Roy Wilson
Journal of Artificial Societies and Social Simulation 10 (4) 4

Kyeywords: Social Influence; Decision Processes; Social Networks; Group Dynamics; Simulation; Agent-Based Modeling
Abstract: This paper describes a simulation study of decision-making. It is based on a model of social influence in small, task-oriented, groups. A process model of dyadic social influence is built on top of a dynamic model of status and task participation that describes the emergence of a stable power and prestige order. Two models of group decision-making are examined: a static model for which the beliefs of actors do not change, and a process model for which they do as a function of the standing of each member of each interacting pair in the evolving power and prestige order. The models are compared on a set of N=111 cases, each requiring an affirmative or negative group response to a proposition A(c) that pertains to a case c. Initial beliefs are assigned to each of five members of distinct professions based on an analysis of independently collected behavioral data pertinent to the proposition to be affirmed or denied in each case. Although the two influence models yield identical decisions in 70% of the cases examined, the differences between them are statistically significant and in several instances show a medium effect size. Most importantly, the differences can be explained in terms of social influence and the status and task participation model on which it depends.

Using Computational Agents to Design Participatory Social Simulations

Minh Nguyen-Duc and Alexis Drogoul
Journal of Artificial Societies and Social Simulation 10 (4) 5

Kyeywords: Participatory Social Simulations, Agent-Based Social Simulations, Computational Agents, Role-Playing Games, Artificial Maieutics, User-Centered Design
Abstract: In social science, the role of stakeholders is increasing in the development and use of simulation models. Their participation in the design of agent-based models (ABMs) has widely been considered as an efficient solution to the validation of this particular type of model. Traditionally, "agents" (as basic model elements) have not been concerned with stakeholders directly but via designers or role-playing games (RPGs). In this paper, we intend to bridge this gap by introducing computational or software agents, implemented from an initial ABM, into a new kind of RPG, mediated by computers, so that these agents can interact with stakeholders. This interaction can help not only to elicit stakeholders' informal knowledge or unpredicted behaviours, but also to control stakeholders' focus during the games. We therefore formalize a general participatory design method using software agents, and illustrate it by describing our experience in a project aimed at developing agent-based social simulations in the field of air traffic management.

Studying Organisational Topology with Simple Computational Models

Anthony Dekker
Journal of Artificial Societies and Social Simulation 10 (4) 6

Kyeywords: Network Rewiring, Small World Networks, Self-Synchronization, Agent Simulation, Collaboration, Problem Solving
Abstract: The behaviour of many complex systems is influenced by the underlying network topology. In particular, this applies to social systems in which people or organisational units collaboratively solve problems. Network rewiring processes are one useful tool in understanding the relationship between network topology and behaviour. Here we use the Kawachi network rewiring process, together with three simple simulation models of organisational collaboration, to investigate the network characteristics that influence performance. The simulation models are based on the Assignment Problem, the Kuramoto Model from physics, and a novel model of collaborative problem-solving which involves finding numbers with certain characteristics, the existence of which is guaranteed by Lagrange\'s Theorem. For all three models, performance is best when the underlying organisational network has a low average distance between nodes. In addition, the third model identified long-range connectivity between nodes as an important predictor of performance. The commonly-used clustering coefficient, which is a measure of short-range connectivity, did not affect performance. We would expect that long-range network connectivity would also influence the behaviour of other complex systems displaying global self-synchronization. The paper also demonstrates the utility of simple computational models in studying issues of organisational topology.

The Impact of HIV/AIDS in the Context of Socioeconomic Stressors: an Evidence-Driven Approach

Shah Jamal Alam, Ruth Meyer, Gina Ziervogel and Scott Moss
Journal of Artificial Societies and Social Simulation 10 (4) 7

Kyeywords: Agent-Based Social Simulation, Evidence-Driven Modeling, Socioeconomic Stressors, HIV/AIDS Impact
Abstract: In this paper, we present an agent-based simulation model of the social impacts of HIV/AIDS in villages in the Sekhukhune district of the Limpopo province in South Africa. AIDS is a major concern in South Africa, not just in terms of disease spread but also in term of its impact on society and economic development. The impact of the disease cannot however be considered in isolation from other stresses, such as food insecurity, high climate variability, market fluctuations and variations in support from government and non-government sources. The model described in this paper focuses on decisions made at the individual and household level, based upon evidence from detailed case studies, and the different types of networks between these players that influence their decision making. Key to the model is that these networks are dynamic and co-evolving, something that has rarely been considered in social network analysis. The results presented here demonstrate how this type of simulation can aid better understanding of this complex interplay of issues. In turn, we hope that this will prove to be a powerful tool for policy development.

Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society

Phillip Stroud, Sara Del Valle, Stephen Sydoriak, Jane Riese and Susan Mniszewski
Journal of Artificial Societies and Social Simulation 10 (4) 9

Kyeywords: Agent Based Modeling, Computer Simulation, Epidemic Simulation, Public Health Policy
Abstract: EpiSimS is a massive simulation of the movements, activities, and social interactions of individuals in realistic synthetic populations, and of the dynamics of contagious disease spread on the resulting social contact network. This paper describes the assumptions and methodology in the EpiSimS model. It also describes and presents a simulation of the spatial dynamics of pandemic influenza in an artificial society constructed to match the demographics of southern California. As an example of the utility of the massive simulation approach, we demonstrate a strong correlation between local demographic characteristics and pandemic severity, which gives rise to previously unanticipated spatial pandemic hotspots. In particular, the average household size in a census tract is strongly correlated with the clinical attack rate computed by the simulation. Public heath agencies with responsibility for communities having relatively high population per household should expect to be more severely hit by a pandemic.

An Agent-Based Representation of the Garbage Can Model of Organizational Choice

Guido Fioretti and Alessandro Lomi
Journal of Artificial Societies and Social Simulation 11 (1) 1

Kyeywords: Organization Theory, Garbage Can Model, Agent-Based Modelling
Abstract: Cohen, March and Olsen\'s Garbage Can Model (GCM) of organizational choice represent perhaps the first – and remains by far the most influential –agent-based representation of organizational decision processes. According to the GCM organizations are conceptualized as crossroads of time-dependent flows of four distinct classes of objects: \'participants,\' \'opportunities,\' \'solutions\' and \'problems.\' Collisions among the different objects generate events called \'decisions.\' In this paper we use NetLogo to build an explicit agent-based representation of the original GCM. We conduct a series of simulation experiments to validate and extend some of the most interesting conclusions of the GCM. We show that our representation is able to reproduce a number of properties of the original model. Yet, unlike the original model, in our representation these properties are not encoded explicitly, but emerge from general principles of the Garbage Can decision processes.

How to Choose the Bidding Strategy in Continuous Double Auctions: Imitation Versus Take-The-Best Heuristics

Marta Posada and Adolfo López-Paredes
Journal of Artificial Societies and Social Simulation 11 (1) 6

Kyeywords: Agent Based Models, Double Auction, Individual and Social Learning, Computational Organization, Bounded Rationality
Abstract: Human-subject market experiments have established in a wide variety of environments that the Continuous Double Auction (CDA) guarantees the maximum efficiency (100 percent) and the transaction prices converge quickly to the competitive equilibrium price. Since in human-subject experiments we can not control the agents\' behaviour, one would like to know if these properties (quick price convergence and high market efficiency) hold for alternative agents\' bidding strategies. We go a step farther: we substitute human agents by artificial agents to calibrate the agents\' behaviour . In this paper we demonstrate that price convergence and allocative market efficiency in CDA markets depend on the proportion of the bidding strategies (Kaplan, Zero-Intelligence Plus, and GD) that agents have on both market sides. As a result, price convergence may not be achieved. The interesting question to ask is: can convergence be assured if the agents choose their bidding strategies? Since humans are frugal we explore two fast & frugal heuristics (imitation versus take-the-best) to choose one of three bidding strategies in order to answer this question. We find that the take-the-best choice performs much better than the imitation heuristic in the three market environments analyzed. Our experiment can be interpreted as a test to see whether an individual learning outperforms social learning or individual rationality (take-the-best) outperforms ecological rationality (imitation), for a given relevant institution (the CDA) in alternative environments.

Reinforcement Learning Dynamics in Social Dilemmas

Segismundo S. Izquierdo, Luis R. Izquierdo and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 11 (2) 1

Kyeywords: Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning
Abstract: In this paper we replicate and advance Macy and Flache\'s (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players\' decisions can change the dynamics of the model dramatically.

Differential Equation Models Derived from an Individual-Based Model Can Help to Understand Emergent Effects

Sylvie Huet and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 11 (2) 10

Kyeywords: Primacy Effect, Information Filtering, Agent-Based Model, Aggregated Model, Collective Effects of Interactions, Double-Modelling
Abstract: We study a model of primacy effect on individual's attitude. Typically, when receiving a strong negative feature first, the individual keeps a negative attitude whatever the number of moderate positive features it receives afterwards. We consider a population of individuals, which receive the features from a media, and communicate with each other. We observe that interactions favour the primacy effect, compared with a population of isolated individuals. We derive a differential equation system ruling the evolution of probabilities that individuals retain different sets of features. The study of this aggregated model of the IBM shows that interaction can increase or decrease the number of individuals exhibiting a primacy effect. We verify on the IBM that the interactions can decrease the primacy effect in the conditions suggested by the study of the aggregated model. We finally discuss the interest of such a double-modelling approach (using a model of the individual based model) for this application.

A Model-To-Model Analysis of Bertrand Competition

Xavier Vilà
Journal of Artificial Societies and Social Simulation 11 (2) 11

Kyeywords: Agent-Based Computational Economics, Model-To-Model Analysis,
Abstract: This paper studies a version of the classical Bertrand model in which consumers exhibit some strategic behavior when deciding from what seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers\' behavior influences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the behavior of the process. Second, we use finite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the first approach and still obtain the same basic results. It is suggested that the limitations of the first approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach while obtaining the same basic results.

Horizontal and Vertical Multiple Implementations in a Model of Industrial Districts

Ugo Merlone, Michele Sonnessa and Pietro Terna
Journal of Artificial Societies and Social Simulation 11 (2) 5

Kyeywords: Replication of Models; Model Validation; Agent-Based Simulation
Abstract: In this paper we discuss strategies concerning the implementation of an agent-based simulation of complex phenomena. The model we consider accounts for population decomposition and interaction in industrial districts. The approach we follow is twofold: on one hand, we implement progressively more complex models using different approaches (vertical multiple implementations); on the other hand, we replicate the agent-based simulation with different implementations using jESOF, JAS and plain C++ (horizontal multiple implementations). By using both different implementation approaches and a multiple implementation strategy, we highlight the benefits that arise when the same model is implemented on radically different simulation environments, comparing the advantages of multiple modeling implementations. Our findings provide some important suggestions in terms of model validation, showing how models of complex systems tend to be extremely sensitive to implementation details. Finally we point out how statistical techniques may be necessary when comparing different platform implementations of a single model.

REsCape: an Agent-Based Framework for Modeling Resources, Ethnicity, and Conflict

Ravi Bhavnani, Dan Miodownik and Jonas Nart
Journal of Artificial Societies and Social Simulation 11 (2) 7

Kyeywords: Agent-Based Model, Ethnicity, Salience, Polarization, Domination, Civil War, Greed, Natural Resources
Abstract: This research note provides a general introduction to REsCape: an agent-based computational framework for studying the relationship between natural resources, ethnicity, and civil war. By permitting the user to specify: (i) different resource profiles ranging from a purely agrarian economy to one based on the artisanal or industrial extraction of alluvial or kimberlite diamonds; (ii) different patterns of ethnic domination, ethnic polarization, and varying degrees of ethnic salience; as well as (iii) specific modes of play for key agents, the framework can be used to assess the effects of key variables — whether taken in isolation or in various combinations — on the onset and duration of civil war. Our objective is to make REsCape available as an open source toolkit in the future, one that can be used, modified, and refined by students and scholars of civil war.

Progress in Model-To-Model Analysis

Juliette Rouchier, Claudio Cioffi-Revilla, Gary Polhill and Keiki Takadama
Journal of Artificial Societies and Social Simulation 11 (2) 8

Kyeywords: Social Simulation, Agent-Based Modelling, Comparative Computational Methodology, Validation, Replication
Abstract: [No abstract]

Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game

Keiki Takadama, Tetsuro Kawai and Yuhsuke Koyama
Journal of Artificial Societies and Social Simulation 11 (2) 9

Kyeywords: Micro- and Macro-Level Validation, Agent-Based Simulation, Agent Modeling, Sequential Bargaining Game, Reinforcement Learning
Abstract: This paper addresses both micro- and macro-level validation in agent-based simulation (ABS) to explore validated agents that can reproduce not only human-like behaviors externally but also human-like thinking internally. For this purpose, we employ the sequential bargaining game, which can investigate a change in humans' behaviors and thinking longer than the ultimatum game (i.e., one-time bargaining game), and compare simulation results of Q-learning agents employing any type of the three types of action selections (i.e., the ε-greedy, roulette, and Boltzmann distribution selections) in the game. Intensive simulations have revealed the following implications: (1) Q-learning agents with any type of three action selections can reproduce human-like behaviors but not human-like thinking, which means that they are validated from the macro-level viewpoint but not from the micro-level viewpoint; and (2) Q-learning agents employing Boltzmann distribution selection with changing the random parameter can reproduce both human-like behaviors and thinking, which means that they are validated from both micro- and macro-level viewpoints.

Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks

Dmytro Tykhonov, Catholijn Jonker, Sebastiaan Meijer and Tim Verwaart
Journal of Artificial Societies and Social Simulation 11 (3) 1

Kyeywords: Trust, Deception, Supply Chain, Multi-Agent System, Simulation
Abstract: This paper describes a multi-agent simulation model of the Trust And Tracing game. The Trust And Tracing game is a gaming simulation for human players, developed as a research tool for data collection on human behaviour in food supply chains with asymmetric information about food quality and food safety. Important issues in the game are opportunistic behaviour (deceit), trust and institutional arrangements for enforcing compliance. The goal is to improve the understanding of human decision making with respect to these issues. To this end multi-agent simulation can be applied to simulate the effect of models of individual decision making in partner selection, negotiation, deceit and trust on system behaviour. The combination of human gaming simulation and multi-agent simulation offers a basis for model refinement in a cycle of validation, experimentation, and formulation of new hypotheses. This paper describes a first round of model formulation and validation. The models presented are validated by a series of experiments performed by the implemented simulation system, of which the outcomes are compared on aggregated level to the outcomes of games played by humans. The experiments cover in a systematic way the important variations in parameter settings possible in the game and in the characteristics of the agents. The simulation results show the same tendencies of behaviour as the observed human games.

A Replication That Failed - on the Computational Model in 'Michael W. Macy and Yoshimichi Sato: Trust, Cooperation and Market Formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002'

Oliver Will and Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 11 (3) 3

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: The article describes how and why we failed to replicate main effects of a computational model that Michael Macy and Yoshimichi Sato published in the Proceedings of the National Academy of Sciences (May 2002). The model is meant to answer a fundamental question about social life: Why, when and how is it possible to build trust with distant people? Based on their model, Macy and Sato warn the US society about an imminent danger: the possible break down of trust caused by too much social mobility. But the computational evidence for exactly that result turned out not to be replicable.

Modelling Socio-Technical Transition Patterns and Pathways

Noam Bergman, Alex Haxeltine, Lorraine Whitmarsh, Jonathan Köhler, Michel Schilperoord and Jan Rotmans
Journal of Artificial Societies and Social Simulation 11 (3) 7

Kyeywords: Complex Systems, Agent-Based Modelling, Social Simulation, Transitions, Transition Theory
Abstract: We report on research that is developing a simulation model for assessing systemic innovations, or 'transitions', of societal systems towards a more sustainable development. Our overall aim is to outline design principles for models that can offer new insights into tackling persistent problems in large-scale systems, such as the European road transport system or the regional management of water resources. The systemic nature of these problems is associated with them being complex, uncertain and cutting across a number of sectors, and indicates a need for radical technological and behavioural solutions that address changes at the systems level rather than offering incremental changes within sub-systems. Model design is inspired by recent research into transitions, an emerging paradigm which provides a framework for tackling persistent problems. We use concepts from the literature on transitions to develop a prototype of a generic 'transition model'. Our prototype aims to capture different types of transition pathways, using historical examples such as the transition from horse-drawn carriages to cars or that from sailing ships to steam ships. The model combines agent-based modelling techniques and system dynamics, and includes interactions of individual agents and sub-systems, as well as cumulative effects on system structures. We show success in simulating different historical transition pathways by adapting the model's parameters and rules for each example. Finally, we discuss the improvements necessary for systematically exploring and detailing transition pathways in empirical case-study applications to current and future transitions such as the transition to a sustainable transport system in Europe.

Simulating Evolutionary Games: A Python-Based Introduction

Alan G. Isaac
Journal of Artificial Societies and Social Simulation 11 (3) 8

Kyeywords: Agent-Based Simulation, Python, Prisoner's Dilemma
Abstract: This paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.

Agent-Based Emergency Evacuation Simulation with Individuals with Disabilities in the Population

Keith Christensen and Yuya Sasaki
Journal of Artificial Societies and Social Simulation 11 (3) 9

Kyeywords: Agent-Based Simulation, Individual-Based Simulation, Disability, Emergency Egress, Evacuation, Reinforcement Learning
Abstract: Catastrophic events have raised numerous issues concerning how effectively the built environment accommodates the evacuation needs of individuals with disabilities. Individuals with disabilities represent a significant, yet often overlooked, portion of the population disproportionately affected in emergency situations. Incorporating disability considerations into emergency evacuation planning, preparation, and other activities is critical. The most widely applied method used to evaluate how effectively the built environment accommodates emergency evacuations is agent-based or microsimulation modeling. However, current evacuation models do not adequately address individuals with disabilities in their simulated populations. This manuscript describes the BUMMPEE model, an agent-based simulation capable of classifying the built environment according to environmental characteristics and simulating a heterogeneous population according to variation in individual criteria. The method allows for simulated behaviors which more aptly represent the diversity and prevalence of disabilities in the population and their interaction with the built environment. Comparison of the results of an evacuation simulated using the BUMMPEE model is comparable to a physical evacuation with a similar population and setting. The results of the comparison indicate that the BUMMPEE model is a reasonable approach for simulating evacuations representing the diversity and prevalence of disability in the population

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Mikola Lysenko and Roshan M. D'Souza
Journal of Artificial Societies and Social Simulation 11 (4) 10

Kyeywords: GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations
Abstract: Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.

Reply to Will and Hegselmann

Michael Macy and Yoshimichi Sato
Journal of Artificial Societies and Social Simulation 11 (4) 11

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: [No abstract]

The Dynamics of Public Opinion in Complex Networks

Shuguang Suo and Yu Chen
Journal of Artificial Societies and Social Simulation 11 (4) 2

Kyeywords: Public Opinion, Complex Network, Consensus, Agent-Based Model
Abstract: This paper studies the problem of public opinion formation and concentrates on the interplays among three factors: individual attributes, environmental influences and information flow. We present a simple model to analyze the dynamics of four types of networks. Our simulations suggest that regular communities establish not only local consensus, but also global diversity in public opinions. However, when small world networks, random networks, or scale-free networks model social relationships, the results are sensitive to the elasticity coefficient of environmental influences and the average connectivity of the type of network. For example, a community with a higher average connectivity has a higher probability of consensus. Yet, it is misleading to predict results merely based on the characteristic path length of networks. In the process of changing environmental influences and average connectivity, sensitive areas are discovered in the system. By sensitive areas we mean that interior randomness emerges and we cannot predict unequivocally how many opinions will remain upon reaching equilibrium. We also investigate the role of authoritative individuals in information control. While enhancing average connectivity facilitates the diffusion of the authoritative opinion, it makes individuals subject to disturbance from non-authorities as well. Thus, a moderate average connectivity may be preferable because then the public will most likely form an opinion that is parallel with the authoritative one. In a community with a scale-free structure, the influence of authoritative individuals keeps constant with the change of the average connectivity. Provided that the influence of individuals is proportional to the number of their acquaintances, the smallest percentage of authorities is required for a controlled consensus in a scale free network. This study shows that the dynamics of public opinion varies from community to community due to the different degree of impressionability of people and the distinct social network structure of the community.

Homo Socionicus: a Case Study of Simulation Models of Norms

Martin Neumann
Journal of Artificial Societies and Social Simulation 11 (4) 6

Kyeywords: Norms, Normative Agent-Based Social Simulation, Role Theory, Methodological Individualism
Abstract: This paper describes a survey of normative agent-based social simulation models. These models are examined from the perspective of the foundations of social theory. Agent-based modelling contributes to the research program of methodological individualism. Norms are a central concept in the role theoretic concept of action in the tradition of Durkheim and Parsons. This paper investigates to what extend normative agent-based models are able to capture the role theoretic concept of norms. Three methodological core problems are identified: the question of norm transmission, normative transformation of agents and what kind of analysis the models contribute. It can be shown that initially the models appeared only to address some of these problems rather than all of them simultaneously. More recent developments, however, show progress in that direction. However, the degree of resolution of intra agent processes remains too low for a comprehensive understanding of normative behaviour regulation.

Governments, Civilians, and the Evolution of Insurgency: Modeling the Early Dynamics of Insurgencies

D. Scott Bennett
Journal of Artificial Societies and Social Simulation 11 (4) 7

Kyeywords: Agent Based Models, Insurgency, Dynamics, Civil War
Abstract: This paper models the early dynamics of insurgency using an agent-based computer simulation of civilians, insurgents, and soldiers. In the simulation, insurgents choose to attack government forces, which then strike back. Such government counterattacks may result in the capture or killing of insurgents, may make nearby civilians afraid to become insurgents, but may also increase the anger of surrounding civilians if there is significant collateral damage. If civilians become angry enough, they become new insurgents. I simulate the dynamics of these interactions, focusing on the effectiveness of government forces at capturing insurgents vs. their accuracy in avoiding collateral damage. The simulations suggest that accuracy (avoidance of collateral damage) is more important for the long-term defeat of insurgency than is effectiveness at capturing insurgents in any given counterattack. There also may be a critical 'tipping point' for accuracy below which the length of insurgencies increases dramatically. The dynamics of how insurgencies grow or decline in response to various combinations of government accuracy and effectiveness illustrate the tradeoffs faced by governments in dealing with the early stages of an insurgency.

Replication in the Deception and Convergence of Opinions Problem

André C. R. Martins
Journal of Artificial Societies and Social Simulation 11 (4) 8

Kyeywords: Replication, Deception, Rational Agents, Epistemology, Opinion Dynamics
Abstract: Reported results of experiments are usually trustworthy, but some of them might be obtained from errors or deceptive behavior. When an agent only read articles about experimental results and use the articles to update his subjective opinions about different theories, the existence of deception can have severe consequences. An earlier attempt to solve that problem suggested that reading replicated results would solve the problems associated with the existence of deception. In this paper, we show that result is not a general case and, for experiments subject to statistical uncertainty, the solution is simply wrong. The analysis of the effect of replicated experiments is corrected here by introducing a differentiation between honest and dishonest mistakes. We observe that, although replication does solve the problem of no convergence, under some circumstances, it is not enough for achieving a reasonable amount of certainty for a realistic number of read reports of experiments.

Errors and Artefacts in Agent-Based Modelling

José Manuel Galán, Luis R. Izquierdo, Segismundo S. Izquierdo, José Ignacio Santos, Ricardo del Olmo, Adolfo López-Paredes and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 12 (1) 1

Kyeywords: Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles
Abstract: The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.

Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 12 (1) 11

Kyeywords: Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application
Abstract: The growing field of studies of opinion formation using physical formalisms and computer simulation based tools suffers from relative lack of connection to the 'real world' societal behaviour. Such sociophysics research should aim at explaining observations or at proposing new ones. Unfortunately, this is not always the case, as many works concentrate more on the models themselves than on the social phenomena. Moreover, the simplifications proposed in simulations often sacrifice realism on the altar of computability. There are several ways to improve the value of the research, the most important by promoting truly multidisciplinary cooperation between physicists aiming to describe social phenomena and sociologists studying the phenomena in the field. In the specific case of modelling of opinion formation there are a few technical ideas which might bring the computer models much closer to reality, and therefore to improve the predictive value of the sociophysics approach.

Exploring Agent-Based Methods for the Analysis of Payment Systems: A Crisis Model for StarLogo TNG

Luca Arciero, Claudia Biancotti, Leandro D'Aurizio and Claudio Impenna
Journal of Artificial Societies and Social Simulation 12 (1) 2

Kyeywords: Agent-Based Modeling, Payment Systems, RTGS, Liquidity, Crisis Simulation
Abstract: This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are then settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants\' liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider.

Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change

Tatiana Filatova, Dawn C. Parker and Anne van der Veen
Journal of Artificial Societies and Social Simulation 12 (1) 3

Kyeywords: Location Choice, Urban Land Market, Agent-Based Computational Economics, Land Use, Land Rent Gradient, Spatial Simulation
Abstract: We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller side; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a \'sellers\' market\' to a \'buyers\' market\'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion.

Games on Cellular Spaces: How Mobility Affects Equilibrium

Pedro Ribeiro de Andrade, Antonio Miguel Vieira Monteiro, Gilberto Câmara and Sandra Sandri
Journal of Artificial Societies and Social Simulation 12 (1) 5

Kyeywords: Spatial Games, Agent-Based Modelling, Mobility, Satisfaction, Chicken Game, Nash Equilibrium
Abstract: In this work we propose a new model for spatial games. We present a definition of mobility in terms of the satisfaction an agent has with its spatial location. Agents compete for space through a non-cooperative game by using mixed strategies. We are particularly interested in studyig the relation between Nash equilibrium and the winner strategy of a given model with mobility, and how the mobility can affect the results. The experiments show that mobility is an important variable concerning spatial games. When we change parameters that affect mobility, it may lead to the success of strategies away from Nash equilibrium.

A Proximate Mechanism for Communities of Agents to Commemorate Long Dead Ancestors

Bill Tomlinson
Journal of Artificial Societies and Social Simulation 12 (1) 7

Kyeywords: Agent Based Models, Ancestor Commemoration, Dominance Relationships, Communication, Cooperation, Memory
Abstract: Many human cultures engage in the collective commemoration of dead members of their community. Ancestor veneration and other forms of commemoration may help to reduce social distance within groups, thereby encouraging reciprocity and providing a significant survival advantage. Here we present a simulation in which a prototypical form of ancestor commemoration arises spontaneously among computational agents programmed to have a small number of established human capabilities. Specifically, ancestor commemoration arises among agents that: a) form relationships with each other, b) communicate those relationships to each other, and c) undergo cycles of life and death. By demonstrating that ancestor commemoration could have arisen from the interactions of a small number of simpler behavioural patterns, this simulation may provide insight into the workings of human cultural systems, and ideas about how to study ancestor commemoration among humans.

A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community

Conrad Power
Journal of Artificial Societies and Social Simulation 12 (1) 8

Kyeywords: Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems
Abstract: The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.

Contra Epstein, Good Explanations Predict

Nicholas S. Thompson and Patrick Derr
Journal of Artificial Societies and Social Simulation 12 (1) 9

Kyeywords: ABM, Agent Based Model, Modeling, Prediction, Explanation, Philosophy of Science
Abstract: Epstein has argued that an explanation\'s capacity to make predictions should play a minor role in its evaluation . This view contradicts centuries of scientific practice and, at least, decades of philosophy of science. We argue that the view is not only unfounded but seems to arise from a mistaken fear that ABM models are in need of defense against the criticism that they don\'t necessarily forecast events in the natural or social world.

Design Guidelines for Agent Based Model Visualization

Daniel Kornhauser, Uri Wilensky and William Rand
Journal of Artificial Societies and Social Simulation 12 (2) 1

Kyeywords: Visualization, Design, Graphics, Guidelines, Communication, Agent-Based Modeling
Abstract: In the field of agent-based modeling (ABM), visualizations play an important role in identifying, communicating and understanding important behavior of the modeled phenomenon. However, many modelers tend to create ineffective visualizations of Agent Based Models (ABM) due to lack of experience with visual design. This paper provides ABM visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations. We begin by introducing a non-hierarchical categorization of ABM visualizations. This categorization serves as a starting point in the creation of an ABM visualization. We go on to present well-known design techniques in the context of ABM visualization. These techniques are based on Gestalt psychology, semiology of graphics, and scientific visualization. They improve the visualization design by facilitating specific tasks, and providing a common language to critique visualizations through the use of visual variables. Subsequently, we discuss the application of these design techniques to simplify, emphasize and explain an ABM visualization. Finally, we illustrate these guidelines using a simple redesign of a NetLogo ABM visualization. These guidelines can be used to inform the development of design tools that assist users in the creation of ABM visualizations.

Tools of the Trade: A Survey of Various Agent Based Modeling Platforms

Cynthia Nikolai and Gregory Madey
Journal of Artificial Societies and Social Simulation 12 (2) 2

Kyeywords: Agent Based Modeling, Individual Based Model, Multi Agent Systems
Abstract: Agent Based Modeling (ABM) toolkits are as diverse as the community of people who use them. With so many toolkits available, the choice of which one is best suited for a project is left to word of mouth, past experiences in using particular toolkits and toolkit publicity. This is especially troublesome for projects that require specialization. Rather than using toolkits that are the most publicized but are designed for general projects, using this paper, one will be able to choose a toolkit that already exists and that may be built especially for one's particular domain and specialized needs. In this paper, we examine the entire continuum of agent based toolkits. We characterize each based on 5 important characteristics users consider when choosing a toolkit, and then we categorize the characteristics into user-friendly taxonomies that aid in rapid indexing and easy reference.

Social Circles: A Simple Structure for Agent-Based Social Network Models

Lynne Hamill and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 12 (2) 3

Kyeywords: Social Networks, Personal Networks, Agent-Based Models
Abstract: None of the standard network models fit well with sociological observations of real social networks. This paper presents a simple structure for use in agent-based models of large social networks. Taking the idea of social circles, it incorporates key aspects of large social networks such as low density, high clustering and assortativity of degree of connectivity. The model is very flexible and can be used to create a wide variety of artificial social worlds.

Agent-Based Simulation on Women's Role in a Family Line on Civil Service Examination in Chinese History

Chao Yang, Kurahashi Setsuya, Keiko Kurahashi, Isao Ono and Takao Terano
Journal of Artificial Societies and Social Simulation 12 (2) 5

Kyeywords: Agent-Based Simulation, Grid Oriented Genetic Algorithm, Inverse Simulation, Family Norm, Civil Service Examination
Abstract: In this paper, following our previous work on civil service examinations in imperial China, we investigate women's role in a Chinese historical family line using an agent-based simulation (ABS) model with a grid oriented genetic algorithm (GOGA) framework. We utilize a GOGA framework, because our ABS had such large parameter spaces with real values that it required much greater computational resources. First, we studied the genealogical records. Second, based on that study, we implemented an agent-based model with the family lines branched out into two clusters to compare different family norms. Third, using an "inverse simulation" technique, we optimized the agent-based model in order to fit the simulation profiles to real profile data with real-coded GA. From these intensive experiments, we have found that (1) The combined influence of the father, uncle, mother and the aunt has important significance in maintaining a successful family norm, and (2) a particular role of the aunt to pass it on as well.

Punishment Deters Crime Because Humans Are Bounded in Their Strategic Decision-Making

Heiko Rauhut and Marcel Junker
Journal of Artificial Societies and Social Simulation 12 (3) 1

Kyeywords: Crime, Punishment, Control, Bounded Rationality, Agent-Based Simulation, Experiment, Game Theory
Abstract: Is it rational to reduce criminal activities if punishments are increased? While intuition might suggest so, game theory concludes differently. From the game theoretical perspective, inspectors anticipate the effect of increased punishments on criminal behavior and reduce their inspection activities accordingly. This implies that higher punishments reduce inspections and do not affect crime rates. We present two laboratory experiments, which challenge this perspective by demonstrating that both, criminals and inspectors, are affected by punishment levels. Thereupon, we investigate with agent-based simulations, whether models of bounded rationality can explain our empirical data. We differentiate between two kinds of bounded rationality; the first considers bounded learning from social interaction, the second bounded decision-making. Our results suggest that humans show both kinds of bounded rationality in the strategic situation of crime, control and punishment. We conclude that it is not the rationality but the bounded rationality in humans that makes punishment effective.

Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study

Whan-Seon Kim
Journal of Artificial Societies and Social Simulation 12 (3) 4

Kyeywords: Complex Adaptive System, Agent-Based Social Simulation, Supply Network, Trust
Abstract: This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. And it applies agent-based social simulation (ABSS), a research method of simulating social systems under the CAS paradigm, to observe emergent outcomes. The main purposes of this paper are to consider a social factor, trust, in modeling the agents\' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge. Also, it is shown that agents\' decision-making behavior based on the trust relationship can contribute to the reduction in the variability of inventory levels. This result can be explained by the fact that mutual trust relationship based on the past experiences of trading diminishes an agent\'s uncertainties about the trustworthiness of its trading partners and thereby tends to stabilize its inventory levels.

An Analysis of the Insertion of Virtual Players in GMABS Methodology Using the Vip-JogoMan Prototype

Diana Adamatti, Jaime Simão Sichman and Helder Coelho
Journal of Artificial Societies and Social Simulation 12 (3) 7

Kyeywords: Role-Playing Games, Multi-Agent Based Simulation, Natural Resources, Virtual Players
Abstract: The GMABS (Games and Multi-Agent-Based Simulation) methodology was created from the integration of RPG and MABS techniques. This methodology links the dynamic capacity of MABS (Multi-Agent-Based Simulation) and the discussion and learning capacity of RPG (Role-Playing Games). Using GMABS, we have developed two prototypes in the natural resources management domain. The first prototype, called JogoMan (Adamatti et. al, 2005), is a paper-based game: all players need to be physically present in the same place and time, and there is a minimum needed number of participants to play the game. In order to avoid this constraint, we have built a second prototype, called ViP-JogoMan (Adamatti et. al, 2007), which is an extension of the first one. This second game enables the insertion of virtual players that can substitute some real players in the game. These virtual players can partially mime real behaviors and capture autonomy, social abilities, reaction and adaptation of the real players. We have chosen the BDI architecture to model these virtual players, since its paradigm is based on folk psychology; hence, its core concepts easily map the language that people use to describe their reasoning and actions in everyday life. ViP-JogoMan is a computer-based game, in which people play via Web, players can be in different places and it does not have a hard constraint regarding the minimum number of real players. Our aim in this paper is to present some test results obtained with both prototypes, as well as to present a preliminary discussion on how the insertion of virtual players has affected the game results.

Agent Street: An Environment for Exploring Agent-Based Models in Second Life

Andrew Crooks, Andrew Hudson-Smith and Joel Dearden
Journal of Artificial Societies and Social Simulation 12 (4) 10

Kyeywords: Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life
Abstract: Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.

Agent Based Modeling and Simulation: An Informatics Perspective

Stefania Bandini, Sara Manzoni and Giuseppe Vizzari
Journal of Artificial Societies and Social Simulation 12 (4) 4

Kyeywords: Multi-Agent Systems, Agent-Based Modeling and Simulation
Abstract: The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.

On the Effects of Skill Upgrading in the Presence of Spatial Labor Market Frictions: An Agent-Based Analysis of Spatial Policy Design

Herbert Dawid, Simon Gemkow, Philipp Harting and Michael Neugart
Journal of Artificial Societies and Social Simulation 12 (4) 5

Kyeywords: Agent-Based Model, Skills, Innovation, Regional Policy
Abstract: We report results of economic policy experiments carried out in the framework of the EURACE agent-based macroeconomic model featuring a distinct geographical dimension and heterogeneous workers with respect to skill types. Using a calibrated model able to replicate a range of stylized facts of goods and labor markets, it is examined in how far e ffects di ffer if policy measures aiming at an improvement of general skills are uniformly spread over all regions in the economy or focused in one particular region. We find that it depends on the level of spatial frictions on the labor market how the spatial distribution of policy measures aff ects the e ffects of the policy. Furthermore, we show that a reduction in spatial frictions does not necessarily improve the growth of output and household income.

A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs

Simon Deichsel and Andreas Pyka
Journal of Artificial Societies and Social Simulation 12 (4) 6

Kyeywords: Methodology, Agent-Based Modelling, Assumptions, Calibration
Abstract: The issues of empirical calibration of parameter values and functional relationships describing the interactions between the various actors plays an important role in agent based modelling. Agent-based models range from purely theoretical exercises focussing on the patterns in the dynamics of interactions processes to modelling frameworks which are oriented closely at the replication of empirical cases. ABMs are classified in terms of their generality and their use of empirical data. In the literature the recommendation can be found to aim at maximizing both criteria by building so-called 'abductive models'. This is almost the direct opposite of Milton Friedman's famous and provocative methodological credo 'the more significant a theory, the more unrealistic the assumptions'. Most methodologists and philosophers of science have harshly criticised Friedman's essay as inconsistent, wrong and misleading. By presenting arguments for a pragmatic reinterpretation of Friedman's essay, we will show why most of the philosophical critique misses the point. We claim that good simulations have to rely on assumptions, which are adequate for the purpose in hand and those are not necessarily the descriptively accurate ones.

Cooperation in the Prisoner's Dilemma Game Based on the Second-Best Decision

Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 12 (4) 7

Kyeywords: Cooperation, Altruism, Agent-Based Simulation, Evolutionary Game Theory
Abstract: In the research addressing the prisoner's dilemma game, the effectiveness and accountableness of the method allowing for the emergence of cooperation is generally discussed. The most well-known solutions for this question are memory based iteration, the tag used to distinguish between defector and cooperator, the spatial structure of the game and the either direct or indirect reciprocity. We have also challenged to approach the topic from a different point of view namely that temperate acquisitiveness in decision making could be possible to achieve cooperation. It was already shown in our previous research that the exclusion of the best decision had a remarkable effect on the emergence of an almost cooperative state. In this paper, we advance the decision of our former research to become more explainable by introducing the second-best decision. If that decision is adopted, players also reach an extremely high level cooperative state in the prisoner's dilemma game and also in that of extended strategy expression. The cooperation of this extended game is facilitated only if the product of two parameters is under the criticality. In addition, the applicability of our model to the problem in the real world is discussed.

Sendero: An Extended, Agent-Based Implementation of Kauffman's NKCS Model

Julian Padget, Richard Vidgen, James Mitchell, Amy Marshall and Rick Mellor
Journal of Artificial Societies and Social Simulation 12 (4) 8

Kyeywords: Coevolution, Agent-Based Modelling, NK, NKCS, Fitness Landscape
Abstract: The idea of agents exploring a fitness landscape in which they seek to move from 'fitness valleys' to higher 'fitness peaks' has been presented by Kauffman in the NK and NKCS models. The NK model addresses single species while the NKCS extension illustrates coevolving species on coupled fitness landscapes. We describe an agent-based simulation (Sendero), built in Repast, of the NK and NKCS models. The results from Sendero are validated against Kauffman's findings for the NK and NKCS models. We also describe extensions to the basic model, including population dynamics and communication networks for NK, and directed graphs and variable change rates for NKCS. The Sendero software is available as open source under the BSD licence and is thus available for download and extension by the research community.

A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)

Brian Heath, Raymond Hill and Frank Ciarallo
Journal of Artificial Societies and Social Simulation 12 (4) 9

Kyeywords: Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose
Abstract: In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.

Norm Internalisation in Human and Artificial Intelligence

Martin Neumann
Journal of Artificial Societies and Social Simulation 13 (1) 12

Kyeywords: Normative Agent Architectures, Norm Internalisation, Socialisation Theories, Theoretical Validity
Abstract: In this article, principles of architectures relating to normative agents are evaluated with regard to the question whether and to what extend results of empirical research are incorporated in the architecture. In the human sciences, internalisation is a crucial element within the concept of norms. Internalisation distinguishes normative behaviour regulation from mere coercion. The aim of this article is to begin answering the question of to what extent normative agent architectures represent the theoretical construct of norm internalisation. The relevant research in this area may be found in socialisation research in psychology and sociology. Evaluation of conclusions from the empirical sciences allows to identify drawbacks and opportunities in existing architectures, as well as to develop suggestions for future development.

Large Scale Daily Contacts and Mobility Model - an Individual-Based Countrywide Simulation Study for Poland

Franciszek Rakowski, Magdalena Gruziel, Michal Krych and Jan P Radomski
Journal of Artificial Societies and Social Simulation 13 (1) 13

Kyeywords: Agent Based Model, Educational Availability, Daily Commuting, Social Network, Virtual Society Simulations
Abstract: In this study we describe a simulation platform used to create a virtual society of Poland, with a particular emphasis on contact patterns arising from daily commuting to schools or workplaces. In order to reproduce the map of contacts, we are using a geo-referenced Agent Based Model. Within this framework, we propose a set of different stochastic algorithms, utilizing available aggregated census data. Based on this model system, we present selected statistical analysis, such as the accessibility of schools or the location of rescue service units. This platform will serve as a base for further large scale epidemiological and transportation simulation studies. However, the first approach to a simple, country-wide transportation model is also presented here. The application scope of the platform extends beyond the simulations of epidemic or transportation, and pertains to any situation where there are no easily available means, other than computer simulations, to conduct large scale investigations of complex population dynamics.

Social Influence and Decision-Making: Evaluating Agent Networks in Village Responses to Change in Freshwater

Mark Altaweel, Lilian N. Alessa and Andrew D. Kliskey
Journal of Artificial Societies and Social Simulation 13 (1) 15

Kyeywords: Agent-Based Modeling, Artificial Neural Network, Social Network, Social Influence, Resilience, Freshwater
Abstract: This paper presents a model, using concepts from artificial neural networks, that explains how small rural communities make decisions that affect access to potable freshwater. Field observations indicate that social relationships as well as individual goals and perceptions of decision makers have a strong influence on decisions that are made by community councils. Our work identifies three types of agents, which we designate as alpha, beta, and gamma agents. We address how gamma agents affect decisions made by community councils in passing resolutions that benefit a village\'s collective access to clean freshwater. The model, which we call the Agent Types Model (ATM), demonstrates the effects of social interactions, corporate influence, and agent-specific factors that determine choices for agents. Data from two different villages in rural Alaska and several parameter sensitivity tests are applied to the model. Results demonstrate that minimizing the social significance and agent-specific factors affecting gamma agents\' negative compliance increases the likelihood that communities adopt measures promoting potable freshwater access. The significance of this work demonstrates which types of communities are potentially more socially vulnerable or resilient to social-ecological change affecting water supplies.

Ontology, a Mediator for Agent-Based Modeling in Social Science

Pierre Livet, Jean-Pierre Muller, Denis Phan and Lena Sanders
Journal of Artificial Societies and Social Simulation 13 (1) 3

Kyeywords: Ontology, Agent-Based Computational Economic, Agent-Based Model of Simulation, Model Design, Model Building, Knowledge Framework, Spatial Simulation, Social Simulation, Ontological Test
Abstract: Agent-Based Models are useful to describe and understand social, economic and spatial systems\' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist\'s hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model design from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the \'ontological test\' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the \'knowledge framework\' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed.

Explaining Simulations Through Self Explaining Agents

Maaike Harbers, John-Jules Meyer and Karel van den Bosch
Journal of Artificial Societies and Social Simulation 13 (1) 4

Kyeywords: Explanation, Agents, Goal-Based Behavior, Virtual Training
Abstract: Several strategies are used to explain emergent interaction patterns in agent-based simulations. A distinction can be made between simulations in which the agents just behave in a reactive way, and simulations involving agents with also pro-active (goal-directed) behavior. Pro-active behavior is more variable and harder to predict than reactive behavior, and therefore it might be harder to explain. However, the approach presented in this paper tries to make advantage of the agents' pro-activeness by using it to explain their behavior. The aggregation of the agents' explanations form a basis for explaining the simulation as a whole. In this paper, an agent model that is able to generate (pro-active) behavior and explanations about that behavior is introduced, and the implementation of the model is discussed. Examples show how the link between behavior generation and explanation in the model can contribute to the explanation of a simulation.

Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting

Denis Phan and Franck Varenne
Journal of Artificial Societies and Social Simulation 13 (1) 5

Kyeywords: Agent-Based Models and Simulations, Epistemology, Economics, Social Sciences, Conceptual Exploration, Model World, Credible World, Experiment, Denotational Hierarchy
Abstract: Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.

What Do Agent-Based and Equation-Based Modelling Tell Us About Social Conventions: The Clash Between ABM and EBM in a Congestion Game Framework

Federico Cecconi, Marco Campenni, Giulia Andrighetto and Rosaria Conte
Journal of Artificial Societies and Social Simulation 13 (1) 6

Kyeywords: Agent-Based Modelling, Equation-Based Modelling, Congestion Game, Model of Social Phenomena
Abstract: In this work simulation-based and analytical results on the emergence steady states in traffic-like interactions are presented and discussed. The objective of the paper is twofold: i) investigating the role of social conventions in coordination problem situations, and more specifically in congestion games; ii) comparing simulation-based and analytical results to figure out what these methodologies can tell us on the subject matter. Our main issue is that Agent-Based Modelling (ABM) and the Equation-Based Modelling (EBM) are not alternative, but in some circumstances complementary, and suggest some features distinguishing these two ways of modeling that go beyond the practical considerations provided by Parunak H.V.D., Robert Savit and Rick L. Riolo. Our model is based on the interaction of strategies of heterogeneous agents who have to cross a junction. In each junction there are only four inputs, each of which is passable only in the direction of the intersection and can be occupied only by an agent one at a time. The results generated by ABM simulations provide structured data for developing the analytical model through which generalizing the simulation results and make predictions. ABM simulations are artifacts that generate empirical data on the basis of the variables, properties, local rules and critical factors the modeler decides to implement into the model; in this way simulations allow generating controlled data, useful to test the theory and reduce the complexity, while EBM allows to close them, making thus possible to falsify them.

A Methodology for Complex Social Simulations

Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 13 (1) 7

Kyeywords: Agent-Based Modeling Methodology, M2M, Social Simulation, Computational Social Science, Social Complexity, Inner Asia
Abstract: Social simulation - an emerging field of computational social science - has progressed from simple toy models to increasingly realistic models of complex social systems, such as agent-based models where heterogeneous agents interact with changing natural or artificial environments. These larger, multidisciplinary projects require a scientific research methodology distinct from, say, simpler social simulations with more limited scope, intentionally minimal complexity, and typically under a single investigator. This paper proposes a methodology for complex social simulations - particularly inter- and multi-disciplinary socio-natural systems with multi-level architecture - based on a succession of models akin to but distinct from the late Imre Lakatos' notion of a 'research programme'. The proposed methodology is illustrated through examples from the Mason-Smithsonian project on agent-based models of the rise and fall of polities in Inner Asia. While the proposed methodology requires further development, so far it has proven valuable for advancing the scientific objectives of the project and avoiding some pitfalls.

Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

Gary Polhill, Lee-Ann Sutherland and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 13 (2) 10

Kyeywords: Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research
Abstract: This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.

An Agent Based Market Design Methodology for Combinatorial Auctions

Jinho Choi, Gyoo Gun Lim and Kun Chang Lee
Journal of Artificial Societies and Social Simulation 13 (2) 2

Kyeywords: Combinatorial Auction, Market Design Methodology, Market Architecture Design, Auction Rule Design, Winner Determination Design, Agent-Based System
Abstract: Auction mechanisms have attracted a great deal of interest and have been used in diverse e-marketplaces. In particular, combinatorial auctions have the potential to play an important role in electronic transactions. Therefore, diverse combinatorial auction market types have been proposed to satisfy market needs. These combinatorial auction types have diverse market characteristics, which require an effective market design approach. This study proposes a comprehensive and systematic market design methodology for combinatorial auctions based on three phases: market architecture design, auction rule design, and winner determination design. A market architecture design is for designing market architecture types by Backward Chain Reasoning. Auction rules design is to design transaction rules for auctions. The specific auction process type is identified by the Backward Chain Reasoning process. Winner determination design is about determining the decision model for selecting optimal bids and auctioneers. Optimization models are identified by Forward Chain Reasoning. Also, we propose an agent based combinatorial auction market design system using Backward and Forward Chain Reasoning. Then we illustrate a design process for the general n-bilateral combinatorial auction market. This study serves as a guideline for practical implementation of combinatorial auction markets design.

Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems

Sungho Lee
Journal of Artificial Societies and Social Simulation 13 (2) 3

Kyeywords: Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling
Abstract: Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.

Simulating Political Stability and Change in the Netherlands (1998-2002): an Agent-Based Model of Party Competition with Media Effects Empirically Tested

Jasper Muis
Journal of Artificial Societies and Social Simulation 13 (2) 4

Kyeywords: Agent-Based Model, Voting Behaviour, Mass Media, Empirical Validation
Abstract: Agent-based models of political party competition in a multidimensional policy space have been developed in order to reflect adaptive learning by party leaders with very limited information feedback. The key assumption is that two categories of actors continually make decisions: voters choose which party to support and party leaders offer citizens a certain policy package. After reviewing the arguments for using agent-based models, I elaborate two ways forward in the development of these models for political party competition. Firstly, theoretical progress is made in this article by taking the role of the mass media into account. In previous work it is implicitly assumed that all parties are equally visible for citizens, whereas I will start from the more realistic assumption that there is also competition for attention in the public sphere. With this addition, it is possible to address the question why new parties are seldom able to successfully compete with political actors already within the political system. Secondly, I argue that, if we really want to learn useful lessons from simulations, we should seek to empirically falsify models by confronting outcomes with real data. So far, most of the agent-based models of party competition have been an exclusively theoretical exercise. Therefore, I evaluate the empirical relevance of different simulations of Dutch party competition in the period from May 1998 until May 2002. Using independent data on party positions, I measure the extent to which simulations generate mean party sizes that resemble public opinion polls. The results demonstrate that it is feasible and realistic to simulate party competition in the Netherlands with agent-based models, even when a rather unstable period is investigated.

Social Simulation and Analysis of the Dynamics of Criminal Hot Spots

Tibor Bosse and Charlotte Gerritsen
Journal of Artificial Societies and Social Simulation 13 (2) 5

Kyeywords: Agent-Based Modelling, Criminal Hot Spots, Displacement, Reputation, Social Simulation, Analysis
Abstract: Within the field of Criminology, the spatio-temporal dynamics of crime are an important subject of study. In this area, typical questions are how the behaviour of offenders, targets, and guardians can be explained and predicted, as well as the emergence and displacement of criminal hot spots. In this article we present a combination of software tools that can be used as an experimental environment to address such questions. In particular, these tools comprise an agent-based simulation model, a verification tool, and a visualisation tool. The agent-based simulation model specifically focuses on the interplay between hot spots and reputation. Using this environment, a large number of simulation runs have been performed, of which results have been formally analysed. Based on these results, we argue that the presented environment offers a valuable approach to analyse the dynamics of criminal hot spots.

Socio-Economic Mechanisms to Coordinate the Internet of Services: The Simulation Environment SimIS

Stefan König, Sebastian Hudert and Torsten Eymann
Journal of Artificial Societies and Social Simulation 13 (2) 6

Kyeywords: Multi-Agent Simulation, Internet, Simulation Tools
Abstract: Visions of 21st century information systems show highly specialized digital services and resources, which interact continuously and with a global reach. Especially with the emergence of technologies, such as the semantic web or software agents, intelligent services within these settings can be implemented, automatically communicating and negotiating over the Internet about digital resources without human intervention. Such environments will eventually realize the vision of an open and global Internet of Services (IoS). In this paper we present an agent-based simulation model and toolkit for the IoS: 'SimIS - Simulating an Internet of Services'. Employing SimIS, distributed management mechanisms and protocols can be investigated in a simulated IoS environment before their actual deployment.

Social Preference, Incomplete Information, and the Evolution of Ultimatum Game in the Small World Networks: An Agent-Based Approach

Bo Xianyu
Journal of Artificial Societies and Social Simulation 13 (2) 7

Kyeywords: Spatial Ultimatum Game, Complex Network, Social Preference, Agent Based Modeling
Abstract: Certain social preference models have been proposed to explain fairness behavior in experimental games. Existing bodies of research on evolutionary games, however, explain the evolution of fairness merely through the self-interest agents. This paper attempts to analyze the ultimatum game's evolution on complex networks when a number of agents display social preference. Agents' social preference is modeled in three forms: fairness consideration or maintaining a minimum acceptable money level, inequality aversion, and social welfare preference. Different from other spatial ultimatum game models, the model in this study assumes that agents have incomplete information on other agents' strategies, so the agents need to learn and develop their own strategies in this unknown environment. Genetic Algorithm Learning Classifier System algorithm is employed to address the agents' learning issue. Simulation results reveal that raising the minimum acceptable level or including fairness consideration in a game does not always promote fairness level in ultimatum games in a complex network. If the minimum acceptable money level is high and not all agents possess a social preference, the fairness level attained may be considerably lower. However, the inequality aversion social preference has negligible effect on the results of evolutionary ultimatum games in a complex network. Social welfare preference promotes the fairness level in the ultimatum game. This paper demonstrates that agents' social preference is an important factor in the spatial ultimatum game, and different social preferences create different effects on fairness emergence in the spatial ultimatum game.

Flocking Behaviour: Agent-Based Simulation and Hierarchical Leadership

Vicenç Quera, Francesc S. Beltran and Ruth Dolado
Journal of Artificial Societies and Social Simulation 13 (2) 8

Kyeywords: Flocking Behaviour; Hierarchical Leadership; Agent-Based Simulation; Social Dynamics
Abstract: We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.

Between Replication and Docking: "Adaptive Agents, Political Institutions, and Civic Traditions" Revisited

Dan Miodownik, Britt Cartrite and Ravi Bhavnani
Journal of Artificial Societies and Social Simulation 13 (3) 1

Kyeywords: Replication, Docking, Agent-Based Model, Italy, Social Capital
Abstract: This article has two primary objectives: (i) to replicate an agent-based model of social interaction by Bhavnani (2003), in which the author explicitly specifies mechanisms underpinning Robert Putnam\'s (1993) work on Civic Traditions in Modern Italy, bridging the gap between the study\'s historical starting point—political regimes that characterized 14th Century Italy—and contemporary levels of social capital—reflected in a \'civic\' North and an \'un-civic\' South; and (ii) to extend the original analysis, using a landscape of Italy that accounts for population density. The replication exercise is performed by different authors using an entirely distinct ABM toolkit (PS-I) with its own rule set governing agent-interaction and cultural change. The extension, which more closely approximates a docking exercise, utilizes equal area cartograms otherwise known as density-equalizing maps (Gastner and Newman 2004) to resize the territory according to 1993 population estimates. Our results indicate that: (i) using the criterion of distributional equivalence, we experience mixed success in replicating the original model given our inability to restrict the selection of partners to \'eligible\' neighbors and limit the number of agent interactions in a timestep; (ii) increasing the number of agents and introducing more realistic population distributions in our extension of the replication model increases distributional equivalence; (iii) using the weaker criteria of relational alignment, both the replication model and its extension capture the basic relationship between institutional effectiveness and civic change, the effect of open boundaries, historical shocks, and path dependence; and (iv) that replication and docking may be usefully combined in model-to-model analysis, with an eye towards verification, reimplementation, and alignment.

Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments

Adam Zagorecki, Kilkon Ko and Louise K. Comfort
Journal of Artificial Societies and Social Simulation 13 (3) 3

Kyeywords: Agent-Based Simulation, Emergency Management, Network Evolution, Performance
Abstract: Achieving efficiency in coordinated action in rapidly changing environments has challenged both researchers and practitioners. Emergency events require both rapid response and effective coordination among participating organizations. We created a simulated operations environment using agent-based modeling to test the efficiency of six different organizational designs that varied the exercise of authority, degree of uncertainty, and access to information. Efficiency is measured in terms of response time, identifying time as the most valuable resource in emergency response. Our findings show that, contrary to dominant organizational patterns of hierarchical authority that limit communication among members via strict reporting rules, any communication among members increases the efficiency of organizations operating in uncertain environments. We further found that a smaller component of highly interconnected, self adapting agents emerges over time to support the organization\'s adaptation in changing conditions. In uncertain environments, heterogeneous agents prove more efficient in sharing information that guides coordination than homogeneous agents.

Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model

Riccardo Boero, Giangiacomo Bravo, Marco Castellani and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 13 (3) 6

Kyeywords: Reputation, Trustworthiness, Laboratory Experiment, Agent-Based Model, Exploration Vs. Exploitation
Abstract: This paper investigates the relevance of reputation to improve the explorative capabilities of agents in uncertain environments. We have presented a laboratory experiment where sixty-four subjects were asked to take iterated economic investment decisions. An agent-based model based on their behavioural patterns replicated the experiment exactly. Exploring this experimentally grounded model, we studied the effects of various reputational mechanisms on explorative capabilities at a systemic level. The results showed that reputation mechanisms increase the agents\' capability for coping with uncertain environments more than individualistic atomistic exploration strategies, although the former does entail a certain amount of false information inside the system.

The Third Way of Agent-Based Social Simulation and a Computational Account of Emergence

Roy Wilson
Journal of Artificial Societies and Social Simulation 13 (3) 8

Kyeywords: Agent-Based Social Simulation, Weak Emergence, Social Networks, Kolmogorov Complexity, Upward Causation, Downward Causation
Abstract: This paper interprets a particular agent-based social simulation (ABSS) in terms of the third way of understanding agent-based simulation proposed by Conte. It is proposed that the normalized compression distance (derived from estimates of Kolmogorov complexity) between the initial and final macrolevel states of the ABSS provides a quantitative measure of the degree to which the results obtained via the ABSS might be obtained via a closed-form expression. If the final macrolevel state of an ABSS can only be obtained by simulation, this confers on agent-based social simulations a special status. Future empirical (computational) work and epistemological analyses are proposed.

Prospects and Pitfalls of Statistical Testing: Insights from Replicating the Demographic Prisoner's Dilemma

Wolfgang Radax and Bernhard Rengs
Journal of Artificial Societies and Social Simulation 13 (4) 1

Kyeywords: Agent-Based Model, Verification, Comparative Computational Methodology, Prisoners Dilemma, Replication, Demographic Prisoners Dilemma
Abstract: This paper documents our efforts (and troubles) in replicating Epstein's (1998) demographic prisoner's dilemma model. Confronted with a number of ambiguous descriptions of model features we introduce a method for systematically generating a large number of model replications and testing for their equivalence to the original model. While, qualitatively speaking, a number of our replicated models resemble the results of the original model reasonably well, statistical testing reveals that in quantitative terms our endeavor was only partially successful. This fact hints towards some unstated assumptions regarding the original model. Finally we conduct a number of statistical tests with respect to the influence of certain design choices like the method of updating, the timing of events and the randomization of the activation order. The results of these tests highlight the importance of an explicit documentation of design choices and especially of the timing of events. A central lesson learned from this exercise is that the power of statistical replication analysis is to a large degree determined by the available data.

Obligation Norm Identification in Agent Societies

Tony Bastin Roy Savarimuthu, Stephen Cranefield, Maryam A. Purvis and Martin K. Purvis
Journal of Artificial Societies and Social Simulation 13 (4) 3

Kyeywords: Norms, Social Norms, Obligations, Norm Identification, Agent-Based Simulation, Simulation of Norms, Artificial Societies, Normative Multi-Agent Systems (NorMAS)
Abstract: Most works on norms have investigated how norms are regulated using institutional mechanisms. Very few works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. This paper describes a mechanism for identifying one type of norm, an obligation norm. The Obligation Norm Inference (ONI) algorithm described in this paper makes use of an association rule mining approach to identify obligation norms. Using agent based simulation of a virtual restaurant we demonstrate how an agent can identify the tipping norm. The experiments that we have conducted demonstrate that an agent in the system is able to add, remove and modify norms dynamically. An agent can also flexibly modify the parameters of the system based on whether it is successful in identifying a norm.

Dilbert-Peter Model of Organization Effectiveness: Computer Simulations

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 13 (4) 4

Kyeywords: Organization Productivity, Peter Principle, Agent Based Modeling
Abstract: We describe a computer model of general effectiveness of a hierarchical organization depending on two main aspects: effects of promotion to managerial levels and efforts to self-promote of individual employees, reducing their actual productivity. The combination of judgment by appearance in the promotion to higher levels of hierarchy and the Peter Principle (which states that people are promoted to their level of incompetence) results in fast declines in effectiveness of the organization. The model uses a few synthetic parameters aimed at reproduction of realistic conditions in typical multilayer organizations. It is shown that improving organization resiliency to self-promotion and continuity of individual productiveness after a promotion can greatly improve the overall organization effectiveness.

Opinion Formation by Informed Agents

Mohammad Afshar and Masoud Asadpour
Journal of Artificial Societies and Social Simulation 13 (4) 5

Kyeywords: Social Networks, Informed Agents, Innovation Diffusion, Bounded Confidence, Opinion Dynamics, Opinion Formation
Abstract: Opinion formation and innovation diffusion have gained lots of attention in the last decade due to its application in social and political science. Control of the diffusion process usually takes place using the most influential people in the society, called opinion leaders or key players. But the opinion leaders can hardly be accessed or hired for spreading the desired opinion or information. This is where informed agents can play a key role. Informed agents are common people, not distinguishable from the other members of the society that act in coordination. In this paper we show that informed agents are able to gradually shift the public opinion toward a desired goal through microscopic interactions. In order to do so they pretend to have an opinion similar to others, but while interacting with them, gradually and intentionally change their opinion toward the desired direction. In this paper a computational model for opinion formation by the informed agents based on the bounded confidence model is proposed. The effects of different parameter settings including population size of the informed agents, their characteristics, and network structure, are investigated. The results show that social and open-minded informed agents are more efficient than selfish or closed-minded agents in control of the public opinion.

Modelling Contextualized Reasoning in Complex Societies with "Endorsements"

Shah Jamal Alam, Armando Geller, Ruth Meyer and Bogdan Werth
Journal of Artificial Societies and Social Simulation 13 (4) 6

Kyeywords: Cognition, Contextualized Reasoning, Evidence-Driven Agent-Based Social Simulation, Empirical Agent-Based Social Simulation, Rich Cognitive Modelling, Tzintzuntzan
Abstract: In many computational social simulation models only cursory reference to the foundations of the agent cognition used is made and computational expenses let many modellers chose simplistic agent cognition architectures. Both choices run counter to expectations framed by scholars active in the domain of rich cognitive modelling that see agent reasoning as socially inherently contextualized. The Manchester school of social simulation proposed a particular kind of a socially contextualized reasoning mechanism, so called endorsements, to implement the cognitive processes underlying agent action selection that eventually causes agent interaction. Its usefulness lies in its lightweight architecture and in taking into account folk psychological conceptions of how reasoning works. These and other advantages make endorsements an amenable tool in everyday social simulation modelling. A yet outstanding comprehensive introduction to the concept of endorsements is provided and its theoretical basis is extended and extant research is critically reviewed. Improvements to endorsements regarding memory and perception are suggested and tested against a case-study.

Agent-Based Modelling: The Next 15 Years

Lynne Hamill
Journal of Artificial Societies and Social Simulation 13 (4) 7

Kyeywords: Agent-Based Modelling,, NetLogo, Policy Advice
Abstract: This short note makes recommendations for the future direction of research in agent-based modelling (ABM). It is a personal view based on my experience as a policy adviser who has recently come to ABM. I suggest that to promote the use of ABM, the ABM community needs demonstrate the value of modelling to other social scientists by showing-by-doing and offering training projects; and to produce tools, guidance on good-practice and basic building blocks. Then the policy contexts most likely to benefit from ABM need to be identified along with any new data requirements, so that the usefulness of ABM can be demonstrated to policy analysts. This is, in my view, the challenge facing the ABM community for the next 15 years.

Diffusion of Competing Innovations: The Effects of Network Structure on the Provision of Healthcare

Adam G. Dunn and Blanca Gallego
Journal of Artificial Societies and Social Simulation 13 (4) 8

Kyeywords: Innovation Diffusion, Scale-Free Networks, Health Policy, Agent-Based Modelling
Abstract: Medical innovations, in the form of new medication or other clinical practices, evolve and spread through health care systems, impacting on the quality and standards of health care provision, which is demonstrably heterogeneous by geography. Our aim is to investigate the potential for the diffusion of innovation to influence health inequality and overall levels of recommended care. We extend existing diffusion of innovation models to produce agent-based simulations that mimic population-wide adoption of new practices by doctors within a network of influence. Using a computational model of network construction in lieu of empirical data about a network, we simulate the diffusion of competing innovations as they enter and proliferate through a state system comprising 24 geo-political regions, 216 facilities and over 77,000 individuals. Results show that stronger clustering within hospitals or geo-political regions is associated with slower adoption amongst smaller and rural facilities. Results of repeated simulation show how the nature of uptake and competition can contribute to low average levels of recommended care within a system that relies on diffusive adoption. We conclude that an increased disparity in adoption rates is associated with high levels of clustering in the network, and the social phenomena of competitive diffusion of innovation potentially contributes to low levels of recommended care.

ODD Updated

Gary Polhill
Journal of Artificial Societies and Social Simulation 13 (4) 9

Kyeywords: ODD, Individual Based Models, Agent Based Models, Replication, Documentation
Abstract: An update to Volker Grimm and colleagues\' Overview, Design concepts and Details (ODD) protocol for documenting individual and agent based models (I/ABM) has recently been published in Ecological Modelling. This renames the \'State variables and scales\' element to \'Entities, state variables and scales\', and the \'Input\' element to \'Input data\', introduces two new Design concepts (\'Basic principles\' and \'Learning\'), and renames another (\'Fitness\' is now generalised to \'Objectives\'). The Design concepts element can now also be shortened such that it is not required to include any design concept that is irrelevant to the model, and expanded to include new design concepts more appropriate to the model being described. Other clarifications of intentions in the original protocol have been made.

Introducing the SAPS System and a Corresponding Allocation Mechanism for Synchronous Online Reciprocal Peer Support Activities

Gijs de Bakker, Jan van Bruggen, Wim Jochems and Peter B. Sloep
Journal of Artificial Societies and Social Simulation 14 (1) 1

Kyeywords: Peer Support, Peer Allocation, Computational Simulations, System Dynamics, Distance Learning
Abstract: While student populations in higher education are becoming more heterogeneous, recently several attempts have been made to introduce online peer support to decrease the tutor load of teachers. We propose a system that facilitates synchronous online reciprocal peer support activities for ad hoc student questions: the Synchronous Allocated Peer Support (SAPS) system. Via this system, students with questions during their learning are allocated to competent fellow-students for answering. The system is designed for reciprocal peer support activities among a group of students who are working on the same fixed modular material every student has to finish, such as courses with separate chapters. As part of a requirement analysis of online reciprocal peer support to succeed, this chapter is focused on the second requirement of peer competence and sustainability of our system. Therefore a study was conducted with a simulation of a SAPS-based allocation mechanism in the NetLogo simulation environment and focuses on the required minimum population size, the effect of the addition of extra allocation parameters or disabling others on the mechanism\'s effectiveness, and peer tutor load spread in various conditions and its influence on the mechanism\'s effectiveness. The simulation shows that our allocation mechanism should be able to facilitate online peer support activities among groups of students. The allocation mechanism holds over time and a sufficient number of students are willing and competent to answer fellow-students\' questions. Also, fine-tuning the parameters (e.g. extra selection criteria) of the allocation mechanism further enhances its effectiveness.

A First Approach on Modelling Staff Proactiveness in Retail Simulation Models

Peer-Olaf Siebers and Uwe Aickelin
Journal of Artificial Societies and Social Simulation 14 (2) 2

Kyeywords: Retail Performance, Management Practices, Proactive Behaviour, Service Experience, Agent-Based Modelling, Simulation
Abstract: There has been a noticeable shift in the relative composition of the industry in the developed countries in recent years; manufacturing is decreasing while the service sector is becoming more important. However, currently most simulation models for investigating service systems are still built in the same way as manufacturing simulation models, using a process-oriented world view, i.e. they model the flow of passive entities through a system. These kinds of models allow studying aspects of operational management but are not well suited for studying the dynamics that appear in service systems due to human behaviour. For these kinds of studies we require tools that allow modelling the system and entities using an object-oriented world view, where intelligent objects serve as abstract \'actors\' that are goal directed and can behave proactively. In our work we combine process-oriented discrete event simulation modelling and object-oriented agent based simulation modelling to investigate the impact of people management practices on retail productivity. In this paper, we reveal in a series of experiments what impact considering proactivity can have on the output accuracy of simulation models of human centric systems. The model and data we use for this investigation are based on a case study in a UK department store. We show that considering proactivity positively influences the validity of these kinds of models and therefore allows analysts to make better recommendations regarding strategies to apply people management practices.

An Agent Operationalization Approach for Context Specific Agent-Based Modeling

Christof Knoeri, Claudia R. Binder and Hans-Joerg Althaus
Journal of Artificial Societies and Social Simulation 14 (2) 4

Kyeywords: Agent Operationalization, Decision-Making, Analytical Hierarchy Process (AHP), Agent-Based Modeling, Conceptual Validation
Abstract: The potential of agent-based modeling (ABM) has been demonstrated in various research fields. However, three major concerns limit the full exploitation of ABM; (i) agents are too simple and behave unrealistically without any empirical basis, (ii) \'proof of concept\' applications are too theoretical and (iii) too much value placed on operational validity instead of conceptual validity. This paper presents an operationalization approach to determine the key system agents, their interaction, decision-making and behavior for context specific ABM, thus addressing the above-mentioned shortcomings. The approach is embedded in the framework of Giddens\' structuration theory and the structural agent analysis (SAA). The agents\' individual decision-making (i.e. reflected decisions) is operationalized by adapting the analytical hierarchy process (AHP). The approach is supported by empirical system knowledge, allowing us to test empirically the presumed decision-making and behavioral assumptions. The output is an array of sample agents with realistic (i.e. empirically quantified) decision-making and behavior. Results from a Swiss mineral construction material case study illustrate the information which can be derived by applying the proposed approach and demonstrate its practicability for context specific agent-based model development.

The ABM Template Models: A Reformulation with Reference Implementations

Alan G. Isaac
Journal of Artificial Societies and Social Simulation 14 (2) 5

Kyeywords: Template Models, Reference Implementations, Spatially-Situated Agents, Spatially Distributed Resources
Abstract: We refine a prominent set of template models for agent-based modeling, and we offer new reference implementations. We also address some issues of design, flexibility, and ease of use that are relevant to the choice of an agent-based modeling platform.

The Current State of Normative Agent-Based Systems

Christopher D. Hollander and Annie S. Wu
Journal of Artificial Societies and Social Simulation 14 (2) 6

Kyeywords: Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling
Abstract: Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.

Knowledge Diffusion and Innovation: Modelling Complex Entrepreneurial Behaviours by Piergiuseppe Morone and Richard Taylor: A Response to the Review

Piergiuseppe Morone and Richard Taylor
Journal of Artificial Societies and Social Simulation 14 (2) 7

Kyeywords: Knowledge Diffusion, Innovation, Agent-Based Model, Validation
Abstract: In this brief note we reply to César García-Díaz and Diemo Urbig who reviewed our book on Knowledge Diffusion and Innovation (Edward Elgar Publishing: Cheltenham, 2010). We take this opportunity to reaffirm our personal view on several relevant issues, such as the need for a holistic view in economics, the adoption of a pragmatic heuristic approach when dealing with complex socio-economic systems, the relevance of a \'prototype model\' to setting a rigorous conceptual framework and the proposition of a novel way of looking at knowledge and innovation.

Multiagent System Applied to the Modeling and Simulation of Pedestrian Traffic in Counterflow

Ana Luisa Ballinas-Hernández, Angélica Muñoz-Meléndez and Alejandro Rangel-Huerta
Journal of Artificial Societies and Social Simulation 14 (3) 2

Kyeywords: Agent-Based Modeling, Pedestrian Crowd, Activity Measurement
Abstract: An agent-based model to simulate a pedestrian crowd in a corridor is presented. Pedestrian crowd models are valuable tools to gain insight into the behavior of human crowds in both, everyday and crisis situations. The main contribution of this work is the definition of a pedestrian crowd model by applying ideas from the field of the kinetic theory of living systems on the one hand, and ideas from the field of computational agents on the other hand. Such combination supported a quantitative characterization of the performance of our agents, a neglected issue in agent-based models, through well-known kinetic parameters. Fundamental diagrams of flow and activity are presented for both, groups of homogeneous pedestrians, and groups of heterogeneous pedestrians in terms of their willingness to reach their goals.

Scale-Free Relationships Facilitate Cooperation in Spatial Games with Sequential Strategy

Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 14 (3) 3

Kyeywords: Cooperation, Second-Best Decision, Multi-Agent Simulation, Spatial Game, Collusive Tendering
Abstract: Recently, the area of study of spatial game continuously has extended, and researchers have especially presented a lot of works of coevolutionary mechanism. We have recognized coevolutionary mechanism as one of the factors for the promotion of cooperation like five rules by Nowak. However, those studies still deal with the optimal response (best decision). The best decision is persuasive in most cases, but does not apply to all situations in the real world. Contemplating that question, researchers have presented some works discussing not only the best decision but also the second-best decision. Those studies compare the results between the best and the second-best, and also state the applicability of the second-best decision. This study, considering that trend, has extended the match between two groups to spatial game with the second-best decision. This extended model expresses relationships of groups as a spatial network, and every group matches other groups of relationships. Then, we examine how mutual cooperation changes in each case where either we add probabilistic perturbation to relationships or ties form various types of the structure. As a result, unlike most results utilizing the best decision, probabilistic perturbation does not induce any change. On the other hand, when ties are the scale-free structure, mutual cooperation is enhanced like the case of the best decision. When we probe the evolution of strategies in that case, groups with many ties play a role for leading the direction of decision as a whole. This role appears without explicit assignment. In the discussion, we also state that the presented model has an analogy to the real situation, collusive tendering.

An Agent-Based Model of Sustainable Corporate Social Responsibility Activities

Isamu Okada
Journal of Artificial Societies and Social Simulation 14 (3) 4

Kyeywords: Corporate Social Responsibility, Agent-Based Simulation, Sustainability, Multiple Sector Model, Micro Economy
Abstract: An agent-based model of firms and their stakeholders' economic actions was used to test the theoretical feasibility of sustainable corporate social responsibility activities. Corporate social responsibility has become important to many firms, but CSR activities tend to get less attention during busts than during boom times. The hypothesis tested is that the CSR activities of a firm are more economically rational if the economic actions of its stakeholders reflect the firm's level of CSR. Our model focuses on three types of stakeholders: workers, consumers, and shareholders. First, we construct a uniform framework based on a microeconomic foundation that includes these stakeholders and the corresponding firms. Then, we formulate parameters for CSR in this framework. Our aim is to identify the conditions under which every type of stakeholder derives benefits from a firm's CSR activities. We simulated our model with heterogeneous agents by computer using several scenarios. For each one, the simulation was run 100 times with different random seeds. We first simulated the homogeneous version discussed above to verify the concept of our model. Next, we simulated the case in which workers had heterogeneous abilities, the firms had cost for CSR activities, and the workers, consumers, and shareholders had zero CSR awareness. We tested the robustness of our simulation results by using sensitivity analysis. Specifically, we investigated the conditions for the pecuniary advantage of CSR activities and effects offsetting benefits of CSR activities. Finally, we developed a new model installed bounded rational and simulated. The results show that the economic actions of stakeholders during boom periods greatly affect the sustainability of CSR activities during slow periods. This insight should lead to a feasible and effective prescription for sustainable CSR activities.

Reallocation Problems in Agent Societies: A Local Mechanism to Maximize Social Welfare

Antoine Nongaillard and Philippe Mathieu
Journal of Artificial Societies and Social Simulation 14 (3) 5

Kyeywords: Resource Allocation, Negotiation, Social Welfare, Agent Society, Behavior, Emergence
Abstract: Resource reallocation problems are common in real life and therefore gain an increasing interest in Computer Science and Economics. Such problems consider agents living in a society and negotiating their resources with each other in order to improve the welfare of the population. In many studies however, the unrealistic context considered, where agents have a flawless knowledge and unlimited interaction abilities, impedes the application of these techniques in real life problematics. In this paper, we study how agents should behave in order to maximize the welfare of the society. We propose a multi-agent method based on autonomous agents endowed with a local knowledge and local interactions. Our approach features a more realistic environment based on social networks, inside which we provide the behavior for the agents and the negotiation settings required for them to lead the negotiation processes towards socially optimal allocations. We prove that bilateral transactions of restricted cardinality are sufficient in practice to converge towards an optimal solution for different social objectives. An experimental study supports our claims and highlights the impact of a realistic environment on the efficiency of the techniques utilized.

Stability of Groups with Costly Beliefs and Practices

Wesley J. Wildman and Richard Sosis
Journal of Artificial Societies and Social Simulation 14 (3) 6

Kyeywords: Costly Signaling, Credibility Enhancing Displays, Cultural Transmission, Religion, Charismatic Leader, Agent-Based Model
Abstract: Costly signaling theory has been employed to explain the persistence of costly displays in a wide array of species, including humans. Henrich (2009) builds on earlier signaling models to develop a cultural evolutionary model of costly displays. Significantly, Henrich's model shows that there can be a stable equilibrium for an entire population committed to costly displays, persisting alongside a no-cost stable equilibrium for the entire population. Here we generalize Henrich's result to the more realistic situation of a population peppered with subgroups committed to high-cost beliefs and practices. The investigative tool is an agent-based model in which agents have cognitive capacities similar to those presupposed in Henrich's population-level cultural evolutionary model, and agents perform similar fitness calculations. Unlike in Henrich's model, which has no group differentiation within the population, our model agents use fitness calculations to determine whether to join or leave high-cost groups. According to our model, high-cost groups achieve long-term stability within a larger population under a wide range of circumstances, a finding that extends Henrich's result in a more realistic direction. The most important emergent pathway to costly group stability is the simultaneous presence of high charisma and consistency of the group leader and high cost of the group. These findings have strategic implications both for leading groups committed to costly beliefs and practices and for controlling their size and influence within wider cultural settings.

Leadership, Violence, and Warfare in Small Societies

Stephen Younger
Journal of Artificial Societies and Social Simulation 14 (3) 8

Kyeywords: Multi-Agent Simulation, Leadership, Violence, Warfare, Pacific Island Societies
Abstract: Multi-agent simulation was used to study the effect of simple models of leadership on interpersonal violence and warfare in small societies. Agents occupied a two dimensional landscape containing villages and food sources. Sharing and stealing contributed to normative reputation. Violence occurred during theft, in revenge killings, and in leader-directed warfare between groups. The simulations were run over many generations to examine the effect of violence on social development. The results indicate that leadership reduced the survival probability of the population. Interpersonal violence killed more agents than warfare when intra-group violence was permitted. More aggressive leaders did not always prevail over less aggressive leaders due to the inherent risks associated with attacks. The results of the simulation are compared to cross-cultural studies and to observations of indigenous Pacific island societies.

Two Challenges in Simulating the Social Processes of Science

Edmund Chattoe-Brown
Journal of Artificial Societies and Social Simulation 14 (4) 1

Kyeywords: Simulating Science, Algorithmic Chemistry, Evolutionary Algorithms, Data Structures, Learning Systems
Abstract: This note discusses two challenges to simulating the social process of science. The first is developing an adequately rich representation of the underlying Data Generation Process which scientific progress can \"learn\". The second is how to get effective data on what, in broad terms, the properties of the \"future\" are. Paradoxically, with due care, we may learn a lot about the future by studying the past.

Science as a Social System and Virtual Research Environment

Sergey Parinov and Cameron Neylon
Journal of Artificial Societies and Social Simulation 14 (4) 10

Kyeywords: Virtual Research Environment, Science System Social Sustainability, Agent Based Modeling
Abstract: The accumulation of gradual changes in scientific landscape and research practice due to the Internet has the potential to enhance the quality of both cognitive and social aspects of science and scientists. New types of research outputs, modes of scientific communication and new circulation mechanisms, as well as enhanced opportunities for scientific re-use and measuring research impact, in combination with new approaches to research assessment and evaluation are all having profound effects on the social system of science. To be sure that these innovations will not break the social sustainability of the science community, it will be valuable to develop a model of science as a tool for computer simulation of social consequences from possible innovations within virtual research environment. Focusing on possible social problems related to these new virtual research environments this short paper provides a brief analysis of the current situation in science (challenges, problems, main actors), general views on model of science (landscape, main agents, important properties, etc.) and on areas where simulation can contribute to better understanding of possible futures for the scientific community.

Using Social Simulation to Explore the Dynamics at Stake in Participatory Research

Olivier Barreteau and Christophe Le Page
Journal of Artificial Societies and Social Simulation 14 (4) 12

Kyeywords: Participatory Research, Institutional Analysis and Design, Knowledge Flow, Agent Based Simulation
Abstract: This position paper contributes to the debate on perspectives for simulating the social processes of science through the specific angle of participatory research. This new way of producing science is still in its infancy and needs some step back and analysis, to understand what is taking place on the boundaries between academic, policy and lay worlds. We argue that social simulation of this practice of cooperation can help in understanding further this new way of doing science, building on existing experience in simulation of knowledge flows as well as pragmatic approaches in social sciences.

Group-Level Exploration and Exploitation: A Computer Simulation-Based Analysis

Jennifer Kunz
Journal of Artificial Societies and Social Simulation 14 (4) 18

Kyeywords: Organisational Learning, Experience-Based Learning, Exploration, Exploitation, Knowledge Management, Genetic Algorithms
Abstract: Organisational research has studied the tension between exploration and exploitation for years. In essence, this body of research agrees on the necessity of a balance between explora-tive and exploitative processes to prevent an organisation from falling into a learning trap. Thus, to enhance the active management of this balance in organisations, a deeper theoretical understanding of the factors that influence the development of exploration and exploitation has to be gained. One of the recently discussed factors is the interplay between exploration and exploitation on different organisational levels. This paper picks up this discussion. It pro-vides an in-depth, computer simulation-based analysis of the performance of organisational types with varying degrees of within-group and between-group exploration and exploitation in situations of different degrees of task complexity. The findings indicate that a high share of between-group processes as compared to within-group processes positively influences the organisational performance level and that dependent on task complexity the optimal share of exploration and exploitation varies.

A Virtual Laboratory for the Study of History and Cultural Dynamics

Juan-Luis Suárez and Fernando Sancho
Journal of Artificial Societies and Social Simulation 14 (4) 19

Kyeywords: Cultural Dynamics, Cultural Complexity, Multi-Agent Based Simulation, Netlogo, Virtual Laboratory
Abstract: This article presents a Virtual Laboratory that enables the researcher to try hypothesis and confirm data analysis about different historical processes and cultural dynamics. This Virtual Cultural Laboratory (VCL) is developed using agent-based modeling technology. Individuals' tendencies and preferences as well as the behavior of cultural objects in the transformation of cultural information are taken into consideration. In addition, the effect of local interactions at different scales over time and space is visualized through the VCL interface. Information repositories, cultural items, borders, population size, individual' tendencies and other features are determined by the user. Finally, the researcher can also isolate specific factors whose effect on the global system might be of interest to the researcher. All the code can be found at http://projects.cultureplex.ca/

Toward Multi-Level, Multi-Theoretical Model Portfolios for Scientific Enterprise Workforce Dynamics

Levent Yilmaz
Journal of Artificial Societies and Social Simulation 14 (4) 2

Kyeywords: Agent-Based Model, Complexity, Innovation, Science Studies, Diversity
Abstract: Development of theoretically sound methods and strategies for informed science and innovation policy analysis is critically important to each nation's ability to benefit from R&D investments. Gaining deeper insight into complex social processes that influence the growth and formation of scientific fields and development over time of a diverse workforce requires a systemic and holistic view. A research agenda for the development of rigorous complex adaptive systems models is examined to facilitate the study of incentives, strategies, mobility, and stability of the science-based innovation ecosystem, while examining implications for the sustainability of a diverse science enterprise.

Role-Playing Game and Learning for Young People About Sustainable Development Stakes: An Experiment in Transferring and Adapting Interdisciplinary Scientific Knowledge

Françoise Gourmelon, Mathias Rouan, Jean-François Lefevre and Anne Rognant
Journal of Artificial Societies and Social Simulation 14 (4) 21

Kyeywords: Children Education, Multi-Agent Environment, Role-Playing Game
Abstract: The study refers to the interactions between socio-economic and natural dynamics in an island biosphere reserve by using companion modelling. This approach provides scientific results and involves interdisciplinarity. In the second phase of the study, we transferred knowledge by adapting the main research output, a role-playing game, to young people. Our goal was to introduce interactions between social and ecological systems, coastal dynamics and integrated management. Adapting the game required close collaboration between the scientists and educators in order to transform both its substance and form and to run it with an easy-to-handle ergonomic platform.

Modeling Scientists as Agents. How Scientists Cope with the Challenges of the New Public Management of Science

Marc Mölders, Robin D. Fink and Johannes Weyer
Journal of Artificial Societies and Social Simulation 14 (4) 6

Kyeywords: Systems Theory, Theory of Action and Decision Making, Academic Publication System, Science System, New Public Management, Agent-Based Modeling and Simulation
Abstract: The paper at hand applies agent-based modeling and simulations (ABMS) as a tool to reconstruct and to analyze how the science system works. A Luhmannian systems perspective is combined with a model of decision making of individual actors. Additionally, changes in the socio-political context of science, such as the introduction of „new public management\", are considered as factors affecting the functionality of the system as well as the decisions of individual scientists (e.g. where to publish their papers). Computer simulation helps to understand the complex interplay of developments at the macro (system) and the micro (actor) level.

Modelling Theory Communities in Science

Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 14 (4) 8

Kyeywords: Simulating Science, Theory Interaction, Agent-Based Modelling, Theory Network
Abstract: This position paper presents a framework for modelling theory communities where theories interact as agents in a conceptual network. It starts with introducing the difficulties in integrating scientific theories by discussing some recent approaches, especially of structuralist theory of science. Theories might differ in reference, extension, scope, objectives, functions, architecture, language etc. To address these potential integration barriers, the paper employs a broad definition of "scientific theory", where a theory is a more or less complex description a describer puts forward in a context called science with the aim of making sense of the world. This definition opens up the agency dimension of theories: theories "do" something. They work on a - however ontologically interpreted - subject matter. They describe something, and most of them claim that their descriptions of this "something" are superior to those of others. For modelling purposes, the paper makes use of such description behaviour of scientific theories on two levels. The first is the level where theories describe the world in their terms. The second is a sub-case of the first: theories can of course describe the description behaviour of other theories concerning this world and compare with own description behaviour. From here, interaction and potential cooperation between theories could be potentially identified by each theory perspective individually. Generating inclusive theory communities and simulating their dynamics using an agent-based model means to implement theories as agents; to create an environment where the agents work as autonomous entities in a self-constituted universe of discourse; to observe what they do with this environment (they will try to apply their concepts, and instantiate their mechanisms of sense-making); and to let them mutually describe and analyse their behaviour and suggest areas for interaction. Some mechanisms for compatibility testing are discussed and the prototype of the model with preliminary applications is introduced.

For an Integrated Approach to Agent-Based Modeling of Science

Nicolas Payette
Journal of Artificial Societies and Social Simulation 14 (4) 9

Kyeywords: Agent-Based Models, Science Dynamics, Social Networks, Scientometrics, Evolutionary Computation
Abstract: The goal of this paper is to provide a sketch of what an agent-based model of the scientific process could be. It is argued that such a model should be constructed with normative claims in mind: i.e. that it should be useful for scientific policy making. In our tentative model, agents are researchers producing ideas that are points on an epistemic landscape. We are interested in our agents finding the best possible ideas. Our agents are interested in acquiring credit from their peers, which they can do by writing papers that are going to get cited by other scientists. They can also share their ideas with collaborators and students, which will help them eventually get cited. The model is designed to answer questions about the effect that different possible behaviors have on both the individual scientists and the scientific community as a whole.

Participatory Agent-Based Simulation for Renewable Resource Management: The Role of the Cormas Simulation Platform to Nurture a Community of Practice

Christophe Le Page, Nicolas Becu, Pierre Bommel and François Bousquet
Journal of Artificial Societies and Social Simulation 15 (1) 10

Kyeywords: Agent-Based Simulation, Smalltalk, Cormas, Multi-Agent System, Generic Simulation Platform, Renewable Natural Resource Management, Community of Practice, Companion Modeling
Abstract: This paper describes how the Cormas platform has been used for 12 years as an artefact to foster learning about agent-based simulation for renewable resource management. Among the existing generic agent-based simulation platforms, Cormas occupies a tiny, yet lively, place. Thanks to regular training sessions and an electronic forum, a community of users has been gradually established that has enabled a sharing of ideas, practices and knowledge, and the emergence of a genuine community of practice whose members are particularly interested in participatory agent-based simulation.

Outstanding in the Field: Evaluating Auction Markets for Farmland Using Multi-Agent Simulation

Adam Arsenault, James Nolan, Richard Schoney and Donald Gilchrist
Journal of Artificial Societies and Social Simulation 15 (1) 11

Kyeywords: Multi-Agent Simulation, Auctions, Agriculture
Abstract: Land acquisition and ownership is an important part of modern agriculture in North America. Given the unique nature of farmland as a good, this paper develops a multi-agent simulation of farmland auction markets in a Canadian context. The model is used to generate data on land transactions between farm agents to determine if a particular auction design or type is better suited to farmland transactions. The simulation uses three different sealed-bid auctions, as well as an English auction. The auctions are compared on the basis of efficiency, stability, and perceived surplus. We find that the form of agent learning about land markets affects both sale price and the variance of sale prices in all of the studied auctions. The second-price-sealed-bid auction generates the most perceived surplus, most equitable share of surplus, and also decreases uncertainty in the common-value element of prices. But on a macroscopic level, it appears that auction choice does not influence market structure or evolution over time.

Nonlinear Dynamics of Crime and Violence in Urban Settings

Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham
Journal of Artificial Societies and Social Simulation 15 (1) 2

Kyeywords: Agent-Based Modeling, Crime, Violence, Anthropology, Socio-Cultural Model, Police
Abstract: We perform analysis of data on crime and violence for 5,660 U.S. cities over the period of 2005-2009 and uncover the following trends: 1) The proportion of law enforcement officers required to maintain a steady low level of criminal activity increases with the size of the population of the city; 2) The number of criminal/violent events per 1,000 inhabitants of a city shows non-monotonic behavior with size of the population. We construct a dynamical model allowing for system-level, mechanistic understanding of these trends. In our model the level of rational behavior of individuals in the population is encoded into each citizen's perceived risk function. We find strong dependence on size of the population, which leads to partially irrational behavior on the part of citizens. The nature of violence changes from global outbursts of criminal/violent activity in small cities to spatio-temporally distributed, decentralized outbursts of activity in large cities, indicating that in order to maintain peace, bigger cities need larger ratio of law enforcement officers than smaller cities. We also observe existence of tipping points for communities of all sizes in the model: reducing the number of law enforcement officers below a critical level can rapidly increase the incidence of criminal/violent activity. Though surprising, these trends are in agreement with the data.

Computational Modelling of Trust and Social Relationships

Alistair Sutcliffe and Di Wang
Journal of Artificial Societies and Social Simulation 15 (1) 3

Kyeywords: Social Agents, Social Modelling, Trust, Social Networks
Abstract: A computational model for the development of social relationships is described. The model implements agent strategies for social interaction based on Dunbar's Social Brain Hypothesis (SBH). A trust related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit the SBH predictions was found across a range of model parameter settings, which varied the waning rate of trust, defect/cooperation rates for agents, and linear/log functions for trust increase and decay. Social interaction strategies which favour interacting with existing strong ties or a time variant strategy produced more SBH conformant results than strategies favour more weaker relationships. The prospects for modeling the emergence of social relationships are discussed.

Rethinking the Tragedy of the Commons: The Integration of Socio-Psychological Dispositions

Julia Schindler
Journal of Artificial Societies and Social Simulation 15 (1) 4

Kyeywords: Agent-Based Model, Common-Pool Resources, Behavioral Game Theory, Nash Equilibria, Nash Extension NetLogo, Socio-Psychological Dispositions, Tragedy of the Commons
Abstract: In current research there is increasing evidence on why and how common-pool resources are successfully, i.e. sustainably, managed without the force of (often unsuccessful) top-level policy regulations. G. Hardin argued in 1968 in his Tragedy of the Commons (Hardin 1968) that commons must become depleted if users are free to choose extraction and resource use levels. In this study, we propose that socio-psychological factors can explain the success of resource use of a common without any top-level regulations. We exemplify this behavior by a spatio-temporally dynamic agent-based model of the Tragedy of the Commons using behavioral game theory and Nash equilibria calculation. By providing a spatio-temporal representation of Hardin's dilemma, the model could verify his argument in a temporal way if socio-psychological influence is disregarded, and indicated that under its influence the common can be sustained. We illustrated how dispositions such as cooperativeness, positive reciprocity, fairness towards others, and risk aversion broadly can support sustainable use, while negative reciprocity, fairness towards oneself, and conformity can inhibit it. Though, we also showed that it would be dangerous to generalize this kind of behavior, as changes in one of these dispositions can result in opposite system behavior, in dependence on the other dispositions. Due to this general capacity to account for such complex behavior that real common-pool system usually exhibit, and its ability to model intermediate equilibria, the proposed modelling approach, i.e. combining game-theory solution concepts with agent-based modelling, may be worth an assessment of its capacity to model empirical phenomena.

UML for ABM

Hugues Bersini
Journal of Artificial Societies and Social Simulation 15 (1) 9

Kyeywords: Agent-Based Modeling, Object-Orientation Simulation, UML, Complex Systems
Abstract: Although the majority of researchers interested in ABM increasingly agree that the most natural way to program their models is to adopt OO practices, UML diagrams are still largely absent from their publications. In the last 15 years, the use of UML has risen constantly, to the point where UML has become the de facto standard for graphical visualization of software development. UML and its 13 diagrams has many universally accepted virtues. Most importantly, UML provides a level of abstraction higher than that offered by OO programming languages (Java, C++, Python, .Net ...). This abstraction layer encourages researchers to spend more time on modeling rather than on programming. This paper initially presents the four most common UML diagrams - class, sequence, state and activity diagrams (based on my personal experience, these are the most useful diagrams for ABM development). The most important features of these diagrams are discussed, and explanations based on conceptual pieces often found in ABM models are given of how best to use the diagrams. Subsequently, some very well known and classical ABM models such as the Schelling segregation model, the spatial evolutionary game, and a continuous double action free market are subjected to more detailed UML analysis.

Investigating the Relative Influence of Genes and Memes in Healthcare

Alistair Sutcliffe and Di Wang
Journal of Artificial Societies and Social Simulation 15 (2) 1

Kyeywords: Agent Models, Network Simulations, Health Informatics, Bayesian Models
Abstract: The process by which genes and memes influence behaviour is poorly understood. Genes generally may have a strong influence as predispositions directing individuals towards certain behaviours; whereas memes may have a less direct influence as information inputs to cognitive processes determining behaviour. In certain areas of medical science, knowledge has progressed towards approximate quantification of genetic influences, while social psychology can provide models of mimetic influence as the spread of attitudes. This paper describes a computational model integration of genetic and mimetic influences in a healthcare domain. It models mimetic influences of advertising and health awareness messages in populations with genetic predispositions towards obesity; environmental variables influence both gene expression and mimetic force. Sensitivity analysis using the model with different population network structures is used to investigate the relative force of meme spread and influence.

Analysis of the Emergent Properties: Stationarity and Ergodicity

Jakob Grazzini
Journal of Artificial Societies and Social Simulation 15 (2) 7

Kyeywords: Statistical Test, Stationarity, Ergodicity, Agent-Based, Simulations
Abstract: This paper illustrates the use of the nonparametric Wald-Wolfowitz test to detect stationarity and ergodicity in agent-based models. A nonparametric test is needed due to the practical impossibility to understand how the random component influences the emergent properties of the model in many agent-based models. Nonparametric tests on real data often lack power and this problem is addressed by applying the Wald-Wolfowitz test to the simulated data. The performance of the tests is evaluated using Monte Carlo simulations of a stochastic process with known properties. It is shown that with appropriate settings the tests can detect non-stationarity and non-ergodicity. Knowing whether a model is ergodic and stationary is essential in order to understand its behavior and the real system it is intended to represent; quantitative analysis of the artificial data helps to acquire such knowledge.

ABMland - a Tool for Agent-Based Model Development on Urban Land Use Change

Nina Schwarz, Daniel Kahlenberg, Dagmar Haase and Ralf Seppelt
Journal of Artificial Societies and Social Simulation 15 (2) 8

Kyeywords: Agent-Based Modelling, Urban, Land Use, Repast
Abstract: Modelling urban land use change can foster understanding of underlying processes and is increasingly realized using agent-based models (ABM) as they allow for explicitly coding land management decisions. However, urban land use change is the result of interactions of a variety of individuals as well as organisations. Thus, simulation models on urban land use need to include a diversity of agent types which in turn leads to complex interactions and coding processes. This paper presents the new ABMland tool which can help in this process: It is software for developing agent-based models for urban land use change within a spatially explicit and joint environment. ABMland allows for implementing agent-based models and parallel model development while simplifying the coding process. Six major agent types are already included as coupled models: residents, planners, infrastructure providers, businesses, developers and lobbyists. Their interactions are pre-defined and ensure valid communication during the simulation. The software is implemented in Java building upon Repast Simphony and other libraries.

Prisoner's Dilemma Game on Complex Networks with Agents' Adaptive Expectations

Bo Xianyu
Journal of Artificial Societies and Social Simulation 15 (3) 3

Kyeywords: Prisoner''s Dilemma Game, Complex Network, Adaptive Expectation, Agent-Based Simulation
Abstract: In the spatial prisoner's dilemma game, an agent's strategy choice depends upon the strategies he expects his neighboring agents to adopt. Yet, the expectation of agents in the games has not been studied seriously by the researchers of games in complex networks. The present paper studies the effect of the agents' adaptive expectation on cooperation emergence in the prisoner's dilemma game in complex networks from an agent-based approach. Simulation results show that the agents' adaptive expectation will favor the emergence of cooperation. However, due to agents' adaptive behavior, agents' initial expectation level does not greatly affect the cooperation frequency in the experiments. Simulation results also show that the agents' expectation adjustment speed significantly affects the cooperation frequency. In addition, the initial number of cooperation agents on the network is not a critical factor in the simulations. However, together with a bigger defection temptation, a larger neighborhood size will produce greater cooperation frequency fluctuations in a Barabási and Albert (BA) network, a feature different from that of Watts and Strogatz (WS) small world networks, which can be explained by their different networks degree distributions. Simulation results show that the cooperation frequency oscillating on the WS network is much smaller than that of the BA networks when defection temptation becomes larger. This research demonstrates that agent's adaptive expectation plays an important role in cooperation emergence on complex networks and it deserves more attentions.

Flora: A Testbed for Evaluating the Potential Impact of Proposed Systems on Population Wellbeing

Edgar Sioson
Journal of Artificial Societies and Social Simulation 15 (3) 6

Kyeywords: Simulation Testbed, Reputation Systems, Decentralized Currency, Modular Framework, Agent-Based Model
Abstract: We present Flora, a testbed that supports multidimensional fitness and resource modeling. Its main features are evaluation metrics related to population wellbeing, scalable representation of resource diversity, and composability of sociotechnical test scenarios through the TDI framework. We ran simulations to illustrate Flora's use in modeling the effects of using information infrastructures with different component systems. We analyzed the impact of hoarders in the absence of accounting systems, compared the performance of different decentralized currency systems in terms of accounting design features, and modeled the potential impact of reputation systems in deterring detrimental socioeconomic behavior. Among findings were the importance of having resource diversity as well as resources that each could target different fitness dimension needs; the inherent robustness of accounting systems that allow organizations to set budgets independently of centrally issued currency; and the greater effectiveness of buyer-screening compared to seller-screening as a means for influencing malevolent socioeconomic actors.

Logic-Based Reputation Model in E-Commerce Simulation

Ioan Alfred Letia and Radu Razvan Slavescu
Journal of Artificial Societies and Social Simulation 15 (3) 7

Kyeywords: Agent Based Social Simulation, Trust, Reputation, Cognitive Modeling, Multi-Modal Logic
Abstract: We employ a multimodal logic in a decision making mechanism involving trust and reputation. The mechanism is then used in a community of interacting agents which develop cooperative relationships, assess the results against several quality criteria and possibly publish their beliefs inside the group. A new definition is proposed for describing how an agent deals with the common reputation information and with divergent opinions. The definition permits selecting and integrating the knowledge obtained from the peers, based on their perceived trust, as well as on threshold called critical mass. The influence of this parameter and of the number of agents supporting a sentence over its adoption are then investigated.

Agent-Based Modelling: Tools for Linking NetLogo and r

Jan C. Thiele, Winfried Kurth and Volker Grimm
Journal of Artificial Societies and Social Simulation 15 (3) 8

Kyeywords: Agent-Based Modelling, Design of Experiments, R, NetLogo, Model Analysis, Modelling Software
Abstract: A seamless integration of software platforms for implementing agent-based models and for analysing their output would facilitate comprehensive model analyses and thereby make agent-based modelling more useful. Here we report on recently developed tools for linking two widely used software platforms: NetLogo for implementing agent-based models, and R for the statistical analysis and design of experiments. Embedding R into NetLogo allows the use of advanced statistical analyses, specific statistical distributions, and advanced tools for visualization from within NetLogo programs. Embedding NetLogo into R makes it possible to design simulation experiments and all settings for analysing model output from the outset, using R, and then embed NetLogo programs in this virtual laboratory. Our linking tools have the potential to significantly advance research based on agent-based modelling.

Slumulation: An Agent-Based Modeling Approach to Slum Formations

Amit Patel, Andrew Crooks and Naoru Koizumi
Journal of Artificial Societies and Social Simulation 15 (4) 2

Kyeywords: Slums, Housing, Developing Countries, Urban Poor, Informal Settlements, Agent-Based Modeling
Abstract: Slums provide shelter for nearly one third of the world's urban population, most of them in the developing world. Slumulation represents an agent-based model which explores questions such as i) how slums come into existence, expand or disappear ii) where and when they emerge in a city and iii) which processes may improve housing conditions for urban poor. The model has three types of agents that influence emergence or sustenance of slums in a city: households, developers and politicians, each of them playing distinct roles. We model a multi-scale spatial environment in a stylized form that has housing units at the micro-scale and electoral wards consisting of multiple housing units at the macro-scale. Slums emerge as a result of human-environment interaction processes and inter-scale feedbacks within our model.

Social Relationships and the Emergence of Social Networks

Alistair Sutcliffe, Di Wang and Robin Dunbar
Journal of Artificial Societies and Social Simulation 15 (4) 3

Kyeywords: Social Agents, Social Relationships, Trust, Evolution, Social Straegies
Abstract: In complex social systems such as those of many mammals, including humans, groups (and hence ego-centric social networks) are commonly structured in discrete layers. We describe a computational model for the development of social relationships based on agents' strategies for social interaction that favour more less-intense, or fewer more-intense partners. A trust-related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit of the observed layers of human social networks was found across a range of model parameter settings. Social interaction strategies which favour interacting with existing strong ties or a time-variant strategy produced more observation-conformant results than strategies favouring more weak relationships. Strong-tie strategies spread in populations under a range of fitness conditions favouring wellbeing, whereas weak-tie strategies spread when fitness favours foraging for food. The implications for modelling the emergence of social relationships in complex structured social networks are discussed.

Reexamining the Relative Agreement Model of Opinion Dynamics

Michael Meadows and Dave Cliff
Journal of Artificial Societies and Social Simulation 15 (4) 4

Kyeywords: Relative Agreement Model, Opinion Dynamics, Agent-Based Simulation
Abstract: We present a brief history of models of opinion dynamics in groups of agents, and summarise work from the creation of the Bounded Confidence model (Krause 2000; Hegselmann and Krause 2002) through to the more recent development of the Relative Agreement (RA) model (Deffuant et al. 2002; Deffuant 2006). In the RA model, randomly-selected pairs of agents interact, expressing their opinions and their confidence in those opinions; and each agent then updates their own opinion on the basis of the new information. The two seminal RA papers (Deffuant et al. 2002, Deffuant 2006), both published in JASSS, each present simulation results from the RA model that we have attempted to independently replicate. We have surveyed over 150 papers that cite Deffuant et al. 2002, yet have found no prior independent replications of the key empirical results for the RA model presented in the 2002 paper. We have each written a separate implementation of the RA model (one in Java, one in Python, both published in full as appendices to this paper) which we therefore believe to be the first independent replications of the RA model as published in the 2002 JASSS paper. We find that both our implementations of the RA model generate results that are in good agreement with each other, but both of which differ very significantly from those presented by Deffuant et al.. Our results are presented along with an analysis and discussion where we argue from first principles that our results are more plausible than those published in the 2002 JASSS paper. We close with discussion of the relevance of this model, along with future applicability.

Using Artificial Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects

Guillermo Montes
Journal of Artificial Societies and Social Simulation 15 (4) 8

Kyeywords: NetLogo, Agent Based Simulation, Racial Disparities, Achievement Gap, United States
Abstract: This paper presents an agent-based model of the standard U.S. k-12th grade classroom using NetLogo. By creating an artificial society, we identify the casual implications of the same-race effect (a moderate sized academic boost to students whose teachers have the same race) on the national educational achievement trends. The model predicts sizeable achievement gaps at the national level, consistent in size with those documented by the US National Report Card (NAEP) stemming from moderate sized same race effects. In addition, matching effects are found to be a source of increased heterogeneity in academic performance for the minority group. These results hold for all teacher-student matching phenomena and have implications for educational policy at the aggregate level. Using artificial societies to disentangle the aggregate effects of hypothesized causes of the achievement gap is a promising strategy that merits further research.

Thomas C. Schelling and the Computer: Some Notes on Schelling's Essay "On Letting a Computer Help with the Work"

Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 15 (4) 9

Kyeywords: Schelling Model, Segregation, Configuration Game, History of Computational Social Science, Agent Based Modeling
Abstract: Today the Schelling model is a standard component in introductory courses to agent-based modelling and simulation. When Schelling presented his model in the years between 1969 and 1978, his own analysis was based on manual table top exercises. Even more, Schelling explicitly warned against using computers for the analysis of his model. That is puzzling. A resolution to that puzzle can be found in an essay that Schelling wrote as teaching material for his students. That essay is now published by Schelling in JASSS, exactly 40 years after it was written. In his essay, Schelling gives a guided tour of a computer implementation of his model he himself implemented, de-spite his warnings. On this tour, though more in passing, Schelling gives hints to an extremely generalised version of his model. My article explains why we find the gen-eralised version of Schelling's model on the tour through his computer program rather than in his published articles.

The Results of Meadows and Cliff Are Wrong Because They Compute Indicator y Before Model Convergence

Guillaume Deffuant, Gérard Weisbuch, Frederic Amblard and Thierry Faure
Journal of Artificial Societies and Social Simulation 16 (1) 11

Kyeywords: Opinion Dynamics, Social Simulation, Agents Based Model
Abstract: Meadows and Cliff (2012) failed to replicate the results of Deffuant et al. (2002) and concluded that our paper was wrong. In this note, we show that the conclusions of Meadows and Cliff are due to a wrong computation of indicator y, which was not fully specified in our 2002 paper. In particular, Meadows and Cliff compute indicator y before model convergence whereas this indicator should be computed after model convergence.

An Agent-Based Model to Explore Game Setting Effects on Attitude Change During a Role Playing Game Session

Emmanuel Dubois, Olivier Barreteau and Véronique Souchère
Journal of Artificial Societies and Social Simulation 16 (1) 2

Kyeywords: Agent-Based Social Simulation, Role Playing Game, Companion Modelling, Attitude-Behaviour Relations, Attitude Change, Game Setting Effects
Abstract: Role playing games (RPGs) can be used as participatory simulation methods for environmental management. However, researchers in the field need to be aware of the influence of the game settings on participants' behavioural patterns and attitudes, before fine tuning the design and use of their games. We developed an agent-based model (CauxAttitude) to assess the framing induced by the conditions of implementation of a specific game, named CauxOpération, on possible changes in participants' attitudes. We designed CauxAttitude on the basis of social psychology theories that describe relations between attitudes and behaviours, as well as on observations of CauxOpération sessions. In this paper, we describe how the model behaved according to variations in the initialization of the parameters, our aim being to explore the effects of subjective choices concerning model design and implementation. The results of our simulations enabled us to identify effects of game settings we explored, including the choice of the population of participants or of the number of participants made by the game designer. Our results also revealed the underlying mechanisms that explain the effects of game settings. These provide clues to the game designer on how to manage them.

An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution

Keeheon Lee, Shintae Kim, Chang Ouk Kim and Taeho Park
Journal of Artificial Societies and Social Simulation 16 (1) 3

Kyeywords: Agent-Based Product Diffusion Model, Individual Purchase Time, Social Network Structure, Estimation, Sensitivity Analysis
Abstract: To maximise the possibility of success for a new product and minimise the risk and opportunity cost of a failed product, firms must understand the diffusion dynamics of competing products. The diffusion dynamics of competing products emerge from the aggregation of consumers' decisions. At the individual level, a consumer's decision consists of "which product to buy among the available products" and "when to buy a product". Individual product choices are affected by local and global social interactions among consumers. It would be helpful for firms to be able to determine the characteristics of the relevant social network for their target market and how changes in this social network influence their market shares. In addition, determining the distribution of product purchase times of consumers and how their variation affects market shares are interesting issues for firms. In this study, therefore, we propose an agent-based simulation model that generates the market share paths (market shares over time) of competing products. We apply the model to estimate the social network and purchase time distribution of the Korean netbook market. Our observation is that Korean netbook consumers tend to buy a product without hesitation, and their social network is rather regular but sparse. We also conduct sensitivity analyses with respect to the social network and the purchase time distribution.

Policy Innovation, Decentralised Experimentation, and Laboratory Federalism

Nicole J. Saam and Wolfgang Kerber
Journal of Artificial Societies and Social Simulation 16 (1) 7

Kyeywords: Laboratory Federalism, Policy Learning, Policy Innovation, Decentralisation
Abstract: Decentralised experimentation and mutual learning of public policies is seen as one of the important advantages of federal systems (Oates: laboratory federalism). Based upon Hayekian ideas of the advantages of decentralised experimentation (as a discovery procedure), we analyse the long-term benefits of parallel experimentation in a federal system from an evolutionary economics perspective. We present a simulation model in which the lower-level jurisdictions in a federal system experiment with randomly chosen policy innovations and can imitate the relatively best solutions. The simulations confirm our hypotheses that a higher degree of decentralisation has positive effects on the long-term accumulation of knowledge of suitable policy solutions and also limits risks through better protection against erroneous policies. Also problems of policy learning and trade offs with (static and dynamic) advantages of centralisation are taken into account.

Agent-Based Modeling as a Tool for Trade and Development Theory

Timothy R. Gulden
Journal of Artificial Societies and Social Simulation 16 (2) 1

Kyeywords: Agent-Based Modeling, Agent-Based Computational Economics, International Economics, Comparative Advantage, Increasing Returns, NetLogo
Abstract: This paper makes use of an agent-based framework to extend traditional models of comparative advantage in international trade, illustrating several cases that make theoretical room for industrial policy and the regulation of trade. Using an agent based implementation of the Hecksher-Ohlin trade model; the paper confirms Samuelson's 2004 result demonstrating that the principle of comparative advantage does not ensure that technological progress in one country benefits its trading partners. It goes on to demonstrate that the presence of increasing returns leads to a situation with multiple equilibria, where free market trading policies can not be relied on to deliver an outcome which is efficient or equitable, with first movers in development enjoying permanent advantage over later developing nations. Finally, the paper examines the impact of relaxation of the Ricardian assumption of capital immobility on the principle of comparative advantage. It finds that the dynamics of factor trade are radically different from the dynamics of trade in goods and that factor mobility converts a regime of comparative advantage into a regime of absolute advantage, thus obviating the reassuring equity results that stem from comparative advantage.

An Agent Based Model of Monopolistic Competition in International Trade with Emerging Firm Heterogeneity

Ermanno Catullo
Journal of Artificial Societies and Social Simulation 16 (2) 7

Kyeywords: International Trade, Agent Based Model, Firm Heterogeneity
Abstract: Export firms have better performance than firms that do not export, the so-called exporter premia: exporters are larger, they are relatively more capital and skill intensive, exporters have higher productivity (Bernard et al. 2007a; Bernard et al. 2005). The better performance of exporters may be the result of a self-selection effect: only the most competitive firms are able to enter foreign markets (ex-ante self-selection). On the other hand, exporting may improve firm performance (ex-post effect). Differences between exporters and non-exporters may have a significant impact on aggregate welfare and growth; in particular, disentangling the importance of the ex-ante effect from the ex-post effect may be useful for designing public policies (Bernard & Jensen 1999). The economic approach based on the Melitz (2003) model analyzes the exporter premia using monopolistic competition markets with firm heterogeneity in terms of a given distribution of firm productivity. This paper presents an agent based simulation of a monopolistic competition market in which firm heterogeneity is an emerging pattern of firms' choices and interactions, conceiving productivity growth as the results of firms' individual innovative efforts. The model is able to replicate the better performance of exporters, stressing the importance of decision-making processes and learning capabilities of firms in determining both the ex-ante and the ex-post effects.

Modeling Sanction Choices on Fraudulent Benefit Exchanges in Public Service Delivery

Yushim Kim, Wei Zhong and Yongwan Chun
Journal of Artificial Societies and Social Simulation 16 (2) 8

Kyeywords: Fraud, Public Service Delivery, Deterrence, Agent-Based Modeling
Abstract: Public service delivery programs are not free from players' opportunistic behaviors, such as fraudulent benefit exchanges. The standard methods used to detect such misbehaviors are static, less effective in uncovering interactions between corrupt agents, and easy to evade because of corrupt agents' familiarity with detection procedures. Current fraud detection efforts do not match the dynamics and adaptive processes they are supposed to monitor and regulate. In this paper, an agent-based simulation model is built to gain insight on sanction choices to deter fraudulent activities in public service delivery programs. The simulation outputs demonstrate that sanctions with low certainty must be accompanied by prompt action in order to observe a reduction in fraudulent vendors. However, a similar level of reduction in fraudulent vendors may be achieved once a certain number of fraudulent vendors are sanctioned, even if the public agency's action is relatively delayed. These characteristics of sanctions provide strategic choices that public service delivery program managers can consider based on their priorities and resources.

MAIA: a Framework for Developing Agent-Based Social Simulations

Amineh Ghorbani, Pieter Bots, Virginia Dignum and Gerard Dijkema
Journal of Artificial Societies and Social Simulation 16 (2) 9

Kyeywords: Modelling Language, Model-Driven Engineering, Institutions, Social Simulation, Meta-Model
Abstract: In this paper we introduce and motivate a conceptualization framework for agent-based social simulation, MAIA: Modelling Agent systems based on Institutional Analysis. The MAIA framework is based on Ostrom's Institutional Analysis and Development framework, and provides an extensive set of modelling concepts that is rich enough to capture a large range of complex social phenomena. Developing advanced agent-based models requires substantial experience and knowledge of software development knowledge and skills. MAIA has been developed to help modellers who are unfamiliar with software development to conceptualize and implement agent-based models. It provides the foundation for a conceptualization procedure that guides modellers to adequately capture, analyse, and understand the domain of application, and helps them report explicitly on the motivations behind modelling choices. A web-based application supports conceptualization with MAIA, and outputs an XML file which is used to generate Java code for an executable simulation.

Modelling the Economy as an Agent-Based Process: ABCE, A Modelling Platform and Formal Language for ACE

Davoud Taghawi-Nejad
Journal of Artificial Societies and Social Simulation 16 (3) 1

Kyeywords: Agent Based Modeling, Macoeconomics, Ontology, Economics, Process, Platform
Abstract: In this paper, I argue that the key innovation of Agent-Based Economics is not the introduction of the individual agent as an ontological object, but the fact that the economy is modelled as a process. I propose a formal language to express economic models as processes. This formal language leads to ABCE, a modelling platform for Agent-Based Economic models. ABCE's core idea is that the modeller specifies the decisions of the agents, the order of actions, the goods and their physical transformation (the production and the consumption functions). Actions, such as production and consumption, interactions and exchange, are handled automatically by the modelling platform, when the agent decided to do them. The result is a program where *the source code contains only economically meaningful commands*. Beyond the decisions and the setup, ABCE handles everything in the background. It scales on multi-core computers and cloud computing services, without the intervention of the modeler. ABCE is based on python, a language which is characterized by highly readable code.

Analysis of Asymmetric Two-Sided Matching: Agent-Based Simulation with Theorem-Proof Approach

Naoki Shiba
Journal of Artificial Societies and Social Simulation 16 (3) 11

Kyeywords: Social Simulation, Agent-Based Models (ABM), Theorem-Proof Approach, Mate-Search Problem, Two-Sided Matching, Job Matching
Abstract: This paper discusses an extended version of the matching problem which includes the mate search problem; this version is a generalization of a traditional optimization problem. The matching problem is extended to a form of the asymmetric two-sided matching problem. An agent-based simulation model is built and simulation results are presented. Todd and Miller (1999) simulated the two-sided matching problem in a symmetric setting. In his model, there are the same number of agents in both parties (groups), each of whom has his/her own mate value. Each agent in a party tries to find his/her mate in the other party, based on his/her candidate's mate value and his/her own aspiration level for his/her partner's mate value. Each agent learns his/her own mate value and adjusts his/her aspiration level through the trial period (adolescence). Todd and Miller (1999) tried several search rules and learning mechanisms that are symmetric for both parties. In the present paper, Todd and Miller's (1999) model is extended to an asymmetric setting where the two parties have different numbers of agents, and the search rule and the learning mechanism for the two parties differ. Through the simulation, the search rules and the learning mechanisms which were identified to be appropriate in a symmetric setting are revealed to be inappropriate in the asymmetric setting and the reason why this is so is discussed. Furthermore, some general facts are derived using a mathematical theorem-proof approach. Some of these facts are used to direct a revision of the model, and a revised simulation model is presented. An implication is obtained for practical situations in asymmetric matching setting. For example, in the job hunting case, if job applicants want to finish their job hunting successfully, they should be modest at the beginning of the hunt.

Asking the Oracle: Introducing Forecasting Principles into Agent-Based Modelling

Samer Hassan, Javier Arroyo, José Manuel Galán, Luis Antunes and Juan Pavón
Journal of Artificial Societies and Social Simulation 16 (3) 13

Kyeywords: Forecasting, Guidelines, Prediction, Agent-Based Modelling, Modelling Process, Social Simulation
Abstract: This paper presents a set of guidelines, imported from the field of forecasting, that can help social simulation and, more specifically, agent-based modelling practitioners to improve the predictive performance and the robustness of their models. The presentation starts with a discussion on the current debate on prediction in social processes, followed by an overview of the recent experience and lessons learnt from the field of forecasting. This is the basis to define standard practices when developing agent-based models under the perspective of forecasting experimentation. In this context, the guidelines are structured in six categories that correspond to key issues that should be taken into account when building a predictor agent-based model: the modelling process, the data adequacy, the space of solutions, the expert involvement, the validation, and the dissemination and replication. The application of these guidelines is illustrated with an existing agent-based model. We conclude by tackling some intrinsic difficulties that agent-based modelling often faces when dealing with prediction models.

Pitfalls in Spatial Modelling of Ethnocentrism: A Simulation Analysis of the Model of Hammond and Axelrod

Fredrik Jansson
Journal of Artificial Societies and Social Simulation 16 (3) 2

Kyeywords: Agent-Based Modelling, Ethnocentrism, Prisoners'' Dilemma, Spatial Interactions, Validation
Abstract: Ethnocentrism refers to the tendency to behave differently towards strangers based only on whether they belong to the ingroup or the outgroup. It is a widespread phenomenon that can be triggered by arbitrary cues, but the origins of which is not clearly understood. In a recent simulation model by Hammond and Axelrod, an ingroup bias evolves in the prisoner's dilemma game. However, it will be argued here that the model does little to advance our understanding of ethnocentrism. The model assumes a spatial structure in which agents interact only with their immediate neighbourhood, populated mostly by clones, and the marker becomes an approximate cue of whether the partner is one. It will be shown that agents with an ingroup bias are successful compared to unconditional co-operators since they only exclude non-clones, but are outcompeted by less error-prone kin identifiers. Thus, the results of the simulations can be explained by a simple form of kin selection. These findings illustrate how spatial assumptions can alter a model to the extent that it no longer describes the phenomenon under study.

CROSS: Modelling Crowd Behaviour with Social-Cognitive Agents

Nanda Wijermans, René Jorna, Wander Jager, Tony van Vliet and Otto Adang
Journal of Artificial Societies and Social Simulation 16 (4) 1

Kyeywords: Crowd Behaviour, Social-Cognitive Model, Multi-Level, Agent-Based, Crowds
Abstract: The use of computer simulations in crowd research is a powerful tool to describe and analyse complex social systems. This paper presents CROSS, a generic framework to model crowd simulations as a social scientific tool for understanding crowd behaviour. In CROSS, individuals are represented by social-cognitive agents that are affected by their social and physical surroundings and produce cognition-based behaviour and behaviour patterns. Understanding is sought by relating intra- and inter-individual levels of behaviour generation with behaviour pattern emergence at group level. By specifying the CROSS framework for a festival context we demonstrate how CROSS meets the need for a theory that reflects the dynamic interplay between individuals and their environment as well as the need for a method that allows for testing.

MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System

Scott Heckbert
Journal of Artificial Societies and Social Simulation 16 (4) 11

Kyeywords: Social-Ecological System, Archaeology, Cellular Automata, Network Model, Trade Network, Agent-Based Model
Abstract: This paper presents results from the MayaSim model, an integrated agent-based, cellular automata, and network model representing the ancient Maya social-ecological system. The model represents the relationship between population growth, agricultural production, soil degradation, climate variability, primary productivity, hydrology, ecosystem services, forest succession, and the stability of trade networks. Agents representing settlements develop and expand within a spatial landscape that changes under climate variation and responds to anthropogenic impacts. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya, although this proof-of-concept model requires refinement and further archaeological data for calibration. This paper aims to identify candidate features of a resilient versus vulnerable social-ecological system, and employs computer simulation to explore this topic, using the ancient Maya as an example. Complex systems modelling identifies how interconnected variables behave, considering fast-moving variables such as land cover change and trade connections, meso-speed variables such as demographics and climate variability, as well as slow-moving variables such as soil degradation.

Communicating Social Simulation Models to Sceptical Minds

Annie Waldherr and Nanda Wijermans
Journal of Artificial Societies and Social Simulation 16 (4) 13

Kyeywords: Social Simulation, Agent-Based Modelling, Rejective Criticism, Constructive Feedback, Communication, Peer Support
Abstract: When talking to fellow modellers about the feedback we get on our simulation models the conversation quickly shifts to anecdotes of rejective scepticism. Many of us experience that they get only few remarks, and especially only little helpful constructive feedback on their simulation models. In this forum paper, we give an overview and reflections on the most common criticisms experienced by ABM modellers. Our goal is to start a discussion on how to respond to criticism, and particularly rejective scepticism, in a way that makes it help to improve our models and consequently also increase acceptance and impact of our work. We proceed by identifying common criticism on agent-based modelling and social simulation methods and show where it shifts to rejection. In the second part, we reflect on the reasons for rejecting the agent-based approach, which we mainly locate in a lack of understanding on the one hand, and academic territorialism on the other hand. Finally, we also give our personal advice to socsim modellers of how to deal with both forms of rejective criticism.

Segregated Cooperation

Roger Waldeck
Journal of Artificial Societies and Social Simulation 16 (4) 14

Kyeywords: Social Emotions, Norms, Prisoner, Spatial Interaction Structures, Segregation, Agent-Based Simulation
Abstract: Observations in experiments show that players in a prisoner's dilemma may adhere more or less to a cooperative norm. Adherence is defined by the intensity of pro-social emotions, like guilt, of deviating from the norm. Players consider also payoffs from defection as a motive to deviate. By combining both incentives, the modeling may explain conditional cooperation and the existence of polymorphic equilibria in which cooperators and defectors coexist. We then show by the use of simulations, that local interaction structures may produce segregation and the appearance of cooperative zones under these conditions.

The Evolution of Multiple Resistant Strains: An Abstract Model of Systemic Treatment and Accumulated Resistance

Benjamin D. Nye
Journal of Artificial Societies and Social Simulation 16 (4) 2

Kyeywords: Evolution, Acquired Resistance, Agent Based Modeling, Selection Pressure, SIS Model, PS-I
Abstract: The proliferation of resistant strains has been an unintended side effect of human interventions designed to eliminate unwanted elements of our environment. Any attempt to destroy an adaptive population must also be considered as a selection pressure, so that the most resistant members will comprise the next generation. Procedures have been developed to slow the evolution of resistances in a population, with the most common approaches being overkill and treatment cycling. This paper presents an agent-based Susceptible-Infection-Susceptible (SIS) model to explore the effectiveness of these procedures on an abstract epidemic of pathogens, focusing on how the interaction between interventions and mutations affects acquired resistance. Illustrative findings indicate that overkill performed better than cycling treatments when variation in resistances had a high degree of heritability. When resistance variation was effectively memoryless, cycling and overkill performed comparably. However, overkill was prone to backlash outliers where an amplification of infection resistance occurred- a significant drawback to the overkill technique. These backlash events indicate that cycling interventions might be more effective when variation is memoryless and carrying resistance incurs a cost to overall fitness. However, under limited fitness-cost conditions explored, cycling performed no better than overkill for preventing resistance.

Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa

Jeffrey Demarest, Sheree Pagsuyoin, Gerard Learmonth, Jonathan Mellor and Rebecca Dillingham
Journal of Artificial Societies and Social Simulation 16 (4) 3

Kyeywords: Agent-Based Model, Water Quality, Early Childhood Diarrhea, Stunting
Abstract: Diarrhea, the second leading cause of child morbidity and mortality, can have detrimental effects in the physical and cognitive development of children in developing countries. Health interventions (e.g., increased access to health services and safe water) designed to address this problem are difficult to implement in resource-limited settings. In this paper, we present a tool for understanding the complex relationship between water and public health in rural areas of a developing country. A spatial and temporal agent-based model (ABM) was developed to simulate the current water, sanitation, and health status in two villages in Limpopo Province, South Africa. The model was calibrated using empirical data and published sources. It was used to simulate the effects of poor water quality on the frequency of diarrheal episodes in children, and consequently on child development. Preliminary simulation results show that at the current total coliform levels in the water sources of the studied villages, children are expected to experience stunting by as much as -1.0 standard deviations from the World Health Organization height norms. With minor modifications, the calibrated ABM can be used to design and evaluate intervention strategies for improving child health in these villages. The model can also be applied to other regions worldwide that face the same environmental challenges and conditions as the studied villages.

Simulating Social and Economic Specialization in Small-Scale Agricultural Societies

Denton Cockburn, Stefani A. Crabtree, Ziad Kobti, Timothy A. Kohler and R. Kyle Bocinsky
Journal of Artificial Societies and Social Simulation 16 (4) 4

Kyeywords: Specialization, Agent-Based Modeling, Archaeology, Social Networks, Models of Social Influence, Barter
Abstract: We introduce a model for agent specialization in small-scale human societies that incorporates planning based on social influence and economic state. Agents allocate their time among available tasks based on exchange, demand, competition from other agents, family needs, and previous experiences. Agents exchange and request goods using barter, balanced reciprocal exchange, and generalized reciprocal exchange. We use a weight-based reinforcement model for the allocation of resources among tasks. The Village Ecodynamics Project (VEP) area acts as our case study, and the work reported here extends previous versions of the VEP agent-based model (“Village”). This model simulates settlement and subsistence practices in Pueblo societies of the central Mesa Verde region between A.D. 600 and 1300. In the base model on which we build here, agents represent households seeking to minimize their caloric costs for obtaining enough calories, protein, fuel, and water from a landscape which is always changing due to both exogenous factors (climate) and human resource use. Compared to the baseline condition of no specialization, specialization in conjunction with barter increases population wealth, global population size, and degree of aggregation. Differences between scenarios for specialization in which agents use only a weight-based model for time allocation among tasks, and one in which they also consider social influence, are more subtle. The networks generated by barter in the latter scenario exhibit higher clustering coefficients, suggesting that social influence allows a few agents to assume particularly influential roles in the global exchange network.

About the Uncertainties in Model Design and Their Effects: An Illustration with a Land-Use Model

Julia Schindler
Journal of Artificial Societies and Social Simulation 16 (4) 6

Kyeywords: Agent-Based Modelling, Policy-Support-Tool, Critique, Justification, Land Use
Abstract: Although agent-based modeling is a strong modelling method in many aspects, its high degree of freedom in agent design can also be regarded as weakness. This freedom requires strong validation strategies during model design for empirical models, especially when models aim to be descriptive enough for policy support. Where theory or evidence does not support model design, assumptions are usually made. In these cases, arguments should be given for why the assumptions do not impair the validity of results. However, we believe that such justifications are sometimes weak in such kinds of models. In particular, we believe that the justification arguments are mostly plausible, but often not strong enough to overrule other plausible arguments leading to different designs. We believe that the reasons for this argumentative ambiguity are sometimes rooted in the type of underlying theory, framework, or validation strategy chosen. The point is that we suspect that simulation results can be sensitive to this ambiguity. To test this hypothesis, we selected a well-tried theory/framework/validation design strategy, and built alternative versions of a land-use change model in line with the underlying strategy. Results clearly show that levels and direction of simulated land-use change are significantly different among model versions.

Coupling Environmental and Social Processes to Simulate the Emergence of a Savannah Landscape Mosaic Under Shifting Cultivation and Assess its Sustainability

Nicolas Becu, Christine Raimond, Eric Garine, Marc Deconchat and Kouami Kokou
Journal of Artificial Societies and Social Simulation 17 (1) 1

Kyeywords: Agent-Based Spatial Simulation, Social Resilience, Cameroon, Landscape Modeling, Shifting Cultivation
Abstract: This paper presents an agent-based spatial simulation of shifting cultivation applied to savannah landscape in North-Cameroon (Duupa ethnic community). The model is based on empirical rules and was developed by a team who seek to create interdisciplinary dynamics by combining domain specific approaches to the same subject. The manner in which the model is described in this paper reflects the interdisciplinary processes that guided its development. It is made up of four domain-specific modules - demography, agriculture, savannah regrowth and social rules - which converge to form a fifth one, i.e., the evolution of the mosaic of cultivated fields. The focus is on how the spatial organization of landscapes results of environmental and social interactions. Two scenarios are presented in this paper. The first simulates the transformation of savannah woodland into a shifting cultivation savannah landscape. The second simulates changes in the landscape and socio-demographic structure of a Duupa village over a 60-year period. The simulation results are used to identify some of the key aspects of the socio-environmental interactions and help to explain why at large spatial scales and over a long period of time, the composition and structure of a landscape appear rather stable. For instance, it is well known that demography plays a key role in both social and environmental dynamics of shifting cultivation systems. Yet, in the case of the Duupa system, we show that social resilience can be acquired through interactions between demographic cycles of rising and falling population levels and a socioeconomic redistribution system. Finally, we compare the model developed with other shifting cultivation models and provide some insights on future developments.

Simple Heuristics as Equilibrium Strategies in Mutual Sequential Mate Search

Ismail Saglam
Journal of Artificial Societies and Social Simulation 17 (1) 12

Kyeywords: Mate Choice, Mate Search, Simple Heuristics, Agent-Based Simulation, Behavioral Stability, Equilibrium Strategies
Abstract: Human mate choice is a boundedly rational process where individuals search for their mates without appealing to optimization techniques due to informational, computational and time constraints. A seminal work by Todd and Miller (1999) models this search process using simple heuristics, i.e. decision rules that adjust individuals' aspiration levels adaptively. To identify the best heuristic among a number of alternatives, they consider fixed measures of success. In this paper, we deal with the same identification problem by examining whether these heuristics would be favored by behavioral selection. To this aim, we extend the two-phase search model of Todd and Miller (1999) to a behavioral (strategic-form) game in which each individual in the population is a distinct player, each player's strategy space contains the same four heuristics (adjustment rules), and the payoff of each player is measured by the likelihood of his/her mating. For this game, we ask whether any strategy profile at which the whole population plays the same heuristic can be behaviorally stable with respect to the Nash equilibrium concept. Our simulations show that the unanimous use of the Take the Next Best Rule by the whole population never becomes an equilibrium in the simulation range of adolescence lengths. While the Adjust Relative Rule is found to be behaviorally stable for a wide part of the simulation range, especially for medium to high adolescence lengths, the rules Adjust Up/Down and Adjust Relative/2 are favored by behavioral selection for a small part of the simulation range and only when the adolescence is long and short, respectively. We make the final evaluation of the four heuristics with respect to a new success measure that integrates a behavioral stability metric proposed in this paper with two metrics of Todd and Miller (1999), namely the likelihood and the assortativeness of the mating generated by the heuristic in use.

Opinion Formation in the Digital Divide

Dongwon Lim, Hwansoo Lee, Hangjung Zo and Andrew Ciganek
Journal of Artificial Societies and Social Simulation 17 (1) 13

Kyeywords: Digital Divide, Opinion Dynamics, Agent-Based Model, Bounded Confidence Model
Abstract: The Internet is a public environment where people increasingly share information and exchange opinions. Not everyone can afford the costs of using the Internet, causing online opinions to be distorted in favor of certain social groups. This study examines the effect of the digital divide on opinion formation using the agent-based modeling (ABM) method. It extends the bounded confidence model to incorporate an online context and introduces accessibility and connectivity as new parameters. The simulation results indicate that connected agents are quicker to converge on a certain opinion than disconnected agents. Connected agents form an opinion cluster while disconnected agents are scattered over a broad range of opinions. The results also show that social harmony is harder to achieve as an individual’s ability to communicate their own opinion improves. Both connected and disconnected agents are more likely to become a minority with higher accessibility. Disconnected agents are 11 to 14 times more likely to become a minority than connected agents, which suggests that the digital divide may be associated with discrimination. This study provides additional insights for academia as well as practitioners on opinion formation in the digital divide. Research limitations are addressed along with suggested future research directions.

Modelling Tourism in the Galapagos Islands: An Agent-Based Model Approach

Francesco Pizzitutti, Carlos F. Mena and Stephen J. Walsh
Journal of Artificial Societies and Social Simulation 17 (1) 14

Kyeywords: Spatial Agent Based Model and Simulation, Galapagos Islands, Tourist Destination Dynamics
Abstract: Currently tourism is the main driver of change in the Galapagos Islands, affecting the social, terrestrial, and marine sub-systems. Tourism also has direct and indirect consequences for the unique archipelago’s natural habitats and for the human well-being. Describing the mechanisms that drive and affect most the tourism development in Galapagos is a preliminary condition to developing a better understanding of the interaction structure of factors that shape the Galapagos archipelago as a social-ecological complex system. In this paper, we present a first attempt to represent the touristic market in Galapagos trough an Agent Based Model (ABM) of touristic activity, focusing on touristic offers, reservations, and touristic activities. The model is based on an individual-based representation of tourists’ consumption preferences and touristic accommodation offers in the Galapagos Islands. Tourist agents are created to mimic the real world by assigning average characteristics of individuals who visit the Galapagos Archipelago of Ecuador. The accommodation offers (i.e., hotels and cruises) are generated in accordance with actual conditions derived from data collected through field surveys. The model includes a market agent that can change the prices, create and delete accommodation offers following an evolutionary algorithm. We carried out preliminary simulations that show a close agreement between real world data and model outputs. Furthermore we used the model to generate three “what if” scenarios in order to study how emergent patterns in the touristic market in Galapagos are affected by changes in the archipelago environment. In this way we illustrate how the model can be used as a useful tool to help public policy makers to explore the consequences of their decisions.

Agent-Based Simulation of Pedestrian Behaviour in Closed Spaces: A Museum Case Study

Alessandro Pluchino, Cesare Garofalo, Giuseppe Inturri, Andrea Rapisarda and Matteo Ignaccolo
Journal of Artificial Societies and Social Simulation 17 (1) 16

Kyeywords: Agent-Based Simulations, Carrying Capacity, Pedestrian Dynamics, Evacuation Dynamics
Abstract: In order to analyse the behaviour of pedestrians at the very fine scale, while moving along the streets, in open spaces or inside a building, simulation modelling becomes an essential tool. In these spatial environments, simulation requires the ability to model the local dynamics of individual decision making and behaviour, which is strongly affected by the geometry, social preferences, local and collective behaviour of other individuals. The dy-namics of people visiting and evacuating a museum offers an excellent case study along this line. In this paper we present an agent-based simulation of the Castello Ursino museum in Catania (Italy), evaluating its carrying capacity in terms of both satisfaction of the visitors in regime of non-emergency dynamics and their safety under alarm conditions.

Agent-Based Simulation of the Search Behavior in China's Resale Housing Market: Evidence from Beijing

Hong Zhang and Yang Li
Journal of Artificial Societies and Social Simulation 17 (1) 18

Kyeywords: Resale Housing Market, Search Behavior, Search Model, Agent-Based Simulation, Sensitivity Analysis
Abstract: In the paradigm of the search theory, we established the search model applicable to the characteristics of China's resale housing market, by modeling the search behavior for buyer and seller, respectively. Setting the parameters based on the Beijing housing market survey in August 2012, we implemented agent-based simulation to study the dynamics of the search behavior measured by search intensity and search time. Sensitivity test was also used to analyze the determinants of the search behavior for trading agents. The simulation results validate the idiosyncratic feature of the agent's search behavior, which is consistent with theoretical analysis. The increase of matching efficiency promotes the agents' search intensities, but the higher unit search cost can reduce the agents' search intensities. The buyer's search behavior is more sensitive to the change in the market tightness ratio. Brokerage service lowers the transaction price and lessens the agents' search intensities. Sensitivity test further reveals that, the matching efficiency and the market tightness ratio play very important role in improving housing market liquidity. The changes in the search cost and the broker commission rate can reduce the agents' search intensities significantly and there are critical turning points at which the abrupt change occurs.

Learning Dilemmas in a Social-Ecological System: An Agent-Based Modeling Exploration

Erin Bohensky
Journal of Artificial Societies and Social Simulation 17 (1) 2

Kyeywords: Catchment, Institution, Learning, South Africa, Water, Agent
Abstract: The process of learning in social-ecological systems is an emerging area of research, but little attention has been given to how social and ecological interactions motivate or inhibit learning. This is highly relevant to the South African water sector, where a major policy transition is occurring that provides local water users and managers with new opportunities to engage in adaptive learning about how to balance human and ecological needs for water. In this paper, an agent-based model is used to explore potential ‘learning dilemmas,’ or barriers to learning in the South African water sector, whereby human perceptions combined with social-ecological conditions affect the capacity, understanding, and willingness required to learn. Agents manage water according to different management strategies and use various indicators to evaluate their success. The model shows that in areas with highly variable hydrological regimes, agents may be less able to learn because conditions are too unpredictable for them to benefit from past experience. Because of these changing conditions, however, agents are more likely to try new water management strategies, promoting a greater diversity of experience in the system for agents to learn from in the future. In water-stressed areas, where agents tend to have greater difficulty fulfilling demand for water than in areas with abundant water supplies, they are also more apt to try new strategies. When learning is restricted to small areas, agents may learn more quickly but based on a more narrow range of experience than in larger or more heterogeneous areas. These results suggest a need to enhance learning so that it accounts for interacting hydrological, ecological, and social dynamics. Although the model is a highly stylised version of reality, this preliminary exploration may eventually help to reverse the past trend of poor understanding of social-ecological dynamics as they relate to water management.

Modeling the Emergence of Social Structure from a Phylogenetic Point of View

Ruth Dolado, Francesc S. Beltran and Vicenç Quera
Journal of Artificial Societies and Social Simulation 17 (1) 8

Kyeywords: Social Structure, Agent-Based Models (ABM), Biological Models
Abstract: Based on previous models (Hemelrijk 1998; Puga-González, Hildenbrant & Hemelrijk 2009), we have developed an agent-based model and software, called A-KinGDom, which allows us to simulate the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions and incorporates two main innovations (preliminary dominance interactions and a kinship factor), which allow us to define four different attack and affiliative strategies. In accordance with these strategies, we compared the data obtained under four simulation conditions with the results obtained in a previous study (Dolado & Beltran 2012) involving empirical observations of a captive group of mangabeys (Cercocebus torquatus). The results show that the combination of the effect of kinship on affiliative interactions and the use of ambiguity-reducing attack provide results that are the most similar to the results of the biological model (i.e., a captive group of mangabeys) used in this study.

Information Sharing to Reduce Misperceptions of Interactions Among Complementary Projects: A Multi-Agent Approach

Emmanuel Labarbe and Daniel Thiel
Journal of Artificial Societies and Social Simulation 17 (1) 9

Kyeywords: Misperception, Interactions, Complementary Activities, Information Sharing, Agent-Based Model
Abstract: Agents who invest periodically in two complementary projects i and j try to minimize shortfall due to misperceptions concerning the interaction a between i and j. Previous studies have analytically solved such problems but they have been limited to two agents making one decision. We set out with the hypothesis of a large number of deciders sharing information with their nearest neighbors in order to improve the understanding of a. After each period of time, they exchange information on their real payoff values which enables them to choose the best neighbor expected perception of  in order to minimize their shortfall. To model this situation, we used an agent-based approach and we considered that the payoff information transmission was more or less efficient depending on the difficulty to assess the real values or when agents voluntarily transfer wrong data to their neighbors. Our simulation results showed that the total shortfall of the network: i.) declines in case of overestimation of a, ii.) depends on the initial agent opinions about a, iii.) evolves in two different curve morphologies, iv.) is influenced by information quality and can express a high heterogeneity of final opinions and v.) declines if the size of the neighborhood increases, which is a counterintuitive result.

Individual Bias and Organizational Objectivity: An Agent-Based Simulation

Bo Xu, Renjing Liu and Weijiao Liu
Journal of Artificial Societies and Social Simulation 17 (2) 2

Kyeywords: Individual Bias, Agent-Based Modeling, Diversity, Exploration, Exploitation
Abstract: We introduce individual bias to the simulation model of exploration and exploitation and examine the joint effects of individual bias and other parameters, aiming to answer two questions: First, whether reducing individual bias can increase organizational objectivity? Second, whether measures, such as increasing organization size, can increase organizational objectivity in the presence of individual bias? Our results show that individual bias has both positive and negative effects, and reducing individual bias may be not beneficial when organization size is large or environment is turbulent. Diverse knowledge resulting from large organization size can help avoid the negative effects of individual bias when the bias is strong enough so that the individuals who are less limited by bias can be distinguished as the source of learning. Our results also suggest that increasing interpersonal learning, decreasing learning from the organization, task complexity, and environmental turbulence, and maintaining personnel turnover can improve organizational objectivity in the presence of individual bias.

Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms

Matthew Oremland and Reinhard Laubenbacher
Journal of Artificial Societies and Social Simulation 17 (2) 6

Kyeywords: Agent-Based Modeling, Optimization, Statistical Test, Genetic Algorithms, Reduction
Abstract: Questions concerning how one can influence an agent-based model in order to best achieve some specific goal are optimization problems. In many models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed in order to find solutions that do not require a search of the entire solution space. Model reduction techniques are introduced and a statistical measure for model similarity is proposed. Heuristic methods can be effective in solving multi-objective optimization problems. A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models. Results from data analysis, model reduction, and algorithm performance are assessed.

ICTs, Social Connectivity, and Collective Action: A Cultural-Political Perspective

Hai-hua Hu, Wen-tian Cui, Jun Lin and Yan-jun Qian
Journal of Artificial Societies and Social Simulation 17 (2) 7

Kyeywords: ICTs, Social Connectivity, Collective Action, Cultural Difference, Political Preference Distribution, Agent-Based Modeling
Abstract: In recent years, information and communication technologies (ICTs) have significantly affected the outcomes of large-scale collective actions. In addition, there is a well-known theoretical proposition that ICTs can fuel collective action by increasing individuals’ social connectivity that is closely related to recruitment capacity. This study aims to test this proposition by examining two moderating factors: the cultural context (i.e., online communication patterns) and the political context (i.e., the distribution of political preferences). By utilizing agent-based modeling, we find that ICT-improved connectivity not only scales down collective action if the distribution of political preference is insufficiently dispersed, but it also slows the diffusion speed if the overall propensity to participate is not strong. Moreover, the effects of ICT-improved connectivity on the scale and speed of collective action are similar under different cultural contexts. However, the theoretical implications suggest that ICTs are more effective in the collectivistic culture than in the individualistic culture.

A Simple Emulation-Based Computational Model

Carlos M Fernández-Márquez and Francisco J Vázquez
Journal of Artificial Societies and Social Simulation 17 (2) 8

Kyeywords: Agent-Based Computational Models, Social Interaction, Social Influence, Innovation
Abstract: Emulation is one of the simplest and most common mechanisms of social interaction. In this paper we introduce a descriptive computational model that attempts to capture the underlying dynamics of social processes led by emulation. The model allows, with few assumptions, to explain how and why highly skewed distributions emerge in human societies, where few trends are representative and co-exist with several minority trends. In particular, the model shows that if a society is too tolerant and permeable, all the agents converge to only one trend that leads to uniformity. If society’s tolerance is moderate, many trends arise but with a high dispersity of size, only a few of them being truly representative. Finally, in highly intolerant societies a considerable degree of segregation is reached, where lots of trends of similar size arise. Furthermore, the proposed model can reproduce several real phenomena in social processes in which emulation is present: cyclic evolution in trend areas, changes in leadership, extinction and resurgence of trend areas, the struggle between neighboring areas and the higher probability of having dominant trends in central areas, corresponding to moderate positions.

Modelling Maritime Piracy: A Spatial Approach

Elio Marchione, Shane D Johnson and Alan Wilson
Journal of Artificial Societies and Social Simulation 17 (2) 9

Kyeywords: Maritime Piracy, Crime, Map Generation, Simulation, Agent-Based Modelling
Abstract: This paper presents a model to generate dynamic patterns of maritime piracy. Model details, outputs and calibration are illustrated. The model presented here is a tool to estimate the number of pirates and their area of action. The Gulf of Aden is considered as a case study, and data on pirate attacks, vessels routes and flows through the Gulf of Aden in the year 2010 are used to build the model. Agent-based modelling is employed to simulate pirate, vessel and naval forces behaviours.

Enhancing Recycling of Construction Materials: An Agent Based Model with Empirically Based Decision Parameters

Christof Knoeri, Igor Nikolic, Hans-Joerg Althaus and Claudia R. Binder
Journal of Artificial Societies and Social Simulation 17 (3) 10

Kyeywords: Empirical Based Modelling, Agent Operationalization Approach, Socio-Technical System, Sustainable Resource Management, Multi Criteria Decision-Making
Abstract: Recycling of construction material is a valuable option for minimizing construction & demolition waste streams to landfills and mitigating primary mineral resource depletion. Material flows in the construction sector are governed by a complex socio-technical system in which awarding authorities decide in interaction with other actors on the use of construction materials. Currently, construction & demolition waste is still mainly deposited in landfills, as construction actors lack the necessary information and training regarding the use of recycled materials, and as a result have low levels of acceptance for them. This paper presents an agent-based model of the Swiss recycled construction material market based on empirical data derived from the agent operationalization approach. It elaborates on how recycling of construction materials can be enhanced by analysing key factors affecting the demand for recycled construction materials and developing scenarios towards a sustainable construction waste management. Doing so it demonstrates how detailed empirical agent decision data were incrementally included in the ABM model. Raising construction actors’ awareness of recycled materials as a decision option, in combination with small price incentives was most effective for enhancing the use of recycled materials. This could lead to a 50% reduction of construction & demolition waste streams to landfills, and significantly reduce the environmental impacts related to concrete applications. From a methodological perspective, although the agent operationalization approach provides a large empirical foundation, incremental model development turned out to be particularly important for the traceability of results and a realistic system representation.

Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'

Jan C. Thiele, Winfried Kurth and Volker Grimm
Journal of Artificial Societies and Social Simulation 17 (3) 11

Kyeywords: Parameter Fitting, Sensitivity Analysis, Model Calibration, Agent-Based Model, Inverse Modeling, NetLogo
Abstract: Agent-based models are increasingly used to address questions regarding real-world phenomena and mechanisms; therefore, the calibration of model parameters to certain data sets and patterns is often needed. Furthermore, sensitivity analysis is an important part of the development and analysis of any simulation model. By exploring the sensitivity of model output to changes in parameters, we learn about the relative importance of the various mechanisms represented in the model and how robust the model output is to parameter uncertainty. These insights foster the understanding of models and their use for theory development and applications. Both steps of the model development cycle require massive repetitions of simulation runs with varying parameter values. To facilitate parameter estimation and sensitivity analysis for agent-based modellers, we show how to use a suite of important established methods. Because NetLogo and R are widely used in agent-based modelling and for statistical analyses, we use a simple model implemented in NetLogo as an example, packages in R that implement the respective methods, and the RNetLogo package, which links R and NetLogo. We briefly introduce each method and provide references for further reading. We then list the packages in R that may be used for implementing the methods, provide short code examples demonstrating how the methods can be applied in R, and present and discuss the corresponding outputs. The Supplementary Material includes full, adaptable code samples for using the presented methods with R and NetLogo. Our overall aim is to make agent-based modellers aware of existing methods and tools for parameter estimation and sensitivity analysis and to provide accessible tools for using these methods. In this way, we hope to contribute to establishing an advanced culture of relating agent-based models to data and patterns observed in real systems and to foster rigorous and structured analyses of agent-based models.

Virtual Fieldwork: Modeling Observer Bias in Kinship and Marriage Alliance Networks

Klaus Hamberger and Floriana Gargiulo
Journal of Artificial Societies and Social Simulation 17 (3) 2

Kyeywords: Kinship, Marriage Alliance, Agent Based Simulation, Observer Bias, Fieldwork, Ebrei
Abstract: The morphological properties of genealogical and marriage alliance networks constitute a key to the understanding of matrimonial behavior and social norms, in particular where these norms have not been explicitly formalized. Their analysis, however, faces a major difficulty: the actual datasets which allow researchers to reconstruct kinship and alliance networks are generally subject to a marked observer bias, if only due to limitations of observer mobility and/or informant memory. This paper presents an agent based simulation method destined to evaluate the impact of this bias on some key indicators of kinship and alliance networks (such as matrimonial circuit frequencies). The method consists in explicitly simulating the exploration of a given network by a virtual observer, the bias being introduced by the observer’s inclination for choosing informants who are more or less closely related to each other. The article presents the model for genealogical and for alliance networks, applies it to a series of artificial networks exhibiting some characteristic morphological patterns, and discusses the divergence of observed from real patterns for different kinds and degrees of observer bias. The methods presented have been implemented in the free software Puck 2.0.

The Effects of Group Composition and Social Preference Heterogeneity in a Public Goods Game: An Agent-Based Simulation

Pablo Lucas, Angela C.M. de Oliveira and Sheheryar Banuri
Journal of Artificial Societies and Social Simulation 17 (3) 5

Kyeywords: Social Preferences, Group Composition, Beliefs, Agent-Based Simulation
Abstract: Behavioural economics highlights the role of social preferences in economic decisions. Further, populations are heterogeneous, suggesting that the composition of social preference types within a group may impact the ability to sustain voluntary public goods contributions. We conduct agent-based simulations of contributions in a public goods game, varying group composition and the weight individuals place on their beliefs versus their underlying social preference type. We then examine the effect of each of these factors on contributions. We find that social preference heterogeneity negatively impacts provision over a wide range of the parameter space, even controlling for the share of types in a group.

Improving Learning in Business Simulations with an Agent-Based Approach

Márcia Baptista, Carlos Roque Martinho, Francisco Lima, Pedro A. Santos and Helmut Prendinger
Journal of Artificial Societies and Social Simulation 17 (3) 7

Kyeywords: Agent-Based Modeling, Business Simulation, Consumer Behavior, Learning Processes
Abstract: Artificial society simulations may provide unprecedented insight into the intricate dynamics of economic markets. Such an insight may help solve the well-known black-box dilemma of business simulations, where designers prefer model concealment over model transparency. The core contribution of this work is an agent-based business simulation that models the marketplace as an artificial society of consumers. In the simulation, users assume the role of a store owner playing against an artificial intelligence competitor. The simulation can be accessed via a graphical user interface that animates the decision behavior of consumers. Consumers are modeled as agents with concrete beliefs, intentions and desires that act to maximize their utility and accomplish their purchase plans. We claim that unlike the classical equation-based approach, the visualization of market dynamics facilitated by our agent-based approach can provide important information to the user. We hypothesize that such information is key to understanding several economic concepts. To validate our hypothesis, we conducted an experiment with 30 users, where we compared the effects of the graphical animation of the market. Our results indicate that the agent-based approach has better learning outcomes both at the level of users' subjective self-assessment and at the level of objective performance metrics and knowledge acquisition tests. As a secondary contribution, we demonstrate by example how simple codification rules at the level of the utility functions of agents allow the emergence of diverse macroeconomic behavior of a two-product duopoly.

A Novel Private Attitude and Public Opinion Dynamics Model for Simulating Pluralistic Ignorance and Minority Influence

Chung-Yuan Huang and Tzai-Hung Wen
Journal of Artificial Societies and Social Simulation 17 (3) 8

Kyeywords: Social Influence, Private Acceptance, Public Compliance, Theory of Reasoned Action, Cognitive Dissonance Theory, Agent-Based Simulation
Abstract: Pluralistic ignorance, a well-documented socio-psychological conformity phenomenon, involves discrepancies between private attitude and public opinion in certain social contexts. However, continuous opinion dynamics models based on a bounded confidence assumption fail to accurately model pluralistic ignorance because they do not address scenarios in which non-conformists do not need to worry about holding and expressing conflicting opinions. Such scenarios reduce the power of continuous opinion dynamics models to explain why certain groups doubt or change their opinions in response to minority views. To simulate the effects of (a) private acceptance of informational social influence and (b) public compliance with normative social influence on pluralistic ignorance and minority influences, we have created an agent-based simulation model in which attitude and opinion respectively represent an agent's private and expressed thoughts. Results from a series of simulation experiments indicate model validity equal to or exceeding those of existing opinion dynamics models that are also based on the bounded confidence assumption, but with different dynamics and outcomes in terms of collective opinion and attitude. The results also support the use of our proposed model for computational social psychology applications.

How Do Agents Make Decisions? A Survey

Tina Balke and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 17 (4) 13

Kyeywords: Decision Making, Agents, Survey
Abstract: When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.

A Mathematical Model of the Beer Game

Mert Edali and Hakan Yasarcan
Journal of Artificial Societies and Social Simulation 17 (4) 2

Kyeywords: Acquisition Lag, Artificial Agents, Beer Game, Mathematical Model, Replication, System Dynamics
Abstract: The beer production-distribution game, in short “The Beer Game”, is a multiplayer board game, where each individual player acts as an independent agent. The game is widely used in management education aiming to give an experience to the participants about the potential dynamic problems that can be encountered in supply chain management, such as oscillations and amplification of oscillations as one moves from downstream towards upstream echelons. The game is also used in numerous scientific studies. In this paper, we construct a mathematical model that is an exact one-to-one replica of the original board version of The Beer Game. We apply model replication principles and discuss the difficulties we faced in the process of constructing the mathematical model. Accordingly, the model is presented in full precision including necessary assumptions, explanations, and units for all parameters and variables. In addition, the adjustable parameters are stated, the equations governing the artificial agents’ decision making processes are mentioned, and an R code of the model is provided. We also shortly discuss how the R code can be used in experimentation and how it can also be used to create a single-player or multi-player beer game on a computer. Our code can produce the exact same benchmark cost values reported by Sterman (1989) verifying that it is correctly implemented. The mathematical model and the R code presented in this paper aims to facilitate potential future studies based on The Beer Game.

Analysing Differential School Effectiveness Through Multilevel and Agent-Based Modelling

Mauricio Salgado, Elio Marchione and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 17 (4) 3

Kyeywords: Agent-Based Modelling, Differential School Effectiveness, Multilevel Modelling, Peer Effects, Teacher Expectation Bias
Abstract: During the last thirty years education researchers have developed models for judging the comparative performance of schools, in studies of what has become known as “differential school effectiveness”. A great deal of empirical research has been carried out to understand why differences between schools might emerge, with variable-based models being the preferred research tool. The use of more explanatory models such as agent-based models (ABM) has been limited. This paper describes an ABM that addresses this topic, using data from the London Educational Authority's Junior Project. To compare the results and performance with more traditional modelling techniques, the same data are also fitted to a multilevel model (MLM), one of the preferred variable-based models used in the field. The paper reports the results of both models and compares their performances in terms of predictive and explanatory power. Although the fitted MLM outperforms the proposed ABM, the latter still offers a reasonable fit and provides a causal mechanism to explain differences in the identified school performances that is absent in the MLM. Since MLM and ABM stress different aspects, rather than conflicting they are compatible methods.

Validation of an Agricultural MAS for Southland, New Zealand

Bill Kaye-Blake, Chris Schilling and Elizabeth Post
Journal of Artificial Societies and Social Simulation 17 (4) 5

Kyeywords: Agriculture, Interdisciplinary Research, Multi-Agent Simulation, Validation, Agent-Based Model
Abstract: This paper describes the process and results of validating a simulation model of agriculture for a region in New Zealand. Validation is treated as a process, in which simulation models are made useful for specific purposes by making them conform to observed historical trends and relationships. In this case, the model was calibrated to reproduce the year-by-year conversion to dairying from 1993 to 2012 in Southland, New Zealand. This was achieved by holding constant some elements of the simulation model, based on economic theory or data, and by running simulations on a range of values for two key parameters. The paper describes the model and process, and demonstrates that empirical validation is possible if approached pragmatically with a view to the intended use of the model. Important elements are: using stylised facts to limit the parameter space ex ante, establishing the range of model outcomes and focusing on the most likely parameter space, focusing the search for parameter values where there is the greatest uncertainty, and using historical data to calibrate models.

Parental Choices and Children’s Skills: An Agent-Based Model of Parental Investment Behavior and Skill Inequality Within and Across Generations

Andrés Cardona
Journal of Artificial Societies and Social Simulation 17 (4) 8

Kyeywords: Skill Formation, Parental Investments, Inequality in the Life Course, Intrahousehold Allocation of Resources, Agent Behavior
Abstract: An agent-based simulation model (ABM) is developed and implemented using Python to explore the emergence of intragenerational and intergenerational skill inequality at the societal level that results from differences in parental investment behavior at the household level during early stages of the life course. Parental behavior is modeled as optimal, heuristic-based, or norm-oriented. Skills grow according to the technology of skill formation developed in the field of economics, calibrated with empirically estimated parameters from existing research. Agents go through a simplified life course. During childhood and adolescence, skills are produced through parental investments. In adulthood, individuals find a partner, give birth to the next generation, and invest in offspring. Number and spacing of children and available resources are treated as exogenous factors and are varied experimentally. Simulation experiments suggest that parental decisions at the household level play a role in the emergence of inequality at the societal level. Being egalitarian or not is the most important distinction in parental investment behavior, while optimizing parents generate similar results as egalitarian parents. Furthermore, there is a tradeoff between equality at home and inequality at the macro-level. Changes in the environment reduce or exacerbate inequality depending on parental investment behavior. One prediction of the model on intragenerational inequality in cognitive skills was validated with the use of empirical data. The simulation can best be described as a middle-range model, informed by research on skill formation and the intrahousehold allocation of resources. It is a first step toward more complex ABMs on inequality from a life course perspective. Possible model extensions are suggested. The Overview, Design Concepts, and Details (ODD) protocol and Design of Experiments (DOE) were used to document the model and set up the experimental design respectively.

Exploring Transitions Towards Sustainable Construction: The Case of Near-Zero Energy Buildings in the Netherlands

Jesús Rosales-Carreón and César García-Díaz
Journal of Artificial Societies and Social Simulation 18 (1) 10

Kyeywords: Agent-Based Model, Near-Zero Energy Buildings, Innovation Systems, Knowledge Elicitation, Systemigrams
Abstract: This paper examines the use of qualitative information in the construction of an agent- based model in order to study the growth of near-Zero Energy Buildings (nZEB’s) in the Netherlands through the innovation systems perspective. Drawing on desktop research and semi-structured interviews, this paper offers two major findings. First, we observed that the difficulties to the development of nZEB’s have been shaped by interaction and institutional barriers: the inner complexity of the building sector has decisively impacted on the growth of nZEB’s. Second, exploring interviewees’ understanding of the system via an agent-based model has brought fresh insights about the problem. Overall, this is a call for an interdisciplinary approach to understand the changes required for nZEB’s in their path for a successful adoption. Agent-based computational modelling, complemented with knowledge that was elicited from several stakeholders within the building sector, has helped to inspect the implication of common beliefs in the course of shaping possible futures toward a transition to nZEB’s.

A Survey of Agent Platforms

Kalliopi Kravari and Nick Bassiliades
Journal of Artificial Societies and Social Simulation 18 (1) 11

Kyeywords: Intelligent Agents, Multi-Agent Systems, Agent Platforms
Abstract: From computer games to human societies, many natural and artificial phenomena can be represented as multi-agent systems. Over time, these systems have been proven a really powerful tool for modelling and understanding phenomena in fields, such as economics and trading, health care, urban planning and social sciences. However, although, intelligent agents have been around for years, their actual implementation is still in its early stages. Since the late nineties many agent platforms have been developed. Some of them have already been abandoned whereas others continue releasing new versions. On the other hand, the agent-oriented research community is still providing more and more new platforms. This vast amount of platform options leads to a high degree of heterogeneity. Hence, a common problem is how people interested in using multi-agent systems should choose which platform to use in order to benefit from agent technology. This decision was usually left to word of mouth, past experiences or platform publicity, lately however people depend on solid survey articles. To date, in most cases multi-agent system surveys describe only the basic characteristics of a few representatives without even providing any classification of the systems themselves. This article presents a comparative up-to-date review of the most promising existing agent platforms that can be used. It is based on universal comparison and evaluation criteria, proposing classifications for helping readers to understand which agent platforms broadly exhibit similar properties and in which situations which choices should be made.

Structuring Qualitative Data for Agent-Based Modelling

Amineh Ghorbani, Gerard Dijkema and Noortje Schrauwen
Journal of Artificial Societies and Social Simulation 18 (1) 2

Kyeywords: Ethnography, Institutional Analysis, Survey, Qualitative Data, MAIA, Conceptual Modelling
Abstract: Using ethnography to build agent-based models may result in more empirically grounded simulations. Our study on innovation practice and culture in the Westland horticulture sector served to explore what information and data from ethnographic analysis could be used in models and how. MAIA, a framework for agent-based model development of social systems, is our starting point for structuring and translating said knowledge into a model. The data that was collected through an ethnographic process served as input to the agent-based model. We also used the theoretical analysis performed on the data to define outcome variables for the simulation. We conclude by proposing an initial methodology that describes the use of ethnography in modelling.

Modelling Academics as Agents: An Implementation of an Agent-Based Strategic Publication Model

Xin Gu, Karen Blackmore, David Cornforth and Keith Nesbitt
Journal of Artificial Societies and Social Simulation 18 (2) 10

Kyeywords: Academic Science, Lotka’s Law, Strategic Publication Model, Agent-Based Model
Abstract: The rapid changes occurring in the higher education domain are placing increasing pressure on the actors in this space to focus efforts on identifying and adopting strategies for success. One particular group of interest are academics or scientists, and the ways that these individuals, or collectives as institutional or discipline-based science systems, make decisions about how best to achieve success in their chosen field. The agent-based model and simulation that we present draws on the hypothetical “strategic publication model” proposed by Mölders, Fink and Weyer (2011), and extends this work by defining experimental settings to implement a prototype ABMS in NetLogo. While considerable work remains to fully resolve theoretical issues relating to the scope, calibration and validation of the model, this work goes some way toward resolving some of the details associated with defining appropriate experimental settings. Also presented are the results of four experiments that focus on exploring the emergent effects of the system that result from varying the strategic mix of actors in the system.

Exploring Creativity and Urban Development with Agent-Based Modeling

Ammar Malik, Andrew Crooks, Hilton Root and Melanie Swartz
Journal of Artificial Societies and Social Simulation 18 (2) 12

Kyeywords: Developing Countries, Urban, Segregation, Land Use, Transport, Agent-Based Modeling
Abstract: Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socio-economic diversity and societal tolerance; the elevated interaction intensity diffuses creativity and improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public services. We operationalize these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following three scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of these scenarios results in greater population density and enables the diffusion of creativity, thus resulting in economic growth. However, the results demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity.

What One Can Learn from Extracting OWL Ontologies from a NetLogo Model That Was Not Designed for Such an Exercise

Klaus G. Troitzsch
Journal of Artificial Societies and Social Simulation 18 (2) 14

Kyeywords: NetLogo, OWL, OWL-API, Ontology, Agent-Based Model
Abstract: J. Gary Polhills forum paper in this issue was an invitation to try the OWL extension on a model that was written more than a year ago. Download and installation was a matter of a few minutes, extending the old model with a few lines as shown in the paper was not a problem either, visualising the OWL output with different versions of Protégé was a little more difficult, but in the end showed interesting suggestions how to improve the original version of the NetLogo model.

Modeling Real Estate Market Responses to Climate Change in the Coastal Zone

Handi Chandra Putra, Haiyan Zhang and Clinton Andrews
Journal of Artificial Societies and Social Simulation 18 (2) 18

Kyeywords: Flooding Risk, Real Estate Market, Agent-Based Model
Abstract: Changing flood risks threaten the value of billions of dollars worth of coastal real estate as well as the viability of coastal communities. This paper presents an agent-based model to capture some of the main features of the housing market that emerges from interactions between autonomous buyers and sellers. We use this model to investigate the adaptive responses of real estate markets to changing patterns of flooding and alternative flood insurance policies. The model includes interactions among households and government through land use regulations, property tax collection and dissemination of flooding risk information. We use detailed data from a flood-prone coastal community in New Jersey, USA to calibrate our model.

Impacts of Farmer Coordination Decisions on Food Supply Chain Structure

Caroline Krejci and Benita Beamon
Journal of Artificial Societies and Social Simulation 18 (2) 19

Kyeywords: Food Supply Chains, Sustainable Agriculture, Coordination, Agent-Based Modeling, Farmer Decision Making, Multi-Agent Simulation
Abstract: To increase profitability, farmers often decide to form strategic partnerships with other farmers, pooling their resources and outputs for greater efficiency and scale. These coordination decisions can have far-reaching and complex implications for overall food supply chain structural emergence, which in turn impacts system outcomes and long-term sustainability. In this paper, we describe an agent-based model that explores the impacts of farmer coordination decisions on the development of food supply chain structure over time. This model focuses on one type of coordination mechanism implementation method, in which coordinated farmer groups produce a single crop type and combine their yields to achieve economies of scale. The farmer agents’ decisions to coordinate with one another depend on their evaluation of the tradeoff between their autonomy and the expected economic benefits of coordination. Each coordination decision is a bilateral process in which the terms of group reward sharing are negotiated. We capture the effects of farmers’ size, income, and autonomy premia, as well as volume-price relationships and group profit-sharing rules, on the rate of farmer coordination and the number and size of groups that form. Results indicate that under many conditions, coordination groups tend to consolidate over time, which suggests implications for overall supply chain structural resilience.

Modeling Oligarchs' Campaign Donations and Ideological Preferences with Simulated Agent-Based Spatial Elections

Mason Wright and Pratim Sengupta
Journal of Artificial Societies and Social Simulation 18 (2) 3

Kyeywords: Multi-Agent Models, Lobbying, Public Choice, Bounded Rationality, Voting Behavior, Social Simulation
Abstract: In this paper, we investigate the interactions among oligarchs, political parties, and voters using an agent-based modeling approach. We introduce the OLIGO model, which is based on the spatial model of democracy, where voters have positions in a policy space and vote for the party that appears closest to them, and parties move in policy space to seek more votes. We extend the existing literature on agent-based models of political economy in the following manner: (1) by introducing a new class of agents – oligarchs – that represent leaders of firms in a common industry who lobby for beneficial subsidies through campaign donations; and (2) by investigating the effects of ideological preferences of the oligarchs on legislative action. We test hypotheses from the literature in political economics on the behavior of oligarchs and political parties as they interact, under conditions of imperfect information and bounded rationality. Our key results indicate that (1) oligarchs tend to donate less to political campaigns when the parties are more resistant to changing their policies, or when voters are more in-formed; and (2) if Oligarchs donate to parties based on a combination of ideological and profit motivations, Oligarchs will tend to donate at a lower equilibrium level, due to the influence of lost profits. We validate these outcomes via comparisons to real world polling data on changes in party support over time.

An Agent-Based Model of Status Construction in Task Focused Groups

André Grow, Andreas Flache and Rafael Wittek
Journal of Artificial Societies and Social Simulation 18 (2) 4

Kyeywords: Status Characteristics, Status Beliefs, Status Construction, Task Focused Groups, Agent-Based Computational Modeling
Abstract: Status beliefs link social distinctions, such as gender and race, to assumptions about competence and social worth. Recent modeling work in status construction theory suggests that interactions in small, task focused groups can lead to the spontaneous emergence and diffusion of such beliefs in larger populations. This earlier work has focused on dyads as the smallest possible groups in which status beliefs might emerge from face-to-face interaction. In today’s societies, however, many task focused interactions take place in groups larger than dyads. In this article, we therefore develop an agent-based computational model that enables us to study the emergence of status beliefs in groups larger than dyads. With this model, we address questions such as: Do basic principles of task focused interaction systematically favor the emergence of status beliefs in groups larger than dyads? Does the time-frame over which small groups interact affect the likelihood with which status beliefs emerge? How does group size affect the emergence of status beliefs? Computational experimentation with the new model suggests that behavioral principles known to spontaneously create hierarchical differentiation between individual group members also tend to align these hierarchies with categorical differences and thereby facilitate the emergence of status beliefs. This tendency is stronger in smaller groups, and in groups that interact either for a very short or very long time.

Impact of Population Relocation to City Commerce: Micro-Level Estimation with Validated Agent-Based Model

SeHoon Lee, Jeong Hee Hong, Jang Won Bae and Il-Chul Moon
Journal of Artificial Societies and Social Simulation 18 (2) 5

Kyeywords: Agent-Based Simulation, Discrete Event Model, Urban Design, Population Modeling, Urban Simulator
Abstract: To reduce overpopulation around Seoul, Korea, the government implemented a relocation policy of public officers by moving the government complex. This implies that there will be a negative impact on the suburban area that originally hosted the complex, but we do not know the magnitude of the impact. Therefore, this paper presents a micro-level estimation of the impact on the city commerce with an agent-based model. This model is calibrated by the micro-level population census data, the time-use data, and the geographic data. Agent behavior is formally specified to illustrate the daily activities of diverse population types, and particularly the model observes how many agents pass by commercial buildings of interest. With the described model, we performed a virtual experiment that examines the strengths of factors in negatively influencing the city commerce. After the experiment, we statistically validated the model with the survey data from the real world, which resulted in relatively high correlation between the real world and the simulations.

Local Opinion Heterogeneity and Individual Participation in Collective Behavior: A Reconsideration

Hai-hua Hu, Jun Lin and Wen-tian Cui
Journal of Artificial Societies and Social Simulation 18 (2) 6

Kyeywords: Local Opinion Heterogeneity, Participation Likelihood, Participation Timing, Collective Behavior, Agent-Based Modeling, Threshold Model
Abstract: Local opinion heterogeneity (LOH) critically influences an individual’s choice of collective behaviors, such as voting and protesting. However, several empirical studies have presented different conclusions on how LOH affects such preference. In the current research, the effect of LOH is considered based on agent-based modeling and the threshold model introduced by Granovetter (1978). A series of simulation experiments and statistical analyses are conducted. Results show that LOH has an inverse U-shape effect on the likelihood of participation (whether an individual decides to participate). By contrast, the findings reveal that LOH has a monotonous effect on participation timing (when a participant makes the decision). Specifically, when LOH is high, an individual opts to participate early. These observations can be explained by the influence of LOH on the structure of social networks and by the moderating effect of the global distribution of opinions within the population.

Mobilization, Flexibility of Identity, and Ethnic Cleavage

Kazuya Yamamoto
Journal of Artificial Societies and Social Simulation 18 (2) 8

Kyeywords: Mobilization, Identity, Nation, Ethnicity, Culture, Agent-Based Modeling
Abstract: In modern states, mobilization policy has been used to awaken people to new ideas such as national identity, industrial capitalism, and civic society. However, it has long been debated whether mobilization in new countries or in countries under reconstruction creates an integrated identity or results in fragmentation of various ethnic groups. Although the idea that identity is not immutable but malleable is now widely accepted in political science, sociology, and other social sciences, the degree to which identity can be reconstructed once it has been mobilized remains unclear. This study employs an agent-based model to address questions regarding the relationship between governments’ mobilization and the integration of identity in countries. The analysis suggests that more rapid mobilization by governments stabilizes a greater ethnic cleavage. This result is found to be robust by changing parameters and by modifying the specifications of the model. In addition, the analysis presents two other implications. The first is that a spiraling fragmentation of identity might occur if governments fail to accommodate people. The second is that in an age of advanced communication, governments need more assimilative power than before in order to secure integration. The analysis suggests that future research about identity formation in countries should consider the rigidity as well as the flexibility of identity.

Growing Food Safety from the Bottom Up: An Agent-Based Model of Food Safety Inspections

Sara McPhee-Knowles
Journal of Artificial Societies and Social Simulation 18 (2) 9

Kyeywords: Agent-Based Modeling, Search, Food Safety, Inspection, Policy
Abstract: The overall burden of foodborne illness is unknown, in part because of under-reporting and limited surveillance. Although the morbidity associated with foodborne illness is lower than ever, public risk perception and an increasingly complex food supply chain contribute to uncertainty in the food system. This paper presents an agent-based model of a simple food safety system involving consumers, inspectors and stores, and investigates the effect of three different inspection scenarios incorporating access to information. The increasing complexity of the food supply chain and agent-based modeling as an appropriate method for this line of investigation from a policy perspective are discussed.

Agile Development of an Attitude-Behaviour Driven Simulation of Alcohol Consumption Dynamics

Daniel Moyo, Abdallah K. Ally, Alan Brennan, Paul Norman, Robin C. Purshouse and Mark Strong
Journal of Artificial Societies and Social Simulation 18 (3) 10

Kyeywords: Agile, Agent-Based, Alcohol, Attitudes, Microsimulation, Modelling
Abstract: Whilst there have been several advocates for the application of software engineering (SE) methodologies in the development of agent-based models and simulations in the social sciences, the uptake of these techniques in the research community has been limited – or if authors are using such techniques, their use is underreported. Software engineering provides structured processes and techniques for designing, documenting, implementing and testing computer software. Software processes have many variations, each with their own unique advantages and disadvantages depending on the constraints (such as: human resources, time, finance, quality) facing a project team. This paper sets out the methods of Scrum agile software development, and discusses the experience of using Scrum to organise workflow and guide the development of an agent-based model of alcohol consumption. By employing Scrum in conjunction with another software engineering method, the Unified Modelling Language, this paper represents a case study in SE methods applied to a real world research problem.

Repast Simphony Statecharts

Jonathan Ozik, Nicholson Collier, Todd Combs, Charles M. Macal and Michael North
Journal of Artificial Societies and Social Simulation 18 (3) 11

Kyeywords: Agent-Based Modeling, Statecharts, Agent-Based Social Simulation, Repast Simphony, Software Engineering Processes
Abstract: Agent states and transitions between states are important abstractions in agent-based social simulation (ABSS). Although it is common to develop ad hoc implementations of state-based and transition-based agent behaviors, “best practice” software engineering processes provide transparent and formally grounded design notations that translate directly into working implementations. Statecharts are a software engineering design methodology and an explicit visual and logical representation of the states of system components and the transitions between those states. Used in ABSS, they can clarify a model’s logic and allow for efficient software engineering of complex state-based models. In addition to agent state and behavioral logic representation, visual statecharts can also be useful for monitoring agent status during a simulation, quickly conveying the underlying dynamics of complex models as a simulation evolves over time. Visual approaches include drag-and-drop editing capabilities for constructing state-based models of agent behaviors and conditions for agent state transitions. Repast Simphony is a widely used, open source, and freely accessible agent-based modeling toolkit. While it is possible for Repast Simphony users to create their own implementations of state-based agent behaviors and even create dynamic agent state visualizations, the effort involved in doing so is usually prohibitive. The new statecharts framework in Repast Simphony, a subset of Harel’s statecharts, introduces software engineering practices through the use of statecharts that directly translate visual representations of agent states and behaviors into software implementations. By integrating an agent statecharts framework into Repast Simphony, we have made it easier for users at all levels to take advantage of this important modeling paradigm. Through the visual programming that statecharts afford, users can effectively create the software underlying agents and agent-based models. This paper describes the development and use of the free and open source Repast Simphony statecharts capability for developing ABSS models.

A Call to Arms: Standards for Agent-Based Modeling and Simulation

Andrew Collins, Mikel Petty, Daniele Vernon-Bido and Solomon Sherfey
Journal of Artificial Societies and Social Simulation 18 (3) 12

Kyeywords: Agent-Based Modeling and Simulation, Standards, Standardization, Standards Development Organization, ODD, Simulation Methods
Abstract: Standards are as old as civilization itself and they are vital to human development. Standards touch almost every part of our lives, from the water we drink to the language used to write this article. A sign of a good standard is one that we do not notice. Good standards exist and so do processes and organizations to create and maintain them. As agent-based modeling and simulation matures as a methodology, a discussion of standards applicable to it becomes increasingly important. Descriptive standards for agent-based models, such as the Overview, Design concepts, and Details protocol and agent-based extensions to the Unified Modeling Language, have already begun to emerge. Software tools for implementing such models, such as Netlogo and Repast Simphony, are increasingly well-known and have the potential to become de facto standards among the wider scientific community for agent-based simulation. Based on the findings of a series of workshops that brought together experts throughout the modeling and simulation community, we argue that agent-based modeling and simulation is no different from the other emerging technical subjects in the sense that standards, both existing and new, may be applicable to it, and that the community should both adopt existing standards that are relevant and exploit the already existing standards processes and organizations to develop new ones.

Engineering Agent-Based Social Simulations: An Introduction

Peer-Olaf Siebers and Paul Davidsson
Journal of Artificial Societies and Social Simulation 18 (3) 13

Kyeywords: Agent-Based Social Simulation, Software Engineering, Software Architectures, UML
Abstract: This special section on "Engineering Agent-Based Social Simulations" aims to represent the current state of the art in using Software Engineering (SE) methods in ABSS. It includes a mixture of theoretically oriented papers that describe frameworks, notations and methods adapted from SE and practice-oriented papers that demonstrate the application of SE methods in real world ABSS projects.

Evidence Based and Conceptual Model Driven Approach for Agent-Based Policy Modelling

Sabrina Scherer, Maria Wimmer, Ulf Lotzmann, Scott Moss and Daniele Pinotti
Journal of Artificial Societies and Social Simulation 18 (3) 14

Kyeywords: Model-Driven Development, Agent-Based Policy Models, Annotation of Policy Models, Conceptual Models, Social Simulation Models, Provenance Information
Abstract: Agent-based policy modelling is an application of agent-based social simulation. In this contribution it is applied to strategic policy making in the public sector. Open government principles relevant in this domain demand solutions that trace the origins of modelling decisions from narrative texts (background documents and stakeholder scenarios) through the whole policy modelling process up to the simulation results. With the help of such traces, decisions made on the basis of such simulation results are more transparent and comprehensible. This paper presents a conceptual model-driven approach developed and implemented in the OCOPOMO project. The approach ensures traceability by integrating technologies for agent-based social simulation, semantic web and model-driven development. Narrative texts are transferred into Consistent Conceptual Description (CCD) models. Those CCD models are transferred semi-automatically into formal policy models implemented in the DRAMS (Declarative Rule-based Agent Modelling System) language. These formal policy models are further elaborated (i.e. the policy modeller has still full flexibility in programming the model), and runnable simulation models are programmed. From the simulation logs, model-based scenarios are generated to interpret and support a better understanding of simulation results. The model-based scenarios are textual narratives with charts summarising the output produced by the simulation runs. Thereby passages in these texts are linked with documents containing original narrative scenarios. These traces are realised via the CCD models. A well-elaborated policy modelling process and a software toolbox support the approach. A pilot case exemplifies the application of the process and the toolbox. Evaluation results from the OCOPOMO project show benefits as well as limitations of the approach. We also reflect how the process and toolbox can be transferred into other application domains.

Intervention Strategies and the Diffusion of Collective Behavior

Hai-hua Hu, Jun Lin and Wen-tian Cui
Journal of Artificial Societies and Social Simulation 18 (3) 16

Kyeywords: Intervention Strategy, Diffusion of Collective Behavior, Social Network, Agent-Based Modeling
Abstract: This paper examines the intervention strategies for the diffusion of collective behavior, such as promoting innovation adoption and repressing a strike. An intervention strategy refers to controlling the behaviors of a small number of individuals in terms of their social or personal attributes, including connectivity (i.e., the number of social ties one holds), motivation (i.e., an individual’s intrinsic cost–benefit judgment on behavior change), and sensitivity (i.e., the degree to which one follows others). Extensive agent-based simulations demonstrate that the optimal strategy fundamentally depends on the goal and time of intervention. Moreover, the nature of the social network (determined by homophily type and level) moderates the effectiveness of a strategy. These results have substantial implications for the design and evaluation of intervention programs.

Multi-Agent Based Simulation of Organizational Routines on Complex Networks

Dehua Gao, Xiuquan Deng, Qiuhong Zhao, Hong Zhou and Bing Bai
Journal of Artificial Societies and Social Simulation 18 (3) 17

Kyeywords: Organizational Routines, Connections, Complex Networks, Multiple Actors, Individual Habits, Multi-Agent Based Simulation
Abstract: Organizational routines are collective phenomena involving multiple individual actors. They are crucial in helping to understand how organizations behave and change in a certain period. In this paper, by regarding the individual habits of multiple actors involved as fundamental building blocks, we consider organizational routines from an ‘emergence-based’ perspective. We emphasise the impacts of connections or network topologies among individual actors in the formation of organizational routines, and carry out a multi-agent based simulation analysis of organizational routines on complex networks. We consider some important factors such as inertia resulted from individual memories, component complexity of organizational tasks, turnover of individual actors, the impacts of both heterogeneity and improvisation of individual actors involved, and the dynamical properties of the network topologies within which individual actors are located. The results of our research show that network topologies among individual actors do determine the dynamic characteristics of organizational routines. Although the fact is that the mechanisms beneath this are also influenced by some main factors like the memory capacity of individual actors and the component complexity of organizational tasks that these individual actors should deal with repetitively, and that the total costs for the organization to bear during their implementation of organizational tasks are variant, the routine system on scale-free networks can always have a better performance, and obtain a much higher coherency and routinization level of collective behaviours, even in the case of turnover of individual actors. In addition, when individual actors involved are heterogeneous, the routine system on scale-free networks would also exhibit a strong anti-disturbance ability, no matter whether there are minor improvisations from these individual actors or not. Nevertheless, a large number of improvisations enable individual actors to act in some more individualistic manners, and destroy the routine system as a result.

Considering a Multi-Level Model as a Society of Interacting Models: Application to a Collective Motion Example

Benjamin Camus, Christine Bourjot and Vincent Chevrier
Journal of Artificial Societies and Social Simulation 18 (3) 7

Kyeywords: Multi-Level, Multi-Model, Multi-Agent, Collective Motion
Abstract: As they involve relationships between interacting individuals and groups, social systems can be described at different levels of resolution. In a number of modeling cases, only one of these levels is explicitly represented. In order to study phenomena where both individual and collective representations are needed, multi-level modeling is a good approach as it explicitly represents these different levels. We propose to consider a multi-level representation from a multi-modeling point of view. This perspective allows explicitly specifying the level’s relationships and, therefore, to test hypothesis about interaction between individuals and groups in social systems. We define a framework to better specify the concepts used in multi-level modeling and their relationships. This framework is implemented through the AA4MM meta-model, which benefits from a middleware layer. This meta-model uses the multi-agent paradigm to consider a multi-model as a society of interacting models. We extend this meta-model to consider multi-level modeling, and present a proof of concept of a collective motion example, where we show the advantages of this approach for the study of social phenomena.

Fuzzy Logic for Social Simulation Using NetLogo

Luis R. Izquierdo, Doina Olaru, Segismundo S. Izquierdo, Sharon Purchase and Geoffrey N. Soutar
Journal of Artificial Societies and Social Simulation 18 (4) 1

Kyeywords: Fuzzy Logic, NetLogo, Social Simulation, Agent-Based Modelling, Mamdani Inference, IF-THEN Rule
Abstract: Fuzzy Logic is a framework particularly useful to formalise and deal with imprecise concepts and statements expressed in natural language. This paper has three related aims. First, it aims to provide a short introduction to the basics of Fuzzy Logic within the context of social simulation. Secondly, it presents a well-documented NetLogo extension that facilitates the use of Fuzzy Logic within NetLogo. Finally, by providing a concrete example, it shows how researchers can use the Fuzzy Logic extension to build agent-based models in which individual agents hold their own fuzzy concepts and use their own fuzzy rules, which may also change over time. We argue that Fuzzy Logic and the tools provided here can be useful in Social Simulation in different ways. For example, they can assist in the process of analysing the robustness of a certain social theory expressed in natural language to different specifications of the imprecise concepts that the theory may contain (such as e.g. “wealthy”, “poor” or “disadvantaged”). They can also facilitate the exploration of the effect that heterogeneity in concept interpretations may have in a society (i.e. the significance of the fact that different people may have different interpretations of the same concept). Thus, this paper and the tools included in it can make the endeavour of translating social theories into computer programs easier and more rigorous at the same time.

Agent-Based Simulation of Time to Decide: Military Commands and Time Delays

Woo-Seop Yun, Il-Chul Moon and Tae-Eog Lee
Journal of Artificial Societies and Social Simulation 18 (4) 10

Kyeywords: Command and Control (C2), Combat Effectiveness, Infantry Company Engagement, Agent-Based Simulation
Abstract: Modelling command and control (C2) is regarded as a difficult task because of the complexity of the decision-making required by individuals in combat. Despite the difficulties, C2 modelling is frequently used for high echelon units, i.e. battalion, division and above. This paper extends these models to the lowest army unit: the infantry company. Previous studies have modelled this particular unit as either an abstract entity or a detailed behaviour model without C2. Our model includes C2 in the models to determine the most critical tasks at company level C2 because this information could direct company commanders to engage in more important operational tasks. Our analysis is based on agent-based modelling and the virtual experiment framework. The overall model includes operational details as discrete event models and soldier behaviour as behavioural models. Our analytical results enable us to identify the key C2 tasks of company commanders and the changes in the importance of various operational environments.

Pathways to Truth: Comparing Different Upscaling Options for an Agent-Based Sector Model

Albert Zimmermann, Anke Möhring, Gabriele Mack, Ali Ferjani and Stefan Mann
Journal of Artificial Societies and Social Simulation 18 (4) 11

Kyeywords: Agent-Based Model, Agriculture, FADN, Extrapolation
Abstract: Simulation results can be highly sensitive to the way agents are upscaled to a larger organizational and spatial level. This paper tests an ex-post validation method for forecasting models by using old base years and forecasting into recent years for which observed data is already available. Our case in point is a comparison between different upscaling methods in the agent-based agricultural sector model SWISSland. It is shown that individual-farm extrapolation factors strongly enhance alignment with the total population in the base year. However, they may cause inconsistencies in those agent-based models in which relations between the farms are an important part. Therefore, an adjustment of the sample by making almost no use of some farms whilst making highly disproportionate use of others turned out to be the most suitable method for the SWISSland model.

Degrees of Separation, Social Learning, and the Evolution of Cooperation in a Small-World Network

Nicholas Seltzer and Oleg Smirnov
Journal of Artificial Societies and Social Simulation 18 (4) 12

Kyeywords: Cooperation, Social Networks, Small-World, Modern Society, Simulation, Agent-Based
Abstract: We analyze a novel agent-based model of a social network in which agents make contributions to others conditional upon the social distance, which we measure in terms of the “degrees of separation” between the two players. On the basis of a simple imitation model, the emerging strategy profile is characterized by high levels of cooperation with those who are directly connected to the agent and lower but positive levels of cooperation with those who are indirectly connected to the agent. Increasing maximum interaction distance decreases cooperation with close neighbors but increases cooperation with distant neighbors for a net negative effect. On the other hand, allowing agents to learn and imitate socially distant neighbors increases cooperation for all types of interaction. Combining greater interaction distance with greater learning distance leads to a positive change in the total social welfare produced by the agents’ contributions.

An Agent-Based Model to Assess the Attractiveness of Industrial Estates

Fernando Fonseca, Rui António Rodrigues Ramos and Antônio Nélson Rodrigues da Silva
Journal of Artificial Societies and Social Simulation 18 (4) 13

Kyeywords: Industrial Estates, Agent-Based Models, Firms
Abstract: This article describes the main approaches adopted in a study focused on planning industrial estates on a subregional scale. The study was supported by an agent-based model, using firms as agents to assess the attractiveness of industrial estates. The simulation was made by the NetLogo toolkit and the environment represents a geographical space. Three scenarios and four hypotheses were used in the simulation to test the impact of different policies on the attractiveness of industrial estates. Policies were distinguished by the level of municipal coordination at which they were implemented and by the type of intervention. In the model, the attractiveness of industrial estates was based on the level of facilities, amenities, accessibility and on the price of land in each industrial estate. Firms are able to move and relocate whenever they find an attractive estate. The relocating firms were selected by their size, location and distance to an industrial estate. Results show that a coordinated policy among municipalities is the most efficient policy to promote advanced-qualified estates. In these scenarios, it was observed that more industrial estates became attractive, more firms were relocated and more vacant lots were occupied. Furthermore, the results also indicate that the promotion of widespread industrial estates with poor-quality infrastructures and amenities is an inefficient policy to attract firms.

The Effects of Network Structure on the Emergence of Norms in Adaptive Populations

Peter Revay
Journal of Artificial Societies and Social Simulation 18 (4) 14

Kyeywords: Social Norms, Agent-Based Modeling, Social Networks, Neighborhood Structure, Cooperation
Abstract: The different ways individuals socialize with others affect the conditions under which social norms are able to emerge. In this work an agent-based model of cooperation in a population of adaptive agents is presented. The model has the ability to implement a multitude of network topologies. The agents possess strategies represented by boldness and vengefulness values in the spirit of Axelrod's (1986) norms game. However, unlike in the norms game, the simulations abandon the evolutionary approach and only follow a single-generation of agents who are nevertheless able to adapt their strategies based on changes in their environment. The model is analyzed for potential emergence or collapse of norms under different network and neighborhood configurations as well as different vigilance levels in the agent population. In doing so the model is found able to exhibit interesting emergent behavior suggesting potential for norm establishment even without the use of so-called metanorms. Although the model shows that the success of the norm is dependent on the neighborhood size and the vigilance of the agent population, the likelihood of norm collapse is not monotonically related to decreases in vigilance.

Combining Segregation and Integration: Schelling Model Dynamics for Heterogeneous Population

Erez Hatna and Itzhak Benenson
Journal of Artificial Societies and Social Simulation 18 (4) 15

Kyeywords: Schelling Model, Ethnic Segregation, Residential Dynamics, Heterogeneous Agents
Abstract: The Schelling model is a simple agent-based model that demonstrates how individuals’ relocation decisions can generate residential segregation in cities. Agents belong to one of two groups and occupy cells of rectangular space. Agents react to the fraction of agents of their own group within the neighborhood around their cell. Agents stay put when this fraction is above a given tolerance threshold but seek a new location if the fraction is below the threshold. The model is well-known for its tipping point behavior: an initially random (integrated) pattern remains integrated when the tolerance threshold is below 1/3 but becomes segregated when the tolerance threshold is above 1/3. In this paper, we demonstrate that the variety of the Schelling model’s steady patterns is richer than the segregation–integration dichotomy and contains patterns that consist of segregated patches of each of the two groups, alongside areas where both groups are spatially integrated. We obtain such patterns by considering a general version of the model in which the mechanisms of the agents' interactions remain the same, but the tolerance threshold varies between the agents of both groups. We show that the model produces patterns of mixed integration and segregation when the tolerance threshold of an essential fraction of agents is either low, below 1/5, or high, above 2/3. The emerging mixed patterns are relatively insensitive to the model’s other parameters.

"Anarchy" Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012

Simon Angus and Behrooz Hassani-Mahmooei
Journal of Artificial Societies and Social Simulation 18 (4) 16

Kyeywords: Agent Based Modelling, Social Sciences, Simulation, Publishing
Abstract: Agent Based Modelling (ABM), a promising scientific toolset, has received criticism from some, in part, due to a claimed lack of scientific rigour, especially in the communication of its methods and results. To test the veracity of these claims, we conduct a structured analysis of over 900 scientific objects (figures, tables, or equations) that arose from 128 ABM papers published in the Journal of Artificial Societies and Social Simulation (JASSS), during the period 2001 to 2012 inclusive. Regrettably, we find considerable evidence in support of the detractors of ABM as a scientific enterprise: elementary plotting attributes are left off more often than not; basic information such as the number of replicates or the basis behind a particular statistic are not included; and few, if any, established methodological communication standards are apparent. In short, 'anarchy reigns'. Whilst the study was confined only to ABM papers of JASSS, we conclude that if the ABM community wishes its approach to be accepted further afield, authors, reviewers, and editors should take the results of our work as a wake-up call.

The Complexities of Agent-Based Modeling Output Analysis

Ju-Sung Lee, Tatiana Filatova, Arika Ligmann-Zielinska, Behrooz Hassani-Mahmooei, Forrest Stonedahl, Iris Lorscheid, Alexey Voinov, Gary Polhill, Zhanli Sun and Dawn C. Parker
Journal of Artificial Societies and Social Simulation 18 (4) 4

Kyeywords: Agent-Based Modeling, Methodologies, Statistical Test, Sensitivity Analysis, Spatio-Temporal Heterogeneity, Visualization
Abstract: The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analyzing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.

Predicting Self-Initiated Preventive Behavior Against Epidemics with an Agent-Based Relative Agreement Model

Liang Mao
Journal of Artificial Societies and Social Simulation 18 (4) 6

Kyeywords: Self-Initiated Behavior, Infectious Diseases, Agent-Based Modeling, Relative Agreement Rules, Social Network
Abstract: Human self-initiated behavior against epidemics is recognized to have significant impacts on disease spread. A few epidemic models have incorporated self-initiated behavior, and most of them are based on a classic population-based approach, which assumes a homogeneous population and a perfect mixing pattern, thus failing to capture heterogeneity among individuals, such as their responsive behavior to diseases. This article proposes an agent-based model that combines epidemic simulation with a relative agreement model for individual self-initiated behavior. This model explicitly represents discrete individuals, their contact structure, and most importantly, their progressive decision making processes, thus characterizing individualized responses to disease risks. The model simulation and sensitivity analysis show the existence of critical points (threshold values) in the model parameter space to control influenza epidemic including minimum values for the initially positive population size, the communication rate, and the attitude uncertainty. These threshold effects shed insights on preventive strategy design to deal with the current circumstances that new vaccines are often insufficient to combat emerging communicable diseases.

Emergence and Collapse of the Norm of Resource Sharing Around Locally Abundant Resources

Shiro Horiuchi
Journal of Artificial Societies and Social Simulation 18 (4) 7

Kyeywords: Agent Based Model, Resources, Norms, Hawk-Dove-Bourgeois Game
Abstract: How do individuals resolve conflicts over resources? One way is to share resources, which is possible between known individuals, with the use of sanctions on free riders or by partner selection. Another way is for anonymous individuals to respect the finders’ ownership of resources based on asymmetry and avoid conflicts over resources. This study elucidates the conditions under which anonymous individuals share resources with each other irrespective of their asymmetry with regard to resources. High resource values inhibit anonymous individuals from sharing resources; however, small cumulative values and local distributions let anonymous individuals share the resources. Punishment of the richest individuals also supports resource sharing. These conditions may represent resource sharing among anonymous individuals in periods of great disasters and may be the origin of the practice of exchange in prehistoric times.

Modeling Pre-European Contact Coast Salish Seasonal Social Networks and Their Impacts on Unbiased Cultural Transmission

Adam Rorabaugh
Journal of Artificial Societies and Social Simulation 18 (4) 8

Kyeywords: Cultural Transmission, Seasonal Mobility, Complex Foragers, Agent-Based Modeling, Social Networks, Cultural Drift
Abstract: Understanding the relationships between seasonal social networks and diversity in artifact styles, is crucial for examining the production and reproduction of knowledge among complex foraging societies such as those of the Pacific Northwest Coast. This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission). The results of these simulations suggest that the relationship between learning opportunities and innovation rate has more impact on artifact style richness and evenness than seasonal social networks. Seasonal aggregation does appear to result in a higher amount of one-off rare variants, but this effect is not statistically significant. Overall, the restriction of learning opportunities appears more crucial in patterning cultural diversity among complex foragers than the potential impacts from individuals drawing on different seasonal social networks.

SimDrink: An Agent-Based NetLogo Model of Young, Heavy Drinkers for Conducting Alcohol Policy Experiments

Nick Scott, Michael Livingston, Aaron Hart, James Wilson, David Moore and Paul Dietze
Journal of Artificial Societies and Social Simulation 19 (1) 10

Kyeywords: Agent-Based Model, NetLogo, Alcohol, Night-Time Economy, Heavy Drinking, SimDrink
Abstract: Aggression and other acute harms experienced in the night-time economy are topics of significant public health concern. Although policies to minimise these harms are frequently proposed, there is often little evidence available to support their effectiveness. In particular, indirect and displacement effects are rarely measured. This paper describes a proof-of-concept agent-based model ‘SimDrink’, built in NetLogo, which simulates a population of 18-25 year old heavy alcohol drinkers on a night out in Melbourne to provide a means for conducting policy experiments to inform policy decisions. The model includes demographic, setting and situational-behavioural heterogeneity and is able to capture any unintended consequences of policy changes. It consists of individuals and their friendship groups moving between private, public-commercial (e.g. nightclub) and public-niche (e.g. bar, pub) venues while tracking their alcohol consumption, spending and whether or not they experience consumption-related harms (i.e. drink too much), are involved in verbal violence, or have difficulty getting home. When compared to available literature, the model can reproduce current estimates for the prevalence of verbal violence experienced by this population on a single night out, and produce realistic values for the prevalence of consumption-related and transport-related harms. Outputs are robust to variations in underlying parameters. Further work with policy makers is required to identify several specific proposed harm reduction interventions that can be virtually implemented and compared. This will allow evidence based decisions to be made and will help to ensure any interventions have their intended effects.

Long Term Impacts of Bank Behavior on Financial Stability. an Agent Based Modeling Approach

Ilker Arslan, Eugenio Caverzasi, Mauro Gallegati and Alper Duman
Journal of Artificial Societies and Social Simulation 19 (1) 11

Kyeywords: Agent Based Modeling, Credit Networks, Financial Stability
Abstract: This paper presents an agent-based model aiming to shed light on the potential destabilizing effects of bank behavior. Our work takes its motivation from the effects of the financial crisis which erupted in 2007 in the US. It draws on the Financial Instability Hypothesis by Hyman P. Minsky, and on the Agent Based macro modeling literature (Delli Gatti et al. 2010, Riccetti et. al 2013) to model a simplified economy in which heterogeneous banks and firms interact on game theoretic rules. Simulation results suggest that aggregate financial instability may emerge as the outcome of banks’ attempt to increase their profit or market share through their pricing strategies. A further finding from the model is the need for banks to take into account time consistency when issuing credit in order to protect the financial stability of the system.

Hybrid Agent Modeling in Population Simulation: Current Approaches and Future Directions

Pei-jun Ye, Xiao Wang, Cheng Chen, Yue-tong Lin and Fei-Yue Wang
Journal of Artificial Societies and Social Simulation 19 (1) 12

Kyeywords: Agent Modeling, Population Synthesis, Survey
Abstract: Agent based population is fundamental to the micro-simulation of various dynamic urban eco- and social systems. This paper surveys the basic theories and methods of hybrid agent modeling in population simulation emerged recent years. We first introduce the framework of this hybrid agent based population generation. The current approaches of initial database synthesis including sample based and sample free methods are then discussed in detail, followed by a brief comparison of these methods. In addition, the major types and characteristics of agent models and their applications are also reviewed. Finally, we conclude by outlining the future directions of population research using agent technology

Exploring Homeowners’ Insulation Activity

Jonas Friege, Georg Holtz and Émile Chappin
Journal of Artificial Societies and Social Simulation 19 (1) 4

Kyeywords: Spatial Agent-Based Model, Decision-Making Process, Homeowners, Thermal Insulation, Situational Factors, Social Interaction
Abstract: Insulating existing buildings offers great potential for reducing greenhouse gas emissions and meeting Germany’s climate protection targets. Previous research suggests that, since homeowners’ decision-making processes are inadequately understood as yet, today’s incentives aiming at increasing insulation activity lead to unsatisfactory results. We developed an agent-based model to foster the understanding of homeowners’ decision-making processes regarding insulation and to explore how situational factors, such as the structural condition of houses and social interaction, influence their insulation activity. Simulation experiments allow us furthermore to study the influence of socio-spatial structures such as residential segregation and population density on the diffusion of renovation behavior among homeowners. Based on the insights gained, we derive recommendations for designing innovative policy instruments. We conclude that the success of particular policy instruments aiming at increasing homeowners’ insulation activity in a specific region depends on the socio-spatial structure at hand, and that reducing financial constraints only has a relatively low potential for increasing Germany’s insulation rate. Policy instruments should also target the fact that specific renovation occasions are used to undertake additional insulation activities, e.g. by incentivizing lenders and craftsmen to advise homeowners to have insulation installed.

A Data-Driven Approach for Agent-Based Modeling: Simulating the Dynamics of Family Formation

Mazhar Sajjad, Karandeep Singh, Euihyun Paik and Chang-Won Ahn
Journal of Artificial Societies and Social Simulation 19 (1) 9

Kyeywords: Data-Driven, Agent-Based Model, Family Formation, Socioeeconomic Status
Abstract: In this paper, we propose a data-driven agent-based modeling approach that boosts the strength of agent-based models (ABM) in the dynamics of family formation. The proposed model analyzes the impact of socioeconomic factors on individual decisions about family formations. The key features of our model are the heterogeneous nature regarding agent’s age and socioeconomic factors: income and education. Based on these attributes, agents take decisions about acceptable partners and transition to family formation. One of our objectives is to fill the gap that exists between the methodologies of demography and agent-based social simulation. Making such a connection between these two approaches, this model attempts to incorporate empirical data into agent-based social simulation which enables us to analyze the transition of family formation effectively. Further, our simulated results depict the patterns of the hazards of family formation that are observed at micro-level dynamics and explains how marriage patterns change overtime. The proposed work gives a strong insight to strengthen the extent of demographic analysis through data-driven agent-based approach.

TreatMethHarm: An Agent-Based Simulation of How People Who Use Methamphetamine Access Treatment

Francois Lamy, Brendan Quinn, Robyn Dwyer, Nicola Thomson, David Moore and Paul Dietze
Journal of Artificial Societies and Social Simulation 19 (2) 3

Kyeywords: Agent-Based Modelling, Methamphetamine Use, Drug-Related Harms, Treatment Access, Drug Career
Abstract: Methamphetamine use in Australia has recently attracted considerable attention due to increased human and social costs. Despite evidences indicating increasing methamphetamine-related harm and significant numbers of frequent and dependent users, methamphetamine treatment coverage remains low in Australia. This paper aims to investigate the complex interplay between methamphetamine use and treatment-related access by designing an agent-based model, using epidemiological data and expert-derived assumptions. This paper presents the architecture and core mechanisms of an agent-based model, TreatMethHarm, and details the results of model calibration performed by testing the key model parameters. At this stage of development, TreatMethHarm is able to produce proportions of methamphetamine users that replicate those produced by our epidemiological survey. However, this agent-based model still requires additional information and further tests before validation. TreatMethHarm provides a useful tool to elicit dialogue between researchers from different disciplines, integrate a variety of data and identify missing information.

Simulating the Transmission of Foot-And-Mouth Disease Among Mobile Herds in the Far North Region, Cameroon

Hyeyoung Kim, Ningchuan Xiao, Mark Moritz, Rebecca Garabed and Laura W. Pomeroy
Journal of Artificial Societies and Social Simulation 19 (2) 6

Kyeywords: Foot-And-Mouth Disease (FMD), Mobility, Disease Transmission, Transhumance, SIR Model, Agent-Based-Model (ABM)
Abstract: Animal and human movements can impact the transmission of infectious diseases. Modeling such impacts presents a significant challenge to disease transmission models because these models often assume a fully mixing population where individuals have an equal chance to contact each other. Whereas movements result in populations that can be best represented as a dynamic networks whose structure changes over time as individual movements result in changing distances between individuals within a population. We model the impact of the movements of mobile pastoralists on foot-and-mouth disease (FMD) transmission in a transhumance system in the Far North Region of Cameroon. The pastoralists in our study area move their livestock between rainy and dry season pastures. We first analyzed transhumance data to derive mobility rules that can be used to simulate the movements of the agents in our model. We developed an agent-based model coupled with a susceptible–infected–recovered (SIR) model. Each agent represents a camp of mobile pastoralists with multiple herds and households. The simulation results demonstrated that the herd mobility significantly influenced the dynamics of FMD. When the grazing area is not explicitly considered (by setting the buffer size to 100 km), all the model simulations suggested the same curves as the results using a fully mixing population. Simulations that used grazing areas observed in the field (≤5 km radius) resulted in multiple epidemic peaks in a year, which is similar to the empirical evidence that we obtained by surveying herders from our study area over the last four years.

Learning with Communication Barriers Due to Overconfidence. What a "Model-To-Model Analysis" Can Add to the Understanding of a Problem

Juliette Rouchier and Emily Tanimura
Journal of Artificial Societies and Social Simulation 19 (2) 7

Kyeywords: Collective Learning, Agent-Based Simulation, M2M, Influence Model, Analytical Model, Over-Confidence
Abstract: In this paper, we describe a process of validation for an already published model, which relies on the M2M paradigm of work. The initial model showed that over-confident agents, which refuse to communicate with agents whose beliefs differ, disturb collective learning within a population. We produce an analytical model based on probabilistic analysis, that enables us to explain better the process at stake in our first model, and demonstrates that this process is indeed converging. To make sure that the convergence time is meaningful for our question (not just for an infinite number of agents living for an infinite time), we use the analytical model to produce very simple simulations and assess that the result holds in finite contexts.

Revising the Human Development Sequence Theory Using an Agent-Based Approach and Data

Viktoria Spaiser and David J. T. Sumpter
Journal of Artificial Societies and Social Simulation 19 (3) 1

Kyeywords: Agent-Based Simulation, Human Development Sequence Theory, Democratisation, Mathematical Modeling, Data Analysis, Inequality
Abstract: Agent-based models and computer simulations are promising tools for studying emergent macro-phenomena. We apply an agent-based approach in combination with data analysis to investigate the human development sequence (HDS) theory developed by Ronald Inglehart and Christian Welzel. Although the HDS theory is supported by correlational evidence, the sequence of economic growth, democracy and emancipation stated by the theory is not entirely consistent with data. We use an agent-based model to make quantitative predictions about several different micro-level mechanisms. Comparison to data allows us to identify important inconsistencies between HDS and the data, and propose revised agent-based models that modify the theory. Our results indicate the importance of elites and economic inequality in explaining the data available on democratisation.

Oscillatory Patterns in the Amount of Demand for Dental Visits: An Agent Based Modeling Approach

Maryam Sadeghipour, Peyman Shariatpanahi, Afshin Jafari, Mohammad Hossein Khosnevisan and Arezoo Ebn Ahmady
Journal of Artificial Societies and Social Simulation 19 (3) 10

Kyeywords: Dental Health Care, Dental Routine Visit, Oscillatory Patterns, Agent Based Modeling, Google Trends
Abstract: There are some empirical evidences indicating that there is a collective complex oscillatory pattern in the amount of demand for dental visit at society level. In order to find the source of the complex cyclic behavior, we develop an agent-based model of collective behavior of routine dental check-ups in a social network. Simulation results show that demand for routine dental check-ups can follow an oscillatory pattern and the pattern’s characteristics are highly dependent upon the structure of the social network of potential patients, the population, and the number of effective contacts between individuals. Such a cyclic pattern has public health consequences for patients and economic consequences for providers. The amplitude of oscillations was analyzed under different scenarios and for different network topologies. This allows us to postulate a simulation-based theory for the likelihood observing and the magnitude of a cyclic demand. Results show that in case of random networks, as the number of contacts increases, the oscillatory pattern reaches its maximum intensity, for any population size. In case of ring lattice networks, the amplitude of oscillations reduces considerably, when compared to random networks, and the oscillation intensity is strongly dependent on population. The results for small world networks is a combination of random and ring lattice networks. In addition, the simulation results are compared to empirical data from Google Trends for oral health related search queries in different United States cities. The empirical data indicates an oscillatory behavior for the level of attention to dental and oral health care issues. Furthermore, the oscillation amplitude is correlated with town’s population. The data fits the case of random networks when the number of effective contacts is about 4-5 for each person. These results suggest that our model can be used for a fraction of people deeply involved in Internet activities like Web-based social networks and Google search.

Exploring the Combined Effect of Factors Influencing Commuting Patterns and CO2 Emissions in Aberdeen Using an Agent-Based Model

Jiaqi Ge and Gary Polhill
Journal of Artificial Societies and Social Simulation 19 (3) 11

Kyeywords: Agent-Based Model, Commuting, CO2 Emissions, Flexitime, Urban Concentration
Abstract: This paper develops an agent-based model of the daily commute in Aberdeen City and the surrounding area in Scotland, UK. We study the impact of flexitime work arrangements, urban concentration, a new bypass, and cycle lanes on commute time length, reliability and CO2 emissions, and analyse the diverse conflation of these factors and the different connections of them in order to detect their cumulative effects. Our results suggest that flexitime will reduce CO2 emissions from traffic. It also reduces mean commute time and makes commute time more reliable. We find that although higher urban concentration will make travel time less reliable, it will reduce CO2 emissions from commuting and cut commute time length. There might also be a trade-off between travel time length and reliability regarding urban concentration. We show that the new bypass will only reduce mean commute time by a small amount, while slightly increasing total CO2 emissions. Finally, we find that cyclists sharing roads with cars do not necessarily slow down the traffic on the whole. We conclude that infrastructural, social and urban issues should never be studied in isolation with each other, and that urban policies will have ramifications for both urban and surrounding ex-urban areas.

Enhancing Agent-Based Models with Discrete Choice Experiments

Stefan Holm, Renato Lemm, Oliver Thees and Lorenz M. Hilty
Journal of Artificial Societies and Social Simulation 19 (3) 3

Kyeywords: Agent-Based Modeling, Discrete Choice Experiments, Preference Elicitation, Decision Model, Market Simulation, Wood Market
Abstract: Agent-based modeling is a promising method to investigate market dynamics, as it allows modeling the behavior of all market participants individually. Integrating empirical data in the agents’ decision model can improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experiments (DCEs) to enhance the empirical foundation of ABMs. The DCE method is based on random utility theory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combined approach is applied to a case study of a roundwood market in Switzerland. We conducted DCEs with roundwood suppliers to quantitatively characterize the agents’ decision model. We evaluate our approach using a fitness measure and compare two DCE evaluation methods, latent class analysis and hierarchical Bayes. Additionally, we analyze the influence of the error term of the utility function on the simulation results and present a way to estimate its probability distribution.

VALFRAM: Validation Framework for Activity-Based Models

Jan Drchal, Michal Čertický and Michal Jakob
Journal of Artificial Societies and Social Simulation 19 (3) 5

Kyeywords: Agent-Based Modelling, Activity Based Model, Transport, Validation, Methodology, Simulation
Abstract: Activity-based models are a specific type of agent-based models widely used in transport and urban planning to generate and study travel demand. They deal with agents that structure their behaviour in terms of daily activity schedules: sequences of activity instances (such as work, sleep or shopping) with assigned start times, durations and locations, and interconnected by trips with assigned transport modes and routes. Despite growing importance of activity-based models in transport modelling, there has been no work focusing specifically on statistical validation of such models so far. In this paper, we propose a six-step Validation Framework for Activity-based Models (VALFRAM) that exploits historical real-world data to quantify the model's validity in terms of a set of numeric metrics. The framework compares the temporal and spatial properties and the structure of modelled activity schedules against real-world origin-destination matrices and travel diaries. We demonstrate the usefulness of the framework on a set of six different activity-based transport models.

The Emergence of Climate Change Mitigation Action by Society: An Agent-Based Scenario Discovery Study

Sebastiaan Greeven, Oscar Kraan, Émile Chappin and Jan H. Kwakkel
Journal of Artificial Societies and Social Simulation 19 (3) 9

Kyeywords: Agent-Based Modeling, Scenario Discovery, Uncertainty, Climate Change Mitigation, Exploratory Modeling
Abstract: Developing model-based narratives of society’s response to climate change is challenged by two factors. First, society’s response to possible future climate change is subject to many uncertainties. Second, we argue that society’s mitigation action emerge out of the actions and interactions of the many actors in society. Together, these two factors imply that the overarching dynamics of society’s response to climate change are unpredictable. In contrast to conventional processes of developing scenarios, in this study the emergence of climate change mitigation action by society has been represented in an agent-based model with which we developed two narratives of the emergence of climate change mitigation action by applying exploratory modelling and analysis. The agent-based model represents a two-level game involving governments and citizens changing their emission behaviour in the face of climate change through mitigation action. Insights gained from the exploration on uncertainties pertaining to the system have been used to construct two internally consistent and plausible narratives on the pathways of the emergence of mitigation action, which, as we argue, are a reasonable summary of the uncertainty space. The first narrative highlights how and when strong mitigation action emerges while the second narrative highlights how and when weak mitigation action emerges. In contrast to a conventional scenario development process, these two scenarios have been discovered bottom up rather than being defined top down. They succinctly capture the possible outcomes of the emergence of climate change mitigation by society across a large range of uncertain factors. The narratives therefore help in conveying the consequences of the various uncertainties influencing the emergence of climate change mitigation action by society.

Dynamics of Uncertain Opinion Formation: An Agent-Based Simulation

Haiming Liang, Yucheng Dong and Congcong Li
Journal of Artificial Societies and Social Simulation 19 (4) 1

Kyeywords: Opinion Formation, Uncertain Opinions, Uncertainty Tolerance, Communication Regime, Agent-Based Simulation
Abstract: Opinion formation describes the dynamics of opinions in a group of interaction agents and is a powerful tool for predicting the evolution and diffusion of the opinions. The existing opinion formation studies assume that the agents express their opinions by using the exact number, i.e., the exact opinions. However, when people express their opinions, sentiments, and support emotions regarding different issues, such as politics, products, and events, they often cannot provide the exact opinions but express uncertain opinions. Furthermore, due to the differences in culture backgrounds and characters of agents, people who encounter uncertain opinions often show different uncertainty tolerances. The goal of this study is to investigate the dynamics of uncertain opinion formation in the framework of bounded confidence. By taking different uncertain opinions and different uncertainty tolerances into account, we use an agent-based simulation to investigate the influences of uncertain opinions in opinion formation from two aspects: the ratios of the agents that express uncertain opinions and the widths of the uncertain opinions, and also provide the explanations of the observations obtained.

From Consumer Decision to Market Share – Unanimity of Majority?

Agnieszka Kowalska-Styczeń and Katarzyna Sznajd-Weron
Journal of Artificial Societies and Social Simulation 19 (4) 10

Kyeywords: Agent-Based Model, Word of Mouth Marketing, Cellular Automata, Consumer Behavior
Abstract: We use a general cellular automata model to study the consumer decision-making process. Within this general model we use three different rules governing word-of-mouth communication (w-o-m), one majority rule and two unanimity rules, and ask the question if differences between these three w-o-m rules, introduced on the microscopic level, will manifest on the macroscopic level. We show that in the model with the majority rule the neighborhood plays a significant role in terms of the market shares whereas movement (interpreted as seeking for information in other sources) is almost negligible. Exactly the opposite phenomena are observed for models in which unanimity, instead of majority, is needed to convince agents. We also introduce a modification of the unanimity rule, based on the Latane theory of the social influence, and show that on the macroscopic level this modification is indistinguishable from the simple unanimity rule. We conclude the paper with a recommendation which rules are more appropriate to model particular marketing phenomena.

A Heuristic Combinatorial Optimisation Approach to Synthesising a Population for Agent Based Modelling Purposes

Nam Huynh, Johan Barthelemy and Pascal Perez
Journal of Artificial Societies and Social Simulation 19 (4) 11

Kyeywords: Synthetic Population, Combinatorial Optimisation, Sample-Free, Agent Based Modelling, Social Behaviours
Abstract: This paper presents an algorithm that follows the sample-free approach to synthesise a population for agent based modelling purposes. This algorithm is among the very few in the literature that do not rely on a sample survey data to construct a synthetic population, and thus enjoy a potentially wider applications where such survey data is not available or inaccessible. Different to existing sample-free algorithms, the population synthesis presented in this paper applies the heuristics to part of the allocation of synthetic individuals into synthetic households. As a result the iterative process allocating individuals into households, which normally is the most computationally demanding and time consuming process, is required only for a subset of synthetic individuals. The population synthesiser in this work is therefore computational efficient enough for practical application to build a large synthetic population (many millions) for many thousands target areas at the smallest possible geographical level. This capability ensures that the geographical heterogeneity of the resulting synthetic population is best preserved. The paper also presents the application of the new method to synthesise the population for New South Wales in Australia in 2006. The resulting total synthetic population has approximately 6 million people living in over 2.3 million households residing in private dwellings across over 11000 Census Collection Districts. Analyses show evidence that the synthetic population matches very well with the census data across seven demographics attributes that characterise the population at both household level and individual level.

A Simple Agent-Based Spatial Model of the Economy: Tools for Policy

Bernardo Alves Furtado and Isaque Daniel Rocha Eberhardt
Journal of Artificial Societies and Social Simulation 19 (4) 12

Kyeywords: Modeling, Agent-Based Models, Public Finance, Taxes, Municipalities, Quality of Life
Abstract: This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The real estate market allows families to move to dwellings with higher quality or lower price when the families capitalize property values. The goods market allows consumers to search on a flexible number of firms choosing by price and proximity. The labor market entails a matching process between firms (given its location) and candidates, according to their qualification. The government may be configured into one, four or seven distinct sub-national governments, which are all economically conurbated. The role of government is to collect taxes on the value added of firms in its territory and invest the taxes into higher levels of quality of life for residents. The results suggest that the configuration of administrative boundaries is relevant to the levels of quality of life arising from the reversal of taxes. The model with seven regions is more dynamic, but more unequal and heterogeneous across regions. The simulation with only one region is more homogeneously poor. The study seeks to contribute to a theoretical and methodological framework as well as to describe, operationalize and test computer models of public finance analysis, with explicitly spatial and dynamic emphasis. Several alternatives of expansion of the model for future research are described. Moreover, this study adds to the existing literature in the realm of simple microeconomic computational models, specifying structural relationships between local governments and firms, consumers and dwellings mediated by distance.

Simulation of Technology Sourcing Overseas Post-Merger Behaviors in a Global Game Model

Feiqiong Chen, Qiaoshuang Meng and Fei Li
Journal of Artificial Societies and Social Simulation 19 (4) 13

Kyeywords: Post-Merger Integration, Technology Innovation, Multi-Agent Simulation, Integration Degree, Target Autonomy
Abstract: The abilities to efficiently identify potential innovation profits and form an optimal post-merger strategy are key to evaluating overseas merger and acquisition (M&A) performances. The paper uses a global game with asymmetric payoff structure and multi-agent simulation methods to analyze the optimal overseas post-merger strategy. We model three stages of the M&A processes: merger decision stage, post-merger integration stage, and technology innovation after M&A, to analyze how different resource similarity and resource complementarity of the two companies influence the degree of optimal post-merger integration and target autonomy as well as technology innovation profit after M&A. The agent-based simulation shows that, in overseas M&As, resource similarity has a positive relation with integration and a negative relation with target autonomy; however, resource complementarity has the opposite effect. The negative interaction effect between resource similarity and complementarity will decrease the degrees of integration. In high-resource-similarity and low-resource-complementarity M&As, a high integration degree and low target autonomy will maximize innovation profit, while for high-resource-similarity and high-resource-complementarity M&As, a high integration degree and target autonomy is best for innovation profit. For low-resource-similarity and high-resource-complementarity M&As, a low integration degree and high target autonomy will be the best post-merger strategy. Model outputs are robust to variations of the parameters.

Ontology Based Business Simulations

Thomas Farrenkopf, Michael Guckert, Neil Urquhart and Simon Wells
Journal of Artificial Societies and Social Simulation 19 (4) 14

Kyeywords: Social Simulation, Ontology, BDI Agent
Abstract: Within business games there is a need to provide realistic feedback for decisions made, if such business games are to continue to remain relevant in increasingly complex business environments. We address this problem by using software agents to simulate individuals and to model their actions in response to business decisions. In our initial studies we have used software agents to simulate consumers who make buying decisions based on their private preferences and those prevalent within their social network. This approach can be applied to search for behavioural patterns in social structures and to verify predicted values based on a priori theoretical considerations. Individual behaviour can be modelled for each agent and its effects within the marketplace can be examined by running simulations. Our simulations are founded upon the BDI software model (belief-desire-intention) combined with ontologies to make world knowledge available to the agents which can then determine their actions in accordance with this knowledge. We demonstrate how ontologies can be integrated into the BDI concept utilising the Jadex agent framework. Our examples are based upon the simulation of market mechanisms within the context of different industries. We use a framework, developed previously, known as AGADE within which each agent evolves its knowledge using an ontology maintained during the simulation. This generic approach allows the simulation of various consumer scenarios which can be modelled by creating appropriate ontologies.

Modeling Spatial Contacts for Epidemic Prediction in a Large-Scale Artificial City

Mingxin Zhang, Alexander Verbraeck, Rongqing Meng, Bin Chen and Xiaogang Qiu
Journal of Artificial Societies and Social Simulation 19 (4) 3

Kyeywords: Spatial Contacts, Agent-Based Modeling, Artificial City
Abstract: Spatial contacts among human beings are considered as one of the influential factors during the transmission of contagious diseases, such as influenza and tuberculosis. Therefore, representing and understanding spatial contacts plays an important role in epidemic modeling research. However, most current research only considers regular spatial contacts such as contacts at home/school/office, or they assume static social networks for modeling social contacts and omit travel contacts in their epidemic models. This paper describes a way to model relatively complete spatial contacts in the context of a large-scale artificial city, which combines different data sources to construct an agent-based model of the city Beijing. In this model, agents have regular contacts when executing their daily activity patterns which is similar to other large-scale agent-based epidemic models. Besides, a microscopic public transportation component is included in the artificial city to model public travel contacts. Moreover, social contacts also emerge in this model due to the dynamic generation of social networks. To systematically examine the effect of the relatively complete spatial contacts have for epidemic prediction in the artificial city, a pandemic influenza disease progression model was implemented in this artificial city. The simulation results validated the model. In addition, the way to model spatial contacts in this paper shows potential not only for improving comprehension of disease spread dynamics, but also for use in other social systems, such as public transportation systems and city level evacuation planning.

Exploring an Effective Incentive System on a Groupware

Fujio Toriumi, Hitoshi Yamamoto and Isamu Okada
Journal of Artificial Societies and Social Simulation 19 (4) 6

Kyeywords: Groupware, Agent-Based Simulation, Meta-Sanction Game, Public Good Games,
Abstract: Groupware is an effective form of media for knowledge sharing and active open communication. One remaining important issue is how to design groupware in which vast amounts of beneficial content are provided and active discussion is facilitated. The behavior of information in such a medium resembles public-goods games because users voluntarily post beneficial information that creates media values. Many studies on such games have shown the effects of rewards or punishments in promoting cooperative behavior. In this paper, we show what types of incentive systems of rewards and punishments promote and maintain effective information behaviors or cooperative regimes in actual groupware. Our agent-based simulation demonstrates that a meta-reward system in which rewarders can gain other benefits for their own reward actions will probably encourage cooperation. Counterintuitively, our simulation also demonstrates that a system that applies sanctioning functions does not necessarily promote cooperation. Interestingly, a first-order reward system without any second-order incentives impedes the formation of cooperative regimes, while this is not the case with first-order punishment systems without second-order incentives. These findings may elucidate how successful groupware operates.

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Patryk Siedlecki, Janusz Szwabiński and Tomasz Weron
Journal of Artificial Societies and Social Simulation 19 (4) 9

Kyeywords: Opinion Dynamics, Social Influence, Conformity, Anticonformity, Bi-Polarization, Agent-Based Modelling
Abstract: Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agent-based model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al, 2009b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a bi-polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction L of negative cross-links between cliques. In the regime of small values of L the system is able to reach the total positive consensus. If the values of L are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a sufficient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is required to arrive at a polarized steady state within our model.

Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead

Giorgio Fagiolo and Andrea Roventini
Journal of Artificial Societies and Social Simulation 20 (1) 1

Kyeywords: Economic Policy, Agent-Based Models, DSGE Models, Great Recession
Abstract: The Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework - the New Neoclassical Synthesis (NNS) - grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications (fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

An Agent-Based Model of Electricity Consumer: Smart Metering Policy Implications in Europe

Julija Vasiljevska, Jochem Douw, Anna Mengolini and Igor Nikolic
Journal of Artificial Societies and Social Simulation 20 (1) 12

Kyeywords: Electricity Consumer, Agent-Based Modelling, Smart Metering, Consumer Values
Abstract: EU Regulation 2009/72/EC concerning common rules for internal market in electricity calls upon 80% of EU electricity consumers to be equipped with smart metering systems by 2020, provided that a positive economic assessment of all long-term costs and benefits to the market and the individual consumer is guaranteed. Understanding the impact that smart metering systems may have on the electricity stakeholders (consumers, distribution system operators, energy suppliers and the society at large) is important for faster and effective deployment of such systems and of the innovative services they offer. For this purpose, in this paper an agent-based model is developed, where the electricity consumer behaviour due to different smart metering policies is simulated. Consumers are modelled as household agents having dynamic preferences on types of electricity contracts offered by the supplier. Development of preferences depends on personal values, memory and attitudes, as well as the degree of interaction in a social network structure. We are interested in exploring possible diffusion rates of smart metering enabled services under different policy interventions and the impact of this technological diffusion on individual and societal performance indicators. In four simulation experiments and three intervention policies we observe the diffusion of energy services and individual and societal performance indicators (electricity savings, CO2 emissions savings, social welfare, consumers' comfort change), as well as consumers' satisfaction. From these results and based on expert validation, we conclude that providing the consumer with more options does not necessarily lead to higher consumer's satisfaction, or better societal performance. A good policy should be centred on effective ways to tackle consumers concerns.

A Psychologically-Motivated Model of Opinion Change with Applications to American Politics

Peter Duggins
Journal of Artificial Societies and Social Simulation 20 (1) 13

Kyeywords: Agent-Based Model, Opinion Dynamics, Social Networks, Conformity, Polarization, Extremism
Abstract: Agent-based models are versatile tools for studying how societal opinion change, including political polarization and cultural diffusion, emerges from individual behavior. This study expands agents' psychological realism using empirically-motivated rules governing interpersonal influence, commitment to previous beliefs, and conformity in social contexts. Computational experiments establish that these extensions produce three novel results: (a) sustained strong diversity of opinions within the population, (b) opinion subcultures, and (c) pluralistic ignorance. These phenomena arise from a combination of agents' intolerance, susceptibility and conformity, with extremist agents and social networks playing important roles. The distribution and dynamics of simulated opinions reproduce two empirical datasets on Americans' political opinions.

The Coconut Model with Heterogeneous Strategies and Learning

Sven Banisch and Eckehard Olbrich
Journal of Artificial Societies and Social Simulation 20 (1) 14

Kyeywords: Search Equilibrium Model, Agent-Based Models, Model Alignment, Heterogeneous Agents, Adaptive Agents, Temporal Difference Learning
Abstract: In this paper, we develop an agent-based version of the Diamond search equilibrium model - also called Coconut Model. In this model, agents are faced with production decisions that have to be evaluated based on their expectations about the future utility of the produced entity which in turn depends on the global production level via a trading mechanism. While the original dynamical systems formulation assumes an infinite number of homogeneously adapting agents obeying strong rationality conditions, the agent-based setting allows to discuss the effects of heterogeneous and adaptive expectations and enables the analysis of non-equilibrium trajectories. Starting from a baseline implementation that matches the asymptotic behavior of the original model, we show how agent heterogeneity can be accounted for in the aggregate dynamical equations. We then show that when agents adapt their strategies by a simple temporal difference learning scheme, the system converges to one of the fixed points of the original system. Systematic simulations reveal that this is the only stable equilibrium solution.

Improving Execution Speed of Models Implemented in NetLogo

Steven F. Railsback, Daniel Ayllón, Uta Berger, Volker Grimm, Steven Lytinen, Colin Sheppard and Jan Thiele
Journal of Artificial Societies and Social Simulation 20 (1) 3

Kyeywords: Agent-Based Modeling, Computational Efficiency, Execution Speed, Individual-Based Modeling, NetLogo, Modeling Platforms
Abstract: NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.

Augmenting Bottom-up Metamodels with Predicates

Ross Gore, Saikou Diallo, Christopher Lynch and Jose Padilla
Journal of Artificial Societies and Social Simulation 20 (1) 4

Kyeywords: Metamodel, Agent-Based Simulation, Statistical Modeling, Predicates, Validation
Abstract: Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: (1) top-down and (2) bottom-up. The top down approach enables users to decompose high-level mental models into behaviors and interactions of agents. In contrast, the bottom-up approach constructs a relatively small, simple model that approximates the structure and outcomes of a dataset gathered from the runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. For most users the construction of a bottom-up metamodel entails: (1) creating an experimental design, (2) running the simulation for all cases specified by the design, (3) collecting the inputs and output in a dataset and (4) applying first-order regression analysis to find a model that effectively estimates the output. Unfortunately, the sums of input variables employed by first-order regression analysis give the impression that one can compensate for one component of the system by improving some other component even if such substitution is inadequate or invalid. As a result the metamodel can be misleading. We address these deficiencies with an approach that: (1) automatically generates Boolean conditions that highlight when substitutions and tradeoffs among variables are valid and (2) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations.

An Agent-Based Model of Flood Risk and Insurance

Jan Dubbelboer, Igor Nikolic, Katie Jenkins and Jim Hall
Journal of Artificial Societies and Social Simulation 20 (1) 6

Kyeywords: Flooding, London, Flood Insurance, Flood Re, Agent-Based Modelling
Abstract: Flood risk emerges from the dynamic interaction between natural hazards and human vulnerability. Methods for the quantification of flood risk are well established, but tend to deal with human and economic vulnerability as being static or changing with an exogenously defined trend. In this paper we present an Agent-Based Model (ABM) developed to simulate the dynamical evolution of flood risk and vulnerability, and facilitate an investigation of insurance mechanism in London. The ABM has been developed to firstly allow an analysis of the vulnerability of homeowners to surface water flooding, which is one of the greatest short-term climate risks in the UK with estimated annual costs of £1.3bn to £2.2bn. These costs have been estimated to increase by 60-220% over the next 50 years due to climate change and urbanisation. Vulnerability is influenced by homeowner’s decisions to move house and/or install measures to protect their properties from flooding. In particular, the ABM focuses on the role of flood insurance, simulating the current public-private partnership between the government and insurers in the UK, and the forthcoming re-insurance scheme Flood Re, designed as a roadmap to support the future affordability and availability of flood insurance. The ABM includes interaction between homeowners, sellers and buyers, an insurer, a local government and a developer. Detailed GIS and qualitative data of the London borough of Camden are used to represent an area at high risk of surface water flooding. The ABM highlights how future development can exacerbate current levels of surface water flood risk in Camden. Investment in flood protection measures are shown to be beneficial for reducing surface water flood risk. The Flood Re scheme is shown to achieve its aim of securing affordable flood insurance premiums, however, is placed under increasing pressure in the future as the risk of surface water flooding continues to increase.

An Agent Based Model for a Double Auction with Convex Incentives

Annalisa Fabretti and Stefano Herzel
Journal of Artificial Societies and Social Simulation 20 (1) 7

Kyeywords: Incentives, Agent-Based Simulations, Market Instability, Price Convergence, Order Book Analysis
Abstract: We studied the influence of convex incentives, e.g. option-like compensations, on the behavior of financial markets. Such incentives, usually offered to portfolio managers, have been often considered a potential source of market instability. We built an agent-based model of a double-auction market where some of the agents are endowed with convex contracts. We show that these contracts encourage traders to buy more aggressively, increasing total demand and market prices. Our analysis suggests that financial markets with many managers with convex contracts are more likely to be more unstable and less efficient.

From Micro Behaviors to Macro Dynamics: An Agent-Based Economic Model with Consumer Credit

Paola D'Orazio and Gianfranco Giulioni
Journal of Artificial Societies and Social Simulation 20 (1) 9

Kyeywords: Agent-Based Model, Credit Supply, Consumer Debt, Precautionary Saving, Wealth Distribution, Labor Market Matching
Abstract: The paper develops an agent-based model populated by heterogeneous consumers, a productive sector and a banking sector. Taking a bottom up approach, the paper aims at providing a first tool to analyze households' borrowing dynamics in the different phases of the business cycle by relaxing some assumptions of mainstream consumption models and considering more realistic household borrowing behaviors. Although very simple, the model allows us to grasp the main implications of the interaction between consumers' wants (desired consumption), consumers' beliefs (their expectations about their future income), the behavior of the banking sector (rationing) and the behavior of the production sector (forecasting future demand). After presenting and discussing sensitivity analysis over a parameters' set, the paper reports results and the ex-post validation by comparing artificial and empirical distributions computed using the European Household Finance and Consumption Survey data set.

An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion

Friedrich Krebs
Journal of Artificial Societies and Social Simulation 20 (2) 10

Kyeywords: Green Electricity, Innovation Diffusion, Spatially Explicit Agent-Based Model, Empirical Calibration and Validation
Abstract: Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath” the diffusion curve of green electricity in Germany.

Agent-Based Modelling Approach for Multidimensional Opinion Polarization in Collective Behaviour

Jin Li and Renbin Xiao
Journal of Artificial Societies and Social Simulation 20 (2) 4

Kyeywords: Social Computing, Collective Behaviour, Agent-Based Model, Multidimensional Opinion Polarization, Social Judgement Theory, Multi-Agent System
Abstract: Opinion polarization in a group is an important phenomenon in collective behaviour that has become increasingly frequent during periods of social transition. In general, an opinion includes several dimensions in reality. By combining social judgement theory with the multi-agent model, we propose a multidimensional opinion evolution model for studying the dynamics of opinion polarization. Compared with previous models, a major contribution is that the opinion of the agent is extended to multiple dimensions, and the BA network is used as a model of real social networks. The results demonstrate that polarization is influenced by the average degree of the network, and the polarization process is affected by the parameters of the assimilation effect and contrast effect. Moreover, the evolution processes in different dimensions of opinion show correlation under certain specific conditions, and the discontinuous equilibrium phenomenon is observed in multidimensional opinion evolution in subsequent experiments.

Utility, Impact, Fashion and Lobbying: An Agent-Based Model of the Funding and Epistemic Landscape of Research

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 20 (2) 5

Kyeywords: Agent-Based Model, Epistemic Landscape, Research Funding, Fashions, Maps of Science
Abstract: The paper presents an agent-based model of an evolution of research interests in a scientific community. The research epistemic/funding landscape is divided into separate domains, which differ in impact on society and the perceived utility, which may determine the public willingness to fund. Scientific domains also differ in their potential for attention grabbing, crucial discoveries, which make them fashionable and also attract funding. The scientists may `follow' the availability of funds via a stylized grant based scheme. The model includes possible effects of the additional public relation and lobbying efforts, promoting certain disciplines at the cost of others. Results are based on two multi-parameter NetLogo models. The first uses an abstract, square lattice topology, and serves as a tool to understand the effects of the parameters describing the individual preferences. The second model, sharing the internal dynamics with the first one, is based on an actual research topics map and projects statistics, derived from the UK Research Council data for 2007--2016. Despite simplifications, results reproduce characteristics of the British research community surprisingly well.

Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward

Jule Thober, Birgit Müller, Jürgen Groeneveld and Volker Grimm
Journal of Artificial Societies and Social Simulation 20 (2) 8

Kyeywords: Agent-Based Modelling, Social-Ecological Modelling, Model Development, Model Testing, Model Analysis, Human Decision-Making
Abstract: Understanding social-ecological systems (SES) is crucial to supporting the sustainable management of resources. Agent-based modelling is a valuable tool to achieve this because it can represent the behaviour and interactions of organisms, human actors and institutions. Agent-based models (ABMs) have therefore already been widely used to study SES. However, ABMs of SES are by their very nature complex. They are therefore difficult to parameterize and analyse, which can limit their usefulness. It is time to critically reflect upon the current state-of-the-art to evaluate to what degree the potential of agent-based modelling for gaining general insights and supporting specific decision-making has already been utilized. We reviewed achievements and challenges by building upon developments in good modelling practice in the field of ecological modelling with its longer history. As a reference, we used the TRACE framework, which encompasses elements of model development, testing and analysis. We firstly reviewed achievements and challenges with regard to the elements of the TRACE framework addressed in reviews and method papers of social-ecological ABMs. Secondly, in a mini-review, we evaluated whether and to what degree the elements of the TRACE framework were addressed in publications on specific ABMs. We identified substantial gaps with regard to (1) communicating whether the models represented real systems well enough for their intended purpose and (2) analysing the models in a systematic and transparent way so that model output is not only observed but also understood. To fill these gaps, a joint effort of the modelling community is needed to foster the advancement and use of strategies such as participatory approaches, standard protocols for communication, sharing of source code, and tools and strategies for model design and analysis. Throughout our analyses, we provide specific recommendations and references for improving the state-of-the-art. We thereby hope to contribute to the establishment of a new advanced culture of agent-based modelling of SES that will allow us to better develop general theory and practical solutions.

The Explanation of Social Conventions by Melioration Learning

Johannes Zschache
Journal of Artificial Societies and Social Simulation 20 (3) 1

Kyeywords: Reinforcement Learning, Agent-Based Simulation, N-Way Coordination Game, Roth-Erev Model
Abstract: In line with previous research, the evolution of social conventions is explored by n-way coordination games. A convention is said to be established if decisions of all actors synchronise over time. In contrast to the earlier studies, an empirically well-grounded process of reinforcement learning is used as behavioural assumption. The model is called melioration learning. It is shown by agent-based simulations that melioration enables actors to establish a convention. Besides the payoffs of the coordination game, the network structure of interactions affects actors' ability to coordinate their choices and the speed of convergence. The results of melioration learning are compared to predictions of the Roth-Erev model.

Modelling Human Behaviours in Disasters from Interviews: Application to Melbourne Bushfires

Carole Adam and Benoit Gaudou
Journal of Artificial Societies and Social Simulation 20 (3) 12

Kyeywords: Human Behaviour Modelling, Agent-Based Social Simulation, Crisis Management
Abstract: This paper describes a model for raising the decision-makers' awareness of the real (irrational and subjective) behaviours of the population in crisis situations. We analyse residents' statements and police hearings gathered after Victoria Black Saturday bushfires in 2009 to deduce a model of human behaviour based on the distinction between objective (capabilities, danger) and subjective (confidence, risk aversion) attributes, and on individual motivations. We evaluate it against observed behaviour archetypes and statistics, and show its explicative value.

Simulation for Interpretation: A Methodology for Growing Virtual Cultures

Ulf Lotzmann and Martin Neumann
Journal of Artificial Societies and Social Simulation 20 (3) 13

Kyeywords: Interpretative Research Process, Agent-Based Modelling, Generative Social Science, Qualitative Data, Thick Description, Cultural Studies
Abstract: Agent-based social simulation is well-known for generative explanations. Following the theory of thick description we extend the generative paradigm to interpretative research in cultural studies. Using the example of qualitative data about criminal culture, the paper describes a research process that facilitates interpretative research by growing virtual cultures. Relying on qualitative data for the development of agent rules, the research process combines several steps: Qualitative data analysis following the Grounded Theory paradigm enables concept identification, resulting in the development of a conceptual model of the concept relations. The software tool CCD is used in conceptual modelling which assists semi-automatic transformation in a simulation model developed in the simulation platform DRAMS. Both tools preserve traceability to the empirical evidence throughout the research process. Traceability enables interpretation of simulations by generating a narrative storyline of the simulation. Thereby simulation enables a qualitative exploration of textual data. The whole process generates a thick description of the subject of study, in our example criminal culture. The simulation is characterized by a socio-cognitive coupling of agents’ reasoning on the state of the mind of other agents. This reveals a thick description of how participants make sense of the phenomenology of a situation from the perspective of their worldview.

Thomas C. Schelling and James M. Sakoda: The Intellectual, Technical, and Social History of a Model

Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 20 (3) 15

Kyeywords: Schelling, Sakoda, Checkerboard Models, Tipping Models, Threshold Models, Agent-Based Modeling
Abstract: The Journal of Mathematical Sociology (JMS) started in 1971. The second issue contained its most cited article: Thomas C. Schelling, “Dynamic Models of Segregation”. In that article, Schelling presented a family of models, one of which became a canonical model. To date it is called the Schelling model—an eponym that affixes the inventor’s name to the invention, one of the highest forms of scientific recognition. In the very first issue of JMS, James Minoru Sakoda published an article entitled “The Checkerboard Model of Social Interaction”. Sakoda’s article more or less went unrecognized. Yet, a careful comparison demonstrates that in a certain sense the Schelling model is just an instance of Sakoda’s model. A precursor of that model was already part of Sakoda’s 1949 dissertation submitted to the University of California at Berkeley. A substantial amount of evidence indicates that in the 1970s Sakoda was well known and recognized as a computational social scientist, whereas Schelling was an unknown in the field. A generation later, the pattern of recognition almost completely reversed: Sakoda had become the unknown, while Schelling was the well-known inventor of the pioneering Schelling model. This article explains this puzzling pattern of recognition. Technical and social factors play a decisive role. Some contrafactual historical reflection suggests that the final result was not inevitable.

A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology

Elizabeth Hunter, Brian Mac Namee and John D. Kelleher
Journal of Artificial Societies and Social Simulation 20 (3) 2

Kyeywords: Agent-Based, Epidemiology, Infectious Disease, Simulation, Model, Taxonomy
Abstract: Agent-based simulation modelling has been used in many epidemiological studies on infectious diseases. However, because agent based modelling is a field without any clear protocol for developing simulations the researcher is given a high amount of flexibility. This flexibility has led to many different forms of agent-based epidemiological simulations. In this paper we review the existing literature on agent-based epidemiological simulation models. From our literature review we identify key similarities and differences in the exisiting simulations. We then use these similarities and differences to create a taxonomy of agent-based epidemiological models and show how the taxonomy can be used.

Effects of the Interaction Between Ideological Affinity and Psychological Reaction of Agents on the Opinion Dynamics in a Relative Agreement Model

Norma L. Abrica-Jacinto, Evguenii Kurmyshev and Héctor A. Juárez
Journal of Artificial Societies and Social Simulation 20 (3) 3

Kyeywords: Opinion Dynamics, Ideological Affinity, Artificial Society, Relative Agreement, Agent-Based Model
Abstract: Ideology is one of the defining elements of opinion dynamics. In this paper, we report the effects of the nonlinear interaction of ideological affinity with the psychological reaction of agents in the frame of a multiparametric mathematical model of opinion dynamics. Computer simulations of artificial networked societies composed of agents of two psychological types were used for studying opinion formation; the simulations showed a phenomenon of preferential self-organization into groups of ideological affinity at the first stages of opinion evolution. The separation into ideologically akin opinion groups (ideological affinity) was more notable in societies composed mostly of concord agents; a larger opinion polarization was associated with the increase of agents’ initial average opinion uncertainty. We also observed a sensibility of opinion dynamics to the initial conditions of opinion and uncertainty, indicating potential instabilities. A measure of convergence was introduced to facilitate the analysis of transitions between the opinion states of networked societies and to detect social instability events. We found that the average of opinion uncertainty distribution reaches a steady state with values lower than the initial average value, sometimes nearing zero, which points at socially apathetic agents. Our analyses showed that the model can be utilized for further investigation on opinion dynamics and can be extended to other social phenomena.

Responsiveness of Mining Community Acceptance Model to Key Parameter Changes

Mark Kofi Boateng and Kwame Awuah-Offei
Journal of Artificial Societies and Social Simulation 20 (3) 4

Kyeywords: Mining Community, Agent-Based Modeling, Diffusion, Sensitivity Analysis, Mining
Abstract: The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines’ environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbor ratio (a measure of homophily in the social network) and number of early adopters (“innovators”). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model’s sensitivity. The results show that the model is more responsive to close neighbor ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbor ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbor ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities.

Growing Unpopular Norms

Christoph Merdes
Journal of Artificial Societies and Social Simulation 20 (3) 5

Kyeywords: Social Norms, Agent-Based Simulation, Social Influence, Pluralistic Ignorance
Abstract: Unpopular norms are a pervasive and puzzling phenomenon of the social world. They are norms that are established and maintained against the interest of their subjects, but without external coercion. Pluralistic ignorance has been suggested as a potential explanation of unpopular norms. What is currently lacking is a formal model of this process that can be meaningfully compared with empirically known properties of pluralistic ignorance. An agent-based model of a growing social network can reproduce the most significant qualitative features, viz a deviation of the perceived norm from the preference distribution and the dynamical lag of the former behind the latter. In addition, the model is extended with a central influence representing for example central media or a powerful political elite.

A Computational Study of the Station Nightclub Fire Accounting for Social Relationships

Sherif El-Tawil, Jieshi Fang, Benigno Aguirre and Eric Best
Journal of Artificial Societies and Social Simulation 20 (4) 10

Kyeywords: Egress, Agent-Based Model, Scalar Field Method, Social Relationships, the Station Building Fire
Abstract: Using agent-based modeling, this study presents the results of a computational study of social relationships among more than four hundreds evacuees in The Station Nightclub building in Rhode Island. The fire occurred on the night of February 20, 2003 and resulted in 100 fatalities. After summarizing and calibrating the computational method used, parametric studies are conducted to quantitatively investigate the influences of the presence of social relationships and familiarity of the building floor plan on the death and injury tolls. It is demonstrated that the proposed model has the ability to reasonably handle the complex social relationships and group behaviors present during egress. The simulations quantify how intimate social affiliations delay the overall egress process and show the extent by which lack of knowledge of a building floor plan limits exit choices and adversely affects the number of safe evacuations.

New Winning Strategies for the Iterated Prisoner's Dilemma

Philippe Mathieu and Jean-Paul Delahaye
Journal of Artificial Societies and Social Simulation 20 (4) 12

Kyeywords: Game Theory, Group Strategy, Iterated Prisoner’s Dilemma (IPD), Agent Behaviour, Memory, Opponent Identification
Abstract: In the iterated prisoner’s dilemma game, new successful strategies are regularly proposed especially outperforming the well-known tit_for_tat strategy. New forms of reasoning have also recently been introduced to analyse the game. They lead William Press and Freeman Dyson to a double infinite family of strategies that -theoretically- should all be efficient strategies. In this paper, we study and confront using several experimentations the main strategies introduced since the discovery of tit_for_tat. We make them play against each other in varied and neutral environments. We use the complete classes method that leads us to the formulation of four new simple strategies with surprising results. We present massive experiments using simulators specially developed that allow us to confront up to 6,000 strategies simultaneously, which had never been done before. Our results show without any doubt the most robust strategies among those so far identified. This work defines new systematic, reproductible and objective experiments suggesting several ways to design strategies that go a step further, and a step in the software design technology to highlight efficient strategies in iterated prisoner’s dilemma and multiagent systems in general.

Introducing a Multi-Asset Stock Market to Test the Power of Investor Networks

Matthew Oldham
Journal of Artificial Societies and Social Simulation 20 (4) 13

Kyeywords: Agent-Based Model, Artificial Stock Market, Networks, Portfolio Anlaysis
Abstract: The behavior of financial markets has frustrated, and continues to frustrate, investors and academics. By utilizing a complex systems framework, researchers have discovered new fields of investigations that have provided meaningful insight into the behavior of financial markets. The use of agent-based models (ABMs) and the inclusion of network science have played an important role in increasing the relevance of the complex systems to financial markets. The challenge of how best to combine these new techniques to produce meaningful results that can be accepted by the broader community remains an issue. By implementing an artificial stock market that utilizes an Ising model based agent-based model (ABM), this paper provides insights into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. A key finding is that the network topology investors form significantly affects the behavior of the market, with the exception being if investors have a bias to following their neighbors, at which point the topology becomes redundant. The model also investigates the impact of introducing multiple risky assets, something that has been absent in previous attempts. By successfully addressing these issues this paper helps to refine and shape a variety of further research tasks for the use of ABMs in uncovering the dynamics of financial markets.

Opinion Communication on Contested Topics: How Empirics and Arguments can Improve Social Simulation

Annalisa Stefanelli and Roman Seidl
Journal of Artificial Societies and Social Simulation 20 (4) 3

Kyeywords: Agent-Based Model, Arguments, Opinion Dynamics, Social Judgment
Abstract: The effect of social interactions on how opinions are developed and changed over time is crucial to public processes that involve citizens and their points of view. In this opinion dynamics exercise, we address the topic of nuclear waste repositories in Switzerland and suggest a more realistic investigation of public opinion using agent-based modeling in combination with empirical data and sociopsychological theory. Empirical data obtained from an online questionnaire (N = 841) is used for the initialization of the model, whose agents directly represent the participants. We use social judgment theory (SJT) to describe how opinions can be adapted during social interactions, including through mechanisms of contrast and assimilation. Furthermore, we focus on the definition of “opinion” itself, claiming that working with disaggregated opinions (i.e., arguments) can play a determining role if one aims to capture real-world mechanisms of opinion dynamics. Simulation results show different patterns for the three different argument categories used for this specific topic (i.e., risk, benefit, and process), suggesting a mutual influence between an individual’s initial knowledge and evaluations and an individual’s social dynamics and opinion changes. The importance of content-related and empirical information, as well as the theory and mechanisms used in the social simulation, are discussed.

Asynchronous Opinion Dynamics with Online and Offline Interactions in Bounded Confidence Model

Zhaogang Ding, Yucheng Dong, Haiming Liang and Francisco Chiclana
Journal of Artificial Societies and Social Simulation 20 (4) 6

Kyeywords: Opinion Dynamics, Asynchronism, Bounded Confidence, Agent-Based Simulation
Abstract: Nowadays, about half of the world population can receive information and exchange opinions in online environments (e.g. the Internet), while the other half do so offline (e.g. face to face). The speed at which information is received and opinions are exchanged in online environment is much faster than offline. To model this phenomenon, in this paper we consider online and offline as two subsystems in opinion dynamics and assume asynchronization when agents in these two subsystems update their opinions. We unfold that asynchronization has a strong impact on the steady-state time of the opinion dynamics, the opinion clusters and the interactions between online and offline subsystems. Furthermore, these effects are often enhanced the larger the size of the online subsystem is.

Efficient and Effective Pair-Matching Algorithms for Agent-Based Models

Nathan Geffen and Stefan Scholz
Journal of Artificial Societies and Social Simulation 20 (4) 8

Kyeywords: HIV, Agent-Based Models, Sexually Transmitted Infections, Pair-Matching
Abstract: Microsimulations and agent-based models across various disciplines need to match agents into relationships. Some of these models need to repeatedly match different pairs of agents, for example microsimulations of sexually transmitted infection epidemics. We describe the requirements for pair-matching in these types of microsimulations, and present several pair-matching algorithms: Brute force (BFPM), Random (RPM), Random k (RKPM), Weighted shuffle (WSPM), Cluster shuffle (CSPM), and Distribution counting (DCPM). Using two microsimulations, we empirically compare the speeds, and pairing quality of these six algorithms. For models which execute pair-matching many thousands or millions of times, BFPM is not usually a practical option because it is slow. On the other hand, RPM is fast but chooses poor quality pairs. Nevertheless both algorithms are used, sometimes implicitly, in many models. Here we use them as yardsticks for upper and lower bounds for speed and quality. In these tests CSPM offers the best trade-off of speed and effectiveness. In general, CSPM is fast and produces stochastic, high quality pair-matches, which are often desirable characteristics for pair-matching in discrete time step microsimulations. Moreover it is a simple algorithm that can be easily adapted for the specific needs of a particular domain. However, for some models, RKPM or DCPM would be as fast as CSPM with matches of similar quality. We discuss the circumstances under which this would happen.

Modeling Organizational Cognition: The Case of Impact Factor

Davide Secchi and Stephen J. Cowley
Journal of Artificial Societies and Social Simulation 21 (1) 13

Kyeywords: Organizational Cognition, Distributed Cognition, E-Cognition, Impact Factor, Perceived Scientific Value, Social Organizing, Agent-Based Simulation Modeling
Abstract: This article offers an alternative perspective on organizational cognition based on e-cognition whereby appeal to systemic cognition replaces the traditional computational model of the mind that is still extremely popular in organizational research. It uses information processing, not to explore inner processes, but as the basis for pursuing organizational matters. To develop a theory of organizational cognition, the current work presents an agent-based simulation model based on the case of how individual perception of scientific value is affected by and affects organizational intelligence units' (e.g., research groups', departmental) framing of the notorious impact factor. Results show that organizational cognition cannot be described without an intermediate meso scale – called here social organizing – that both filters and enables the many kinds of socially enabled perception, action and behavior that are so characteristic of human cognition.

A Minimal Agent-Based Model Reproduces the Overall Topology of Interbank Networks

Sara Cuenda, Maximiliano Fernández, Javier Galeano and José A. Capitán
Journal of Artificial Societies and Social Simulation 21 (1) 2

Kyeywords: Interbank Markets, Agent-Based Modeling, Complex Networks
Abstract: The description of the empirical structure of interbank networks constitutes an important field of study since network theory can be used as a powerful tool to assess the resilience of financial systems and their robustness against failures. On the other hand, the development of reliable models of interbank market structure is relevant as they can be used to analyze systemic risk in the absence of transaction data or to test statistical hypotheses regarding network properties. Based on a detailed data-driven analysis of bank positions (assets and liabilities) taken from the Bankscope database, we here develop a minimal, stochastic, agent-based network model that accounts for the basic topology of interbank networks reported in the literature. The main assumption of our model is that loans between banks attempt to compensate assets and liabilities at each time step, and the model renders networks comparable with those observed in empirical studies. In particular, our model is able to qualitatively reproduce degree distributions, the distribution of the number of transactions, the distribution of exposures, the correlations with nearest-neighbor out-degree, and the clustering coefficient. As our simple model captures the overall structure of empirical networks, it can thus be used as a null model for testing hypotheses relative to other specific properties of interbank networks.

The Role of Heterogeneity and the Dynamics of Voluntary Contributions to Public Goods: An Experimental and Agent-Based Simulation Analysis

Engi Amin, Mohamed Abouelela and Amal Soliman
Journal of Artificial Societies and Social Simulation 21 (1) 3

Kyeywords: Agent-Based Simulation, Cooperation, Public Goods Game, Laboratory Experiment, Social Preferences
Abstract: This paper examines the role of heterogeneous agents in the study of voluntary contributions to public goods. A human-subject experiment was conducted to classify agent types and determine their effects on contribution levels. Data from the experiment was used to build and calibrate an agent-based simulation model. The simulations display how different compositions of agent preference types affect the contribution levels. Findings indicate that the heterogeneity of cooperative preferences is an important determinant of a population’s contribution pattern.

Forecasting Changes in Religiosity and Existential Security with an Agent-Based Model

Ross Gore, Carlos Lemos, F. LeRon Shults and Wesley J. Wildman
Journal of Artificial Societies and Social Simulation 21 (1) 4

Kyeywords: Religion, Agent-Based Model, Data Based Modeling, Social Influence
Abstract: We employ existing data sets and agent-based modeling to forecast changes in religiosity and existential security among a collective of individuals over time. Existential security reflects the extent of economic, socioeconomic and human development provided by society. Our model includes agents in social networks interacting with one another based on the education level of the agents, the religious practices of the agents, and each agent's existential security within their natural and social environments. The data used to inform the values and relationships among these variables is based on rigorous statistical analysis of the International Social Survey Programme Religion Module (ISSP) and the Human Development Report (HDR). We conduct an evaluation that demonstrates, for the countries and time periods studied, that our model provides a more accurate forecast of changes in existential security and religiosity than two alternative approaches. The improved accuracy is largely due to the inclusion of social networks with educational homophily which alters the way in which religiosity and existential security change in the model. These dynamics grow societies where two individuals with the same initial religious practices (or belief In God, or supernatural beliefs) evolve differently based on the educational backgrounds of the individuals with which they surround themselves. Finally, we discuss the limitations of our model and provide direction for future work.

Which Perspective of Institutional Change Best Fits Empirical Data? An Agent-Based Model Comparison of Rational Choice and Cultural Diffusion in Invasive Plant Management

Abigail Sullivan, Li An and Abigail York
Journal of Artificial Societies and Social Simulation 21 (1) 5

Kyeywords: Agent-Based Model, Institutions, Invasive Pest, Collective Action
Abstract: There are multiple theories regarding how institutions change over time, but institutional change is often difficult to study and understand in practice. Agent-based modeling is known as a technique to explore emergent phenomena resulting from the micro level activities and interactions between heterogeneous agents and between agents and the environment. Such models allow researchers to investigate theories which may otherwise be difficult to examine. We present a theoretically driven agent-based model to explore two perspectives on institutional change, rational choice and cultural diffusion, in the context of invasive plant management in Chitwan, Nepal. The Chitwan region is grappling with the spread of the invasive mile-a-minute weed, Mikania micrantha (Mikania). We focus on understanding which perspective of institutional change better fits empirical survey data on Mikania management. We find that rational choice is an unlikely candidate for institutional change in Chitwan and that the social learning and imitation mechanism modeled in the cultural diffusion perspective better replicates empirical patterns. Additionally, the model reveals that the percentage of agents adopting the best practice removal method is not as influential in reducing Mikania as the initial amount of Mikania removed. This result indicates that it may be useful to conduct an empirical assessment varying the initial amount of Mikania removed to understand the management implications for successful removal of Mikania in Chitwan and elsewhere.

Social Norms and the Dominance of Low-Doers

Carlo Proietti and Antonio Franco
Journal of Artificial Societies and Social Simulation 21 (1) 6

Kyeywords: Agent-Based Model, Social Norms, Game Theory
Abstract: Social norms play a fundamental role in holding groups together. The rationale behind most of them is to coordinate individual actions into a beneficial societal outcome. However, there are cases where pro-social behavior within a community seems, to the contrary, to cause inefficiencies and suboptimal collective outcomes. An explanation for this is that individuals in a society are of different types and their type determines the norm of fairness they adopt. Not all such norms are bound to be beneficial at the societal level. When individuals of different types meet a clash of norms can arise. This, in turn, can determine an advantage for the “wrong” type. We show this by a game-theoretic analysis in a very simple setting. To test this result - as well as its possible remedies - we also devise a specific simulation model. Our model is written in NETLOGO and is a first attempt to study our problem within an artificial environment that simulates the evolution of a society over time.

Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation

Frank M. A. Klingert and Matthias Meyer
Journal of Artificial Societies and Social Simulation 21 (1) 7

Kyeywords: Continuous Double Auction, Logarithmic Market Scoring Rule, Market Mechanisms, Multi-Agent Simulation, Prediction Markets, Simulation Validation
Abstract: Prediction markets are a promising instrument for drawing on the “wisdom of the crowds”. For instance, in a corporate context they have been used successfully to forecast sales or project risks by tapping into the heterogeneous information of decentralized actors in and outside of companies. Among the main market mechanisms implemented so far in prediction markets are (1) the continuous double auction and (2) the logarithmic market scoring rule. However, it is not fully understood how this choice affects crucial variables like prediction market accuracy or price variation. Our paper uses an experiment-based and micro validated simulation model to improve the understanding of the mechanism-related effects and to inform further laboratory experiments. The results underline the impact of mechanism selection. Due to the higher number of trades and the lower standard deviation of the price, the logarithmic market scoring rule seems to have a clear advantage at a first glance. This changes when the accuracy level, which is the most important criterion from a practical perspective, is used as an independent variable; the effects become less straightforward and depend on the environment and actors. Besides these contributions, this work provides an example of how experimental data can be used to validate agent strategies on the micro level using statistical methods.

An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members

Ismo T. Koponen and Maija Nousiainen
Journal of Artificial Societies and Social Simulation 21 (2) 1

Kyeywords: Discourse Patterns, Task Focused Groups, Agent-Based Model, Competition, Cooperation
Abstract: Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peer-to-peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity and cooperativity. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.

The Thin Blue Line Between Protesters and Their Counter-Protesters

Tamsin E. Lee
Journal of Artificial Societies and Social Simulation 21 (2) 10

Kyeywords: Agent-Based Modelling, Individual-Based Model, Protest Behaviour, Social Simulation, Netlogo
Abstract: More frequently protests are accompanied by an opposing group performing a counter protest. This phenomenon can increase tension such that police must try to keep the two groups separated. However, what is the best strategy for police? This paper uses a simple agent-based model to determine the best strategy for keeping the two groups separated. The 'thin blue line' varies in density (number of police), width and the keenness of police to approach protesters. Three different groups of protesters are modelled to mimic peaceful, average and volatile protests. In most cases, a few police forming a single-file 'thin blue line' separating the groups is very effective. However, when the protests are more volatile, it is more effective to have many police occupying a wide 'thin blue line', and police being keen to approach protesters. To the authors knowledge, this is the first paper to model protests and counter-protests.

Dynamic Pricing Strategies for Perishable Product in a Competitive Multi-Agent Retailers Market

Wenchong Chen, Hongwei Liu and Dan Xu
Journal of Artificial Societies and Social Simulation 21 (2) 12

Kyeywords: Dynamic Pricing Strategies, Customer Preference, Perishable Product, Multi-Agent, Q-Learning Algorithm
Abstract: Due to the fierce competition in the marketplace for perishable products, retailers have to use pricing strategies to attract customers. Traditional pricing strategies adjust products’ prices according to retailers’ current situations (e.g. Cost-plus pricing strategy, Value-based pricing strategy and Inventory-sensitive pricing strategy). However, many retailers lack the perception for customer preferences and an understanding of the competitive environment. This paper explores a price Q-learning mechanism for perishable products that considers uncertain demand and customer preferences in a competitive multi-agent retailer market (a model-free environment). In the proposed simulation model, agents imitate the behavior of consumers and retailers. Four potential influencing factors (competition, customer preferences, uncertain demand, perishable characteristics) are constructed in the pricing decisions. All retailer agents adjust their products’ prices over a finite sales horizon to maximize expected revenues. A retailer agent adjusts its price according to the Q-learning mechanism, while others adapt traditional pricing strategies. Shortage is allowed while backlog is not. The simulation results show that the dynamic pricing strategy via the Q-learning mechanism can be used for pricing perishable products in a competitive environment, as it can produce more revenue for retailers. Further, the paper investigates how an optimal pricing strategy is influenced by customer preferences, customer demand, retailer pricing parameters and the learning parameters of Q-learning. Based on our results, we provide pricing implications for retailers pursuing higher revenues.

Simulation of the Governance of Complex Systems (SimCo): Basic Concepts and Experiments on Urban Transportation

Fabian Adelt, Johannes Weyer, Sebastian Hoffmann and Andreas Ihrig
Journal of Artificial Societies and Social Simulation 21 (2) 2

Kyeywords: Governance, Agent-Based Modelling, Complexity, Infrastructure Systems, Transport Network, Transport Mode Choice
Abstract: The current paper is positioned at the intersection of computer simulation, governance research, and research on infrastructure systems, such as transportation or energy. It proposes a simulation framework, “Simulation of the governance of complex systems” (SimCo), to study the governability of complex socio-technical systems experimentally by means of agent-based modelling (ABM). SimCo is rooted in a sociological macro-micro-macro model of a socio-technical system, taking into account the interplay of agents' choices (micro) and situational constraints (macro). The paper presents the conceptualization of SimCo, its elements and subsystems as well as their interactions. SimCo depicts the daily routines of users performing their tasks (e.g. going to work) by choosing among different technologies (e.g. modes of transportation), occasionally deciding to replace a worn-out technology. All components entail different dimensions that can be adjusted, thus allowing operators to purposefully intervene, for instance in the case of risk management (e.g. preventing congestion) or system transformation (e.g. towards sustainable mobility). Experiments with a basic scenario of an urban road transport system demonstrate the effects of different modes of governance (soft control, strong control and a combination of both), revealing that soft control may be the best strategy to govern a complex socio-technical system.

PyNetLogo: Linking NetLogo with Python

Marc Jaxa-Rozen and Jan H. Kwakkel
Journal of Artificial Societies and Social Simulation 21 (2) 4

Kyeywords: Agent-Based Modelling, NetLogo, Python
Abstract: Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.

Emergence of Task Formation in Organizations: Balancing Units' Competence and Capacity

Friederike Wall
Journal of Artificial Societies and Social Simulation 21 (2) 6

Kyeywords: Agent-Based Simulation, Complexity, Coordination, Emergence, Reinforcement Learning, Task Formation
Abstract: This paper studies the emergence of task formation under conditions of limited knowledge about the complexity of the problem to be solved by an organization. Task formation is a key issue in organizational theory and the emergence of task formation is of particular interest when the complexity of the overall problem to be solved is not known in advance, since, for example, an organization is newly founded or has gone through an external shock. The paper employs an agent-based simulation based on the framework of NK fitness landscapes and controls for different levels of task complexity and for different coordination modes. In the simulations, artificial organizations are observed while searching for higher levels of organizational performance by two intertwined adaptive processes: short-termed search for superior solutions to the organizations' task and, in mid term, learning-based adaptation of task formation. The results suggest that the emerging task formations vary with the complexity of the underlying problem and, thereby, the balance between units' scope of competence and the organizational capacity for problem-solving is affected. For decomposable problems, task formations emerge which reflect the nature of the underlying problem; for non-decomposable structures, task formations with a broader scope of units' competence emerge. Furthermore, results indicate that, particularly for non-decomposable problems, the coordination mode employed in an organization subtly interferes with the emergence of task formation.

ODD+2D: An ODD Based Protocol for Mapping Data to Empirical ABMs

Ahmed Laatabi, Nicolas Marilleau, Tri Nguyen-Huu, Hassan Hbid and Mohamed Ait Babram
Journal of Artificial Societies and Social Simulation 21 (2) 9

Kyeywords: Empirical Agent-Based Models, ODD Protocol, ODD+2D, Mapping, Data Analysis, Social Simulation
Abstract: The quantity of data and processes used in modeling projects has been dramatically increasing in recent years due to the progress in computation capability and to the popularity of new approaches such as open data. Modelers face an increasing difficulty in analyzing and modeling complex systems that consist of many heterogeneous entities. Adapting existing models is relevant to avoid dealing with the complexity of writing and studying a new model from scratch. ODD (Overview, Design concepts, Details) protocol has emerged as a solution to document Agent-Based Models (ABMs). It appears to be a convenient solution to address significant problems such as comprehension, replication, and dissemination. However, it lacks a standard that formalizes the use of data in empirical models. This paper tackles this issue by proposing a set of rules that outline the use of empirical data inside an ABM. We call this new protocol ODD+2D (ODD+Decision + Data). ODD+2D integrates a mapping diagram called DAMap (Data to Agent Mapping). This mapping model formalizes how data are processed and mapped to agent-based models. In this paper, we focus on the architecture of ODD+2D, and we illustrate it with a residential mobility model in Marrakesh.

An Agent-Based Model of Residential Energy Efficiency Adoption

Magnus Moglia, Aneta Podkalicka and James McGregor
Journal of Artificial Societies and Social Simulation 21 (3) 3

Kyeywords: Energy Efficiency, Policy Assessment, Innovation Diffusion, Solar Hot Water, Consumat, Ex-Ante
Abstract: This paper reports on an Agent-Based Model. The purpose of developing this model is to describe ‘the uptake of low carbon and energy efficient technologies and practices by households and under different interventions’. There is a particular focus on modelling non-financial incentives as well as the influence of social networks as well as the decision making by multiple types of agents in interaction, i.e. recommending agents and sales agents, not just households. The decision making model for householder agents is inspired by the Consumat approach, as well as some of those recently applied to electric vehicles. A feature that differentiates this model is that it also represents information agents that provide recommendations and sales agents that proactively sell energy efficient products. By applying the model to a number of scenarios with policies aimed at increasing the adoption of solar hot water systems, a range of questions are explored, including whether it is more effective to incentivise sales agents to promote solar hot water systems, or whether it is more effective to provide a subsidy directly to households; or in fact whether it is better to work with plumbers so that they can promote these systems. The resultant model should be viewed as a conceptual structure with a theoretical and empirical grounding, but which requires further data collection for rigorous analysis of policy options.

Countries as Agents in a Global-Scale Computational Model

Harold J. Walbert, James L. Caton and Julia R. Norgaard
Journal of Artificial Societies and Social Simulation 21 (3) 4

Kyeywords: Agent Based Modeling, Conflict Resolution, Tribute, Diplomacy, War, Economic Analysis of Conflict
Abstract: Our agent-based model examines the ramifications of formal defense agreements between countries. Our model builds on previous work and creates an empirically based version of a tribute model in which actors within existing real-world networks demand tribute from one another. If the threatened actor does not pay the tribute, the aggressing actor will engage in a decision to start a war. Tribute and war payments are based on a measure of the country's wealth. We utilize the Correlates of War dataset to provide us with worldwide historical defense alliance information. Using these networks as our initial conditions, we run the model forward from four prominent historical years and simulate the interactions that take place as well as the changes in overall wealth. Agents in the model employ a cost benefit analysis in their decision of whether or not to go to war. This model provides results that are in qualitative agreement with historical emergent macro outcomes seen over time.

Generating Synthetic Bitcoin Transactions and Predicting Market Price Movement Via Inverse Reinforcement Learning and Agent-Based Modeling

Kamwoo Lee, Sinan Ulkuatam, Peter Beling and William Scherer
Journal of Artificial Societies and Social Simulation 21 (3) 5

Kyeywords: Cryptocurrency, Bitcoin, Inverse Reinforcement Learning, Agent-Based Modeling
Abstract: In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of predicting the price through reproducing synthetic yet realistic behaviors of rational agents in a simulated market, instead of estimating relationships between the price and price-related factors. IRL provides a systematic way to find the behavioral rules of each agent from Blockchain data by framing the trading behavior estimation as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. Once the rules are recovered, an agent-based model creates hypothetical interactions between the recovered behavioral rules, discovering equilibrium prices as emergent features through matching the supply and demand of Bitcoin. One distinct aspect of our approach with ABM is that while conventional approaches manually design individual rules, our agents’ rules are channeled from IRL. Our experimental results show that the proposed method can predict short-term market price while outlining overall market trend.

Agent-Based Modelling of Viticulture Development in Emerging Markets: The Case of the Małopolska Region

Marcin Czupryna, Paweł Oleksy, Piotr Przybek and Bogumił Kamiński
Journal of Artificial Societies and Social Simulation 21 (3) 6

Kyeywords: Agent-Based Modelling, Market Development, Behavioural Factors, Viticulture, Wine
Abstract: In this paper, we apply an agent-based approach to explain both the final state and the dynamics of the development process of the wine sector in the Małopolska region in Poland. This sector has been affected by various environmental, institutional, behavioural and social factors and has undergone evolutionary changes in recent years. The econometric analysis of empirical data of vineyards in this region provides insights into the degree of influence of various factors under consideration on the aggregate number of vineyards in sub-regions. However, this does no explain the dynamics of the local formation of new vineyards or the underlying latent attitudes of vineyard owners. To overcome this limitation, we developed an agent-based model with heterogeneous agents (regular farms as well as large and small vineyards), which allowed us to identify a two-stage development scenario: i) community building and ii) vineyard creation. Our findings are of two types. Firstly, we showed a case where the agent-based model has good predictive power, in situations where the econometric model fails. Secondly, estimation of the agent-based model parameters and sensitivity analysis revealed crucial factors that have driven development of viticulture in the Małopolska region. In particular, we find that the crucial element underlying the good predictive power of the model is that it enables us to capture the fact that wine enthusiasts initially concentrate in sub-regions with more benign environmental conditions. Next, when one of them eventually established a vineyard, agents in the community had a lowered barrier to entry via the possibility of practical knowledge exchange, joint marketing efforts or vineyard maintenance resource sharing. This is in line with current evidence, which shows strong clustering effects, namely, a relatively large number of vineyards originate at relatively similar times and locations.

Agent-Based Models of Gender Inequalities in Career Progression

John Bullinaria
Journal of Artificial Societies and Social Simulation 21 (3) 7

Kyeywords: Agent-Based Models, Gender Inequalities, Career Preferences, Social Learning, Evolution
Abstract: An agent-based simulation framework is presented that provides a principled approach for investigating gender inequalities in professional hierarchies such as universities or businesses. Populations of artificial agents compete for promotion in their chosen professions, leading to emergent distributions that can be matched to real-life scenarios, and allowing the influence of socially or genetically acquired career preferences to be explored. The aim is that such models will enable better understanding of how imbalances emerge and evolve, facilitate the identification of specific signals that can indicate the presence or absence of discrimination, and provide a tool for determining how and when particular intervention strategies may be appropriate for rectifying any inequalities. Results generated from a representative series of abstract case studies involving innate or culturally-acquired gender-based ability differences, gender-based discrimination, and various forms of gender-specific career preferences, demonstrate the power of the approach. These simulations will hopefully inspire and facilitate better approaches for dealing with these issues in real life.

How to Relate Models to Reality? An Epistemological Framework for the Validation and Verification of Computational Models

Claudius Graebner
Journal of Artificial Societies and Social Simulation 21 (3) 8

Kyeywords: Agent-Based Modelling, Epistemology, Models, Validation, Verification
Abstract: Agent-based simulations have become increasingly prominent in various disciplines. This trend is positive, but it comes with challenges: while there are more and more standards for design, verification, validation, and presentation of the models, the various meta-theoretical strategies of how the models should be related to reality often remain implicit. Differences in the epistemological foundations of models make it however, difficult to relate distinct models to each other and to ensure a cumulative expansion of knowledge. Concepts and the analytic language developed by philosophers of science can help to overcome these obstacles. This paper introduces some of these concepts to the modelling community. It also presents an epistemological framework that helps to clarify how one wishes to generate knowledge about reality by the means of one's model and that helps to relate models to each other. Since the interpretation of a model is strongly connected to the activities of model verification and validation, these two activities will be embedded into the framework and their respective epistemological roles will be clarified. The resulting meta-theoretical framework aligns well with recently proposed frameworks for model presentation and evaluation.

Streamlining Simulation Experiments with Agent-Based Models in Demography

Oliver Reinhardt, Jason Hilton, Tom Warnke, Jakub Bijak and Adelinde M. Uhrmacher
Journal of Artificial Societies and Social Simulation 21 (3) 9

Kyeywords: Agent-Based Modeling, Demography, Simulation Experimentation, Meta-Modeling
Abstract: In the last decade, the uptake of agent-based modeling in demography and other population sciences has been slowly increasing. Still, in such areas, where traditional data-driven, statistical approaches prevail, the hypothesis-driven design of agent-based models leads to questioning the validity of these models. Consequently, suitable means to increase the confidence into models and simulation results are required. To that end, explicit, replicable simulation experiments play a central role in model design and validation. However, the analysis of more complex models implies executing various experiments, each of which combines various methods. To streamline these experimentation processes a flexible computational simulation environment is necessary. With a new binding between SESSL -- an internal domain-specific language for simulation experiments -- and ML3 -- a simulator for linked lives designed specifically for agent-based demographic models -- we cater for these objectives and provide a powerful simulation tool. The proposed approach can serve as a foundation for current efforts of employing advanced and statistical model analysis of agent-based demographic models, as part of a wider process of iterative model building. We demonstrate its potential in specifying and executing different experiments with a simple model of return migration and a more complex model of social care.

Explaining the Emerging Influence of Culture, from Individual Influences to Collective Phenomena

Loïs Vanhée and Frank Dignum
Journal of Artificial Societies and Social Simulation 21 (4) 11

Kyeywords: Cultures, Social Simulations, Agent-Based Modelling
Abstract: This paper presents a simulation model and derived from it a theory to explain how known cultural influences on individual decisions lead to collective phenomena. This simulation models the evolution of a business organization, replicating key micro-level cultural influences on individual decisions (such as allocating and accepting tasks) and subsequent macro-level collective cultural phenomena (such as robustness and sensitivity to environmental complexity). As a result, we derived a theory on how to relate the influence of culture from individual decisions to collective outcomes, based on this simulation. We also point out that cultures appear to be related to specific sets of abstract, coherent and recurrent interaction patterns between individuals.

Towards the Right Ordering of the Sequence of Models for the Evolution of a Population Using Agent-Based Simulation

Morgane Dumont, Johan Barthelemy, Nam Huynh and Timoteo Carletti
Journal of Artificial Societies and Social Simulation 21 (4) 3

Kyeywords: Microsimulation, Agent-Based Modelling, Ordering of Models, Population Evolution, Robustness
Abstract: Agent based modelling is nowadays widely used in transport and the social science. Forecasting population evolution and analysing the impact of hypothetical policies are often the main goal of these developments. Such models are based on sub-models defining the interactions of agents either with other agents or with their environment. Sometimes, several models represent phenomena arising at the same time in the real life. Hence, the question of the order in which these sub-models need to be applied is very relevant for simulation outcomes. This paper aims to analyse and quantify the impact of the change in the order of sub-models on an evolving population modelled using TransMob. This software simulates the evolution of the population of a metropolitan area in South East of Sydney (Australia). It includes five principal models: ageing, death, birth, marriage and divorce. Each possible order implies slightly different results mainly driven by how agents' ageing is defined with respect to death. Furthermore, we present a calendar-based approach for the ordering that decreases the variability of final populations. Finally, guidelines are provided proposing general advices and recommendations for researchers designing discrete time agent-based models.

An Agent-Based Model of Rural Households’ Adaptation to Climate Change

Atesmachew Hailegiorgis, Andrew Crooks and Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 21 (4) 4

Kyeywords: Climate Change Adaptation, Agent-Based Modeling, Socio-Cognitive Behavior
Abstract: Future climate change is expected to have greater impacts on societies whose livelihoods rely on subsistence agricultural systems. Adaptation is essential for mitigating adverse effects of climate change, to sustain rural livelihoods and ensure future food security. We present an agent-based model, called OMOLAND-CA, which explores the impact of climate change on the adaptive capacity of rural communities in the South Omo Zone of Ethiopia. The purpose of the model is to answer research questions on the resilience and adaptive capacity of rural households with respect to variations in climate, socioeconomic factors, and land-use at the local level. Our model explicitly represents the socio-cognitive behavior of rural households toward climate change and resource flows that prompt agents to diversify their production strategy under different climatic conditions. Results from the model show that successive episodes of extreme events (e.g., droughts) affect the adaptive capacity of households, causing them to migrate from the region. Nonetheless, rural communities in the South Omo Zone, and in the model, manage to endure in spite of such harsh climatic change conditions.

A Generative Model of the Mutual Escalation of Anxiety Between Religious Groups

F. LeRon Shults, Ross Gore, Wesley J. Wildman, Christopher Lynch, Justin E. Lane and Monica Toft
Journal of Artificial Societies and Social Simulation 21 (4) 7

Kyeywords: Agent-Based Model, Religious Violence, Identity Fusion, Social Identity, Terror Management, Xenophobia
Abstract: We propose a generative agent-based model of the emergence and escalation of xenophobic anxiety in which individuals from two different religious groups encounter various hazards within an artificial society. The architecture of the model is informed by several empirically validated theories about the role of religion in intergroup conflict. Our results identify some of the conditions and mechanisms that engender the intensification of anxiety within and between religious groups. We define mutually escalating xenophobic anxiety as the increase of the average level of anxiety of the agents in both groups over time. Trace validation techniques show that the most common conditions under which longer periods of mutually escalating xenophobic anxiety occur are those in which the difference in the size of the groups is not too large and the agents experience social and contagion hazards at a level of intensity that meets or exceeds their thresholds for those hazards. Under these conditions agents will encounter out-group members more regularly, and perceive them as threats, generating mutually escalating xenophobic anxiety. The model’s capacity to grow the macro-level emergence of this phenomenon from micro-level agent behaviors and interactions provides the foundation for future work in this domain.

Opinion Dynamics Model Based on Cognitive Biases of Complex Agents

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 21 (4) 8

Kyeywords: Opinion Change, Motivated Reasoning, Confirmation Bias, Complex Agents, Agent Based Model
Abstract: We present an introduction to a novel way of simulating individual and group opinion dynamics, taking into account how various sources of information are filtered due to cognitive biases. The agent-based model presented here falls into the ‘complex agent’ category, in which the agents are described in considerably greater detail than in the simplest ‘spinson’ model. To describe agents’ information processing, we introduced mechanisms of updating individual belief distributions, relying on information processing. The open nature of this proposed model allows us to study the effects of various static and time-dependent biases and information filters. In particular, the paper compares the effects of two important psychological mechanisms: confirmation bias and politically motivated reasoning. This comparison has been prompted by recent experimental psychology work by Dan Kahan. Depending on the effectiveness of information filtering (agent bias), agents confronted with an objective information source can either reach a consensus based on truth, or remain divided despite the evidence. In general, this model might provide understanding into increasingly polarized modern societies, especially as it allows us to mix different types of filters: e.g., psychological, social, and algorithmic.

Using a Socioeconomic Segregation Burn-in Model to Initialise an Agent-Based Model for Infectious Diseases

Elizabeth Hunter, Brian Mac Namee and John D. Kelleher
Journal of Artificial Societies and Social Simulation 21 (4) 9

Kyeywords: Agent-Based, Socioeconomic Status, Infectious Disease, Simulation, Segregation, Model
Abstract: Socioeconomic status can have an important effect on health. In this paper we: (i) propose using house price data as a publicly available proxy for socioeconomic status to examine neighbourhood socioeconomic status at a more fine grained resolution than is available in Irish Central Statistics Office data; (ii) use a dissimilarity index to demonstrate and measure the existence of socioeconomic clustering at a neighbourhood level; (iii) demonstrate that using a standard ABM initialisation process based on CSO small area data results in ABMs systematically underestimating the socioeconomic clustering in Irish neighbourhoods; (iv) demonstrate that ABM models are better calibrated towards socioeconomic clustering after a segregation models has been run for a burn-in period after initial model setup; and (v) that running a socieconomic segregation model during the initiation of an ABM epidemiology model can have an effect on the outbreak patterns of the model. Our results support the use of segregation models as useful additions to the initiation process of ABM for epidemiology.

Comparing Spatial-Interaction and Hybrid Agent-Based Modelling Approaches: An Application to Location Analysis of Services

Lukasz Kowalski
Journal of Artificial Societies and Social Simulation 22 (1) 1

Kyeywords: Agent-Based Model, Spatial Interaction Model, Hybrid Model, Firm Location, Time-Space
Abstract: Aggregated models, such as spatial interaction (SIM) models are widely used in location analysis. Despite their popularity, there are certain limitations to their use. In particular, the method struggles to account for the passing-by population and multi-purpose trips of retail clients, temporal changes in accessibility and some bottom-up processes potentially important for services. Agent-based modelling (ABM) is a promising technique that attempts to address all these problems. However, it still lacks examples of real-world applications. This article aims to provide an example of how hybrid ABM (H-ABM) can be built on a SIM foundation, by incorporating most of its ideas, such as distance-decay function, facility attractiveness parameters and demand elasticity. The author aligns the two models as close as possible and compares their input data, calibration procedures and results. In the final analysis, the hybrid agent-based model proved to be more realistic because it incorporated the time-space variability of supply (i.e., limited numbers of available places in swimming pools), demand (the popularity of certain entry hours) and transport (traffic jams during rush hours). The spatial interaction model was much faster to execute and turned out to be more convenient for more straightforward applications, which do not require detailed data concerning individuals.

Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model

Francesco Pasimeni
Journal of Artificial Societies and Social Simulation 22 (1) 11

Kyeywords: Micro-Grids, Agent-Based Model, Innovation Diffusion, Energy Transition
Abstract: The electricity generation and distribution system in many developed economies is based primarily on the centralised grid. However, there is a need to shift from this traditional system to a newly more decentralised electricity system. This paper explores possible scenarios of adoption and diffusion of Micro-Grids (MGs) in Italy. An agent-based model is formulated to simulate the diffusion process as function of regional factors, subsidies and people's attitude. It assumes that MGs are purchased directly by communities of neighbours, which benefit from cost sharing. Results show high dependence of the diffusion process on regional factors: electricity demand, renewable potential and population. The model confirms that subsidies boost diffusion, mainly when they are regional-based rather than national-based. Higher green attitude accelerates diffusion and reduces environmental impact of the electricity system.

An Agent-Based Assessment of Health Vulnerability to Long-Term Particulate Exposure in Seoul Districts

Hyesop Shin and Mike Bithell
Journal of Artificial Societies and Social Simulation 22 (1) 12

Kyeywords: PM10, Exposure, Health Vulnerability, Agent-Based Model (ABM), Seoul
Abstract: This study presents a proof-of-concept agent-based model (ABM) of health vulnerability to long-term exposure to airborne particulate pollution, specifically to particles less than 10 micrometres in size (PM10), in Seoul, Korea. We estimated the differential effects of individual behaviour and social class across heterogeneous space in two districts, Gwanak and Gangnam. Three scenarios of seasonal PM10 change (business as usual: BAU, exponential increase: INC, and exponential decrease: DEC) and three scenarios of resilience were investigated, comparing the vulnerability rate both between and within each district. Our first result shows that the vulnerable groups in both districts, including those aged over 65, aged under 15, and with a low education level, increased sharply after 5,000 ticks (each tick corresponding to 1 day). This implies that disparities in health outcomes can be explained by socioeconomic status (SES), especially when the group is exposed over a long period. Additionally, while the overall risk population was larger in Gangnam in the AC100 scenarios, the recovery level from resilience scenarios decreased the risk population substantially, for example from 7.7% to 0.7%. Our second finding from the local-scale analysis indicates that most Gangnam sub-districts showed more variation both spatially and in different resilience scenarios, whereas Gwanak areas showed a uniform pattern regardless of earlier prevention. The implication for policy is that, while some areas, such as Gwanak, clearly require urgent mitigating action, areas like Gangnam may show a greater response to simpler corrections, but aggregating up to the district scale may miss particular areas that are more at risk. Future work should consider other pollutants as well as more sophisticated population and pollution modelling, coupled with explicit representation of transport and more careful treatment of individual doses and the associated health responses.

Promoting Sustainable Food Consumption: An Agent-Based Model About Outcomes of Small Shop Openings

Roberto Calisti, Primo Proietti and Andrea Marchini
Journal of Artificial Societies and Social Simulation 22 (1) 2

Kyeywords: Sustainable Consumption, Agent-Based Modelling, Farmers’ Market, Consumer Behaviour, Consumer Networks, Location-Allocation Problem
Abstract: A useful way of promoting sustainable food consumption is to consider the spread of food retail operations focused on food diversification, food specialization, and fresh and local products. These food shops are generally small, which is a great problem for survival against ruthless competition from supermarkets. Our research objective was to construct a simulation with an agent-based model, reproducing the local food consumption market and to investigate how a new, small food retailing shop interacts with this market. As a case study, the model simulates the opening of a small farmers’ market. The intent of the model is to reproduce the current status of consumption for food products within a certain territorial context and given time period, and to investigate how consumers’ behaviour changes with the opening of the new shop. As a result, we could predict changes in consumers’ habits, the economic positioning of new, small shops and its best location. This information is of considerable interest for farmers’ markets and also for policymakers.

A Dynamic Sustainability Analysis of Energy Landscapes in Egypt: A Spatial Agent-Based Model Combined with Multi-Criteria Decision Analysis

Mostafa Shaaban, Jürgen Scheffran, Jürgen Böhner and Mohamed S. Elsobki
Journal of Artificial Societies and Social Simulation 22 (1) 4

Kyeywords: Energy Security, Energy Landscape, Egypt, Multi-Criteria Decision Analysis, Agent-Based Modeling, Geographic Information System
Abstract: To respond to the emerging challenge of climate change, feasible strategies need to be formulated towards sustainable development and energy security on a national and international level. Lacking a dynamic sustainability assessment of technologies for electricity planning, this paper fills the gap with a multi-criteria and multi-stakeholder evaluation in an integrated assessment of energy systems. This allows to select the most preferred strategies for future planning of energy security in Egypt, with a focus on alternative energy pathways and a sustainable electricity supply mix up to 2100. A novel prototype model is used to integrate multi-criteria decision analysis (MCDA) as a premium decision support approach with agent-based modeling (ABM). This tool is popular in analyzing dynamic complex systems. A GIS-based spatial ABM analyzes future pathways for energy security in Egypt, depending on the preferences of agents for selected criteria to facilitate the transformation of energy landscapes. The study reveals significant temporal variations in the spatial ranking of technologies between actors in the energy sector over this period. We conclude that in order to attain a sustainable energy landscape, we should involve relevant stakeholders and analyze their interactions while considering local spatial conditions and key dimensions of sustainable development.

Application Independent Heuristic Data Merging Methodology for Sample-Free Agent Population Synthesis

Bhagya N. Wickramasinghe
Journal of Artificial Societies and Social Simulation 22 (1) 5

Kyeywords: Agent Based Modelling, Synthetic Population Reconstruction, Heuristic Population Construction, Sample Free, Integrating Models, Iterative Proportional Fitting
Abstract: This work proposes a novel application independent heuristics specifying framework and a household structures construction process, for sample-free population synthesis. The framework decouples heuristics and the algorithm by defining a set of generic constructs to specify heuristics on relationships and household structures. The algorithm uses Iterative Proportional Fitting, Monte Carlo sampling and combinatorial optimisation to synthesise the population. Decoupled nature of the system allows it to be used in different applications relatively easily by changing the heuristics. We demonstrate that this is a robust technique capable of producing synthetic agent populations highly consistent to input data distributions using two case studies. Apart from contributing to synthetic population reconstruction, this work will form one of the building blocks for integrating independently developed models to build complex new agent based models.

Impact of Basel III Countercyclical Measures on Financial Stability: An Agent-Based Model

Barbara Llacay and Gilbert Peffer
Journal of Artificial Societies and Social Simulation 22 (1) 6

Kyeywords: Agent-Based Simulation, Financial Markets, Financial Stability, Value-At-Risk, Countercyclical Regulation, Basel III
Abstract: The financial system is inherently procyclical, as it amplifies the course of economic cycles, and precisely one of the factors that has been suggested to exacerbate this procyclicality is the Basel regulation on capital requirements. After the recent credit crisis, international regulators have turned their eyes to countercyclical regulation as a solution to avoid similar episodes in the future. Countercyclical regulation aims at preventing excessive risk taking during booms to reduce the impact of losses suffered during recessions, for example increasing the capital requirements during the good times to improve the resilience of financial institutions at the downturn. The Basel Committee has already moved forward towards the adoption of countercyclical measures on a global scale: the Basel III Accord, published in December 2010, revises considerably the capital requirement rules to reduce their procyclicality. These new countercyclical measures will not be completely implemented until 2019, so their impact cannot be evaluated yet, and it is a crucial question whether they will be effective in reducing procyclicality and the appearance of crisis episodes such as the one experienced in 2007-08. For this reason, we present in this article an agent-based model aimed at analysing the effect of two countercyclical mechanisms introduced in Basel III: the countercyclical buffer and the stressed VaR. In particular, we focus on the impact of these mechanisms on the procyclicality induced by market risk requirements and, more specifically, by value-at-risk models, as it is a issue of crucial importance that has received scant attention in the modeling literature. The simulation results suggest that the adoption of both of these countercyclical measures improves market stability and reduces the emergence of crisis episodes.

Conflicts Induced by Different Responses to Land Expropriation Among the Farmers Involved During Urbanization in China

Haijun Bao, Xiaohe Wu, Haowen Wang, Qiuxiang Li, Yi Peng and Shibao Lu
Journal of Artificial Societies and Social Simulation 22 (1) 7

Kyeywords: Conflict of Interests, Land Expropriation, Evolutionary Game, Multi-Agent Simulation, Farmers
Abstract: Expropriation of collectively-owned land has become an important realistic path for achieving urban development and new urbanization in China considering the shortage of state-owned land. During this process, farmers involved in land expropriation are often in conflict with one another because of the asymmetry of their interests. Such conflicts have a considerable effect on social harmony and stability. However, few studies have investigated such conflict of interests between farmers. Therefore, this research analyzed game behavior for the conflict of interests among farmers. A two-dimensional symmetric evolutionary game model and a multi-agent simulation experiment were used to explore the conflicts induced by the farmers’ different responses to land expropriation. This research finds that the changing strategy choices of farmers in the evolutionary game on collectively owned land expropriation are the main reasons for the occurrence of villager’ confrontations and “nail households”. Results provide targeted policy recommendations for local governments to promote cooperation among farmers, thereby enhancing social harmony. The findings also serve as references for other countries and regions in dealing with intra-conflict of interests in land expropriation.

Innovation and Employment: An Agent-Based Approach

Fábio Neves, Pedro Campos and Sandra Silva
Journal of Artificial Societies and Social Simulation 22 (1) 8

Kyeywords: Agent-Based Model, Innovation, Automation, Employment
Abstract: While the effects of innovation on employment have been a controversial issue in economic literature for several years, this economic puzzle is particularly relevant nowadays. We are witnessing tremendous technological developments which threaten to disrupt the labour market, due to their potential for significantly automating human labour. As such, this paper presents a qualitative study of the dynamics underlying the relationship between innovation and employment, using an agent-based model developed in Python. The model represents an economy populated by firms able to perform either Product Innovation (leading to the discovery of new tasks, which require human labour) or Process Innovation (leading to the automation of tasks previously performed by humans). The analysis led to three major conclusions, valid in this context. The first takeaway is that the Employment Rate in a given economy is dependent on the automation potential of the tasks in that economy and dependent on the type of innovation performed by firms in that economy (with Product Innovation having a positive effect on employment and Process Innovation having a negative effect). Second, in any given economy, if firms’ propensity for product and process innovation, as well as the automation potential of their tasks are stable over time, the Employment Rate in that economy will tend towards stability over time. The third conclusion is that higher levels of Process Innovation and lower levels of Product Innovation, lead to a more intense decline of wage shares and to a wider gap between employee productivity growth and wage growth.

The Value of Values and Norms in Social Simulation

Rijk Mercuur, Virginia Dignum and Catholijn Jonker
Journal of Artificial Societies and Social Simulation 22 (1) 9

Kyeywords: Human Values, Norms, Ultimatum Game, Empirical Data, Agent-Based Model
Abstract: Social simulations gain strength when agent behaviour can (1) represent human behaviour and (2) be explained in understandable terms. Agents with values and norms lead to simulation results that meet human needs for explanations, but have not been tested on their ability to reproduce human behaviour. This paper compares empirical data on human behaviour to simulated data on agents with values and norms in a psychological experiment on dividing money: the ultimatum game. We find that our agent model with values and norms produces aggregate behaviour that falls within the 95% confidence interval wherein human behaviour lies more often than other tested agent models. A main insight is that values serve as a static component in agent behaviour, whereas norms serve as a dynamic component.

Synchronizing Histories of Exposure and Demography: The Construction of an Agent-Based Model of the Ecuadorian Amazon Colonization and Exposure to Oil Pollution Hazards

Noudéhouénou Lionel Jaderne Houssou, Juan Durango Cordero, Audren Bouadjio-Boulic, Lucie Morin, Nicolas Maestripieri, Sylvain Ferrant, Mahamadou Belem, Jose Ignacio Pelaez Sanchez, Melio Saenz, Emilie Lerigoleur, Arnaud Elger, Benoit Gaudou, Laurence Maurice and Mehdi Saqalli
Journal of Artificial Societies and Social Simulation 22 (2) 1

Kyeywords: Ecuadorian Amazon, Oil Pollution Exposure, Agent-Based Modeling, Colonization Demography, Historical Modeling Reconstruction
Abstract: Since the 1970s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures affecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution affecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.

Simulating the Actions of Commuters Using a Multi-Agent System

Neil Urquhart, Simon Powers, Zoe Wall, Achille Fonzone, Jiaqi Ge and Gary Polhill
Journal of Artificial Societies and Social Simulation 22 (2) 10

Kyeywords: Transport Mode Choice, Transport Network, BDI Agent
Abstract: The activity of commuting to and from a place of work affects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the effects of changes to working patterns and locations and inform decision making. In this paper, we outline an agent-based software framework that combines real-world data from multiple sources to simulate the actions of commuters. We demonstrate the framework using data supplied by an employer based in the City of Edinburgh UK. We demonstrate that the BDI-inspired decision-making framework used is capable of forecasting the transportation modes to be used. Finally, we present a case study, demonstrating the use of the framework to predict the impact of moving staff within the organisation to a new work site.

Participatory Modeling and Simulation with the GAMA Platform

Patrick Taillandier, Arnaud Grignard, Nicolas Marilleau, Damien Philippon, Quang-Nghi Huynh, Benoit Gaudou and Alexis Drogoul
Journal of Artificial Societies and Social Simulation 22 (2) 3

Kyeywords: Agent-Based Simulation, Participatory Modeling, Participatory Simulation, Serious Game
Abstract: In recent years, agent-based simulation has become an important tool to study complex systems. However, the models produced are rarely used for decision-making support because stakeholders are often not involved in the modeling and simulation processes. Indeed, if several tools dedicated to participatory modeling and simulation exist, they are limited to the design of simple - KISS - models, which limit their potential impact. In this article, we present the participatory tools integrated within the GAMA modeling and simulation platform. These tools, which take advantage of the GAMA platform concerning the definition of rich - KIDS - models, allows to build models graphically and develop distributed serious games in a simple way. Several application examples illustrate their use and potential.

Endogenous Changes in Public Opinion Dynamics

Francisco J. León-Medina
Journal of Artificial Societies and Social Simulation 22 (2) 4

Kyeywords: Opinion Dynamics, Mechanism Explanation, Agent-Based Modeling, Homophily, Social Influence, Social Network
Abstract: Opinion dynamics models usually center on explaining how macro-level regularities in public opinion (uniformity, polarization or clusterization) emerge as the effect of local interactions of a population with an initial random distribution of opinions. However, with only a few exceptions, the understanding of patterns of public opinion change has generally been dismissed in this literature. To address this theoretical gap in our understanding of opinion dynamics, we built a multi-agent simulation model that could help to identify some mechanisms underlying changes in public opinion. Our goal was to build a model whose behavior could show different types of endogenously (not induced by the researcher) triggered transitions (rapid or slow, radical or soft). The paper formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions. According to our findings, only scale-free networks show fluctuations in public opinion. Public opinion changes in this model appear as a diffusion process of individual opinion shifts that is triggered by an opinion change of a highly connected agent. The frequency, rapidity and radicalness of the diffusion, and hence of public opinion fluctuations, positively depends on how influential external information is in individual opinions and negatively depends on how homophilic social interactions are.

ABOOMS: Overcoming the Hurdles of Continuous-Time Public Goods Games with a Simulation-Based Approach

Tuong Manh Vu, Christian Wagner and Peer-Olaf Siebers
Journal of Artificial Societies and Social Simulation 22 (2) 7

Kyeywords: Agent-Based Modelling and Simulation, Continuous-Time Public Goods Game, Software Engineering, Agent-Based Computational Economics, Object-Oriented Analysis and Design
Abstract: Public Goods Games (PGGs) are a standard experimental economic approach to studying cooperative behaviour. There are two types of games: discrete-time and continuous-time PGGs. While discrete-time PGGs (one-shot decisions about contributions to public goods) can be easily done as lab experiments, continuous-time PGGs (where participants can change contributions at any time) are much harder to realise within a lab environment. This is mainly because it is difficult to consider events happening in continuous time in lab experiments. Simulation offers an opportunity to support real-world lab experiments and is well suited to explore continuous-time PGGs. In this paper, we show how to apply our recently developed ABOOMS (Agent-Based Object-Oriented Modelling and Simulation) development framework to create models for simulation-supported continuous-time PGG studies. The ABOOMS framework utilizes Software Engineering techniques to support the development at the macro level (considering the overall study lifecycle) and at the micro level (considering individual steps related to simulation model development). Our case study shows that outputs from the simulation-supported continuous-time PGG generate dynamics that do not exist in discrete-time setting, highlighting the fact that it is important to study both, discrete and continuous-time PGGs.

Network Meta-Metrics: Using Evolutionary Computation to Identify Effective Indicators of Epidemiological Vulnerability in a Livestock Production System Model

Serge Wiltshire, Asim Zia, Christopher Koliba, Gabriela Bucini, Eric Clark, Scott Merrill, Julie Smith and Susan Moegenburg
Journal of Artificial Societies and Social Simulation 22 (2) 8

Kyeywords: Agent-Based Modeling, Network Analytics, Computational Epidemiology, Evolutionary Computation, Livestock Production
Abstract: We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call "meta-metrics" — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.

Contract Farming in the Mekong Delta's Rice Supply Chain: Insights from an Agent-Based Modeling Study

Hung Khanh Nguyen, Raymond Chiong, Manuel Chica, Richard Middleton and Dung Thi Kim Pham
Journal of Artificial Societies and Social Simulation 22 (3) 1

Kyeywords: Agent-Based Modeling, Contract Farming, Agricultural Supply Chain, Computational Simulation
Abstract: In this paper, we use agent-based modeling (ABM) to study different obstacles to the expansion of contract rice farming in the context of Mekong Delta (MKD)'s rice supply chain. ABM is a bottom-up approach for modeling the dynamics of interactions among individuals and complex combinations of various factors (e.g., economic, social or environmental). Our agent-based contract farming model focuses on two critical components of contractual relationship, namely financial incentives and trust. We incorporate the actual recurrent fluctuations of spot market prices, which induce both contractor and farmer agents to renege on the agreement. The agent-based model is then used to predict emergent system-wide behaviors and compare counterfactual scenarios of different policies and initiatives on maintaining the contract rice farming scheme. Simulation results firstly show that a fully-equipped contractor who opportunistically exploits a relatively small proportion (less than 10%) of the contracted farmers in most instances can outperform spot market-based contractors in terms of average profit achieved for each crop. Secondly, a committed contractor who offers lower purchasing prices than the most typical rate can obtain better earnings per ton of rice as well as higher profit per crop. However, those contractors in both cases could not enlarge their contract farming scheme, since either farmers' trust toward them decreases gradually or their offers are unable to compete with the benefits from a competitor or the spot market. Thirdly, the results are also in agreement with the existing literature that the contract farming scheme is not a cost-effective method for buyers with limited rice processing capacity, which is a common situation among the contractors in the MKD region. These results yield significant insights into the difficulty in expanding the agricultural contracting program in the MKD's rice supply chain.

Coevolutionary Characteristics of Knowledge Diffusion and Knowledge Network Structures: A GA-ABM Model

Junhyok Jang, Xiaofeng Ju, Unsok Ryu and Hyonchol Om
Journal of Artificial Societies and Social Simulation 22 (3) 3

Kyeywords: Knowledge Diffusion, Knowledge Network, Coevolutionary, Genetic Algorithm, Agent-Based Modeling
Abstract: The co-evolutionary dynamics of knowledge diffusion and network structure in knowledge management is a recent research trend in the field of complex networks. The aim of this study is to improve the knowledge diffusion performance of knowledge networks including personnel, innovative organizations and companies. In order to study the co-evolutionary dynamics of knowledge diffusion and network structure, we developed a genetic algorithm-agent based model (GA-ABM) by combining a genetic algorithm (GA) and an agent-based model (ABM). Our simulations show that our GA-ABM improved the average knowledge stock and knowledge growth rate of the whole network, compared with several other models. In addition, it was shown that the topological structure of the optimal network obtained by GA-ABM has the property of a random network. Finally, we found that the clustering coefficients of agents are not significant to improve knowledge diffusion performance.

Modelling Contingent Technology Adoption in Farming Irrigation Communities

Antoni Perello-Moragues, Pablo Noriega and Manel Poch
Journal of Artificial Societies and Social Simulation 22 (4) 1

Kyeywords: Agent-Based Modeling, Innovation Diffusion, Policy-Making, Irrigation Agriculture, Socio-Hydrology
Abstract: Of all the uses of water, agriculture is the one that requires the greatest proportion of resources worldwide. Consequently, it is a salient subject for environmental policy-making, and adoption of modern irrigation systems is a key means to improve water use efficiency. In this paper we present an agent-based model of the adoption process —known as "modernisation"— of a community constituted by farmer agents. The phenomenon is approached as a contingent innovation adoption: a first stage to reach a collective agreement followed by an individual adoption decision. The model is based on historical data from two Spanish irrigation communities during the period 1975-2010. Results suggest that individual profits and farm extension (as proxy of social influence) are suitable assumptions when modelling the modernisation of communities in regions where agriculture is strongly market-oriented and water is scarce. These encouraging results point towards the interest of more sophisticated socio-cognitive modelling within a more realistic socio-hydrologic context.

Space Matters: Extending Sensitivity Analysis to Initial Spatial Conditions in Geosimulation Models

Juste Raimbault, Clémentine Cottineau, Marion Le Texier, Florent Le Nechet and Romain Reuillon
Journal of Artificial Societies and Social Simulation 22 (4) 10

Kyeywords: Space, Initial Conditions, Sensitivity, Agent-Based Models
Abstract: Although simulation models of socio-spatial systems in general and agent-based models in particular represent a fantastic opportunity to explore socio-spatial behaviours and to test a variety of scenarios for public policy, the validity of generative models is uncertain unless their results are proven robust and representative of 'real-world' conditions. Sensitivity analysis usually includes the analysis of the effect of stochasticity on the variability of results, as well as the effects of small parameter changes. However, initial spatial conditions are usually not modified systematically in socio-spatial models, thus leaving unexplored the effect of initial spatial arrangements on the interactions of agents with one another as well as with their environment. In this article, we present a method to assess the effect of variation of some initial spatial conditions on simulation models, using a systematic geometric structures generator in order to create density grids with which socio-spatial simulation models are initialised. We show, with the example of two classical agent-based models (Schelling's model of segregation and Sugarscape's model of unequal societies) and a straightforward open-source workflow using high performance computing, that the effect of initial spatial arrangements is significant on the two models. We wish to illustrate the potential interest of adding spatial sensitivity analysis during the exploration of models for both modellers and thematic specialists.

How to Manage Individual Forgetting: Analysis and Comparison of Different Knowledge Management Strategies

Jie Yan, Renjing Liu, Zhengwen He and Xiaobo Wan
Journal of Artificial Societies and Social Simulation 22 (4) 2

Kyeywords: Forgetting, Knowledge Management Strategy, Exploration-Exploitation, Agent-Based Modeling
Abstract: The creation, transfer and retention of knowledge in an organization has always been the focus of knowledge management researchers; however, one aspect of the dynamics of knowledge, i.e., forgetting, has received comparatively limited attention. To fill this research gap, we extend the basic simulation model proposed by March by incorporating forgetting and three knowledge management strategies, i.e., personalization, codification, and mixed, to explore the impacts of different knowledge management strategies and forgetting on the organizational knowledge level. The simulation results not only clarify the specific measures used to manage individual forgetting in each knowledge management strategy but also identify the boundary conditions under which knowledge management strategies should be adopted under different conditions.

The Dynamics of Language Minorities: Evidence from an Agent-Based Model of Language Contact

Marco Civico
Journal of Artificial Societies and Social Simulation 22 (4) 3

Kyeywords: Language, Multilingualism, Minority, Complexity, Agent-Based Modelling, Population Dynamics
Abstract: This article discusses the adoption of a complexity theory approach to study the dynamics of language contact within multilingual communities. It develops an agent-based model that simulates the dynamics of communication within a community where a minority and a majority group coexist. The individual choice of language for communication is based on a number of simple rules derived from a review of the main literature on the topic of language contact. These rules are then combined with different variables, such as the rate of exogamy of the minority group and the presence of relevant education policies, to estimate the trends of assimilation of the minority group into the majority one. The model is validated using actually observed data from the case of Romansh speakers in the canton of Grisons, Switzerland. The data collected from the simulations are then analysed by means of regression techniques. This paper shows that macro-level language contact dynamics can be explained by relatively simple micro-level behavioural patterns and that intergenerational transmission is crucial for the long-term survival of minority-language groups.

Common Dynamics of Identity and Immigration: The Roles of Mobility and Democracy

Nicolas Houy
Journal of Artificial Societies and Social Simulation 22 (4) 4

Kyeywords: Identity, Immigration, Democracy, Mobility, Schelling Model, Agent-Based Model
Abstract: We look at the dynamics of identity and immigration in a setting in which political decisions regarding immigration are made by a majoritarian democratic process and location is endogenous. We introduced an agent-based model that allowed us to explain the following facts: When individuals are not allowed to choose their own location, the ratio of immigrants in the population is close to optimal and assimilation works well. On the contrary, when individuals are allowed to move, clusters of different types of populations form. This has the following consequences: assimilation becomes more difficult by formation of closed communities and therefore the native identity can only survive if a large level of immigration is supported by individuals protected from its consequences and vote with local information or consideration. Even in the latter case, temporary outbursts of anti-immigration policy can occur. These results should be understood in the recent context of increasing salience of identity concerns and the following positive electoral results for the so-called populist movements in Western countries.

Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models

Christopher Weimer, J.O. Miller, Raymond Hill and Douglas Hodson
Journal of Artificial Societies and Social Simulation 22 (4) 5

Kyeywords: Opinion Dynamics, Agent-Based Modeling, Scheduling, Asynchronous, Synchronous
Abstract: Opinion dynamics models are an important field of study within the agent-based modeling community. Agent scheduling elements within existing opinion dynamics models vary but are largely unjustified and only minimally explained. Furthermore, previous research on the impact of scheduling is scarce, partially due to a lack of a common taxonomy with which to discuss and compare schedules. The Synchrony, Actor type, Scale (SAS) taxonomy is presented, which aims to provide a common lexicon for agent scheduling in opinion dynamics models. This is demonstrated using a generalized repeated averaging model (GRAM) and a generalized bounded confidence model (GBCM). Significant differences in model outcomes with varied schedules are given, along with the results of intentional model biasing using only schedule variation. We call on opinion dynamics modelers to make explicit their choice of schedule and to justify that choice based on realistic social phenomena.

Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis

Martin Klein, Ulrich J. Frey and Matthias Reeg
Journal of Artificial Societies and Social Simulation 22 (4) 6

Kyeywords: Agent Based Modelling, Computational Economics, Energy Systems Analysis, Modelling Guidelines, Policy Modelling, Energy Scenarios
Abstract: This paper tries to show the various roles agent-based modeling and simulation (ABMS) can play in technology and policy assessment of energy systems. We examine the advantages of ABMS methods using three case studies of electricity market models as example (AMIRIS, EMLab-Generation and PowerACE). In particular, we argue why ABMS might serve as framework for many future energy system models that integrate many different algorithms. We then discuss practical and theoretical problems in the development, validation and assessment of energy-system-analytical ABMS and conclude with an outlook and recommendations for energy system modellers who consider incorporating ABMS into their modelling toolbox.

Agent-Based Modelling of Charging Behaviour of Electric Vehicle Drivers

Mart van der Kam, Annemijn Peters, Wilfried van Sark and Floor Alkemade
Journal of Artificial Societies and Social Simulation 22 (4) 7

Kyeywords: Electric Vehicles, Intermittent Renewables, Smart Charging, Environmental Self-Identity, Range Anxiety, Agent-Based Model
Abstract: The combination of electric vehicles (EVs) and intermittent renewable energy sources has received increasing attention over the last few years. Not only does charging electric vehicles with renewable energy realize their true potential as a clean mode of transport, charging electric vehicles at times of peaks in renewable energy production can help large scale integration of renewable energy in the existing energy infrastructure. We present an agent-based model that investigates the potential contribution of this combination. More specifically, we investigate the potential effects of different kinds of policy interventions on aggregate EV charging patterns. The policy interventions include financial incentives, automated smart charging, information campaigns and social charging. We investigate how well the resulting charging patterns are aligned with renewable energy production and how much they affect user satisfaction of EV drivers. Where possible, we integrate empirical data in our model, to ensure realistic scenarios. We use recent theory from environmental psychology to determine agent behaviour, contrary to earlier simulation models, which have focused only on technical and financial considerations. Based on our simulation results, we articulate some policy recommendations. Furthermore, we point to future research directions for environmental psychology scholars and modelers who want to use theory to inform simulation models of energy systems.

An Innovative Approach to Multi-Method Integrated Assessment Modelling of Global Climate Change

Peer-Olaf Siebers, Zhi En Lim, Grazziela P. Figueredo and James Hey
Journal of Artificial Societies and Social Simulation 23 (1) 10

Kyeywords: Integrated Assessment Modelling, Climate Change, Agent-Based Modelling, System Dynamics Modelling, Methodological Advance, Hybridisation, Scalability
Abstract: Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, often used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers.

Land-Use Changes in Distant Places: Implementation of a Telecoupled Agent-Based Model

Yue Dou, Guolin Yao, Anna Herzberger, Ramon Felipe Bicudo da Silva, Qian Song, Ciara Hovis, Mateus Batistella, Emilio Moran, Wenbin Wu and Jianguo Liu
Journal of Artificial Societies and Social Simulation 23 (1) 11

Kyeywords: Telecoupling, Agent-Based Model, Land System, Land-Use Change, Soybean Trade, ODD+D
Abstract: International agricultural trade has changed land uses in trading countries, altering global food security and environmental sustainability. Studies have concluded that local land-use drivers are largely from global sources (e.g., trade increases deforestation in exporting countries). However, little is known about how these local land-use changes affect distant locations, namely the feedback between them. Yet these distant impacts and feedbacks can be significant for governing local land systems. The framework of telecoupling (i.e., socioeconomic-environmental interactions between distant places) has been shown to be an effective conceptual tool to study international trade and the associated socio-economic and environmental impacts. However, a systems simulation tool to quantify the telecoupled causes and effects is still lacking. Here, we construct a new type of agent-based model (ABM) that can simulate land-use changes at multiple distant places (namely TeleABM, telecoupled agent-based model). We use soybean trade between Brazil and China as an example, where Brazil is the sending system and China is the receiving system because they are the world’s largest soybean exporter and importer respectively. We select one representative county in each country to calibrate and validate the model with spatio-temporal analysis of historical land-use changes and the empirical analysis of household survey data. We describe the model following the ODD+D protocol, and validate the model results in each location respectively. We then illustrate how the aggregated farmer agents’ land-use behaviors in the sending system result in land-use changes in the receiving system, and vice versa. One scenario example (i.e., a high-tariff scenario) is given to demonstrate the results of TeleABM. Such a model allows us to advance the understanding of telecoupling features and the influence on land system science, and to test hypotheses about complex coupled human-natural systems (e.g., cascading effect).

Editorial: Meeting Grand Challenges in Agent-Based Models

Li An, Volker Grimm and Billie L. Turner II
Journal of Artificial Societies and Social Simulation 23 (1) 13

Kyeywords: Agent-Based Modeling, Complex Systems, System Integration, Social-Ecological Systems, Overview
Abstract: This editorial paper reviews the state of the science about agent-based modeling (ABM), pointing out the strengths and weaknesses of ABM. This paper also highlights several impending tasks that warrant special attention in order to improve the science and application of ABM: Modeling human decisions, ABM transparency and reusability, validation of ABM, ABM software and big data ABM, and ABM theories. Six innovative papers that are included in the special issue are summarized, and their connections to the ABM impending tasks are brought to attention. The authors hope that this special issue will help prioritize specific resources and activities in relation to ABM advances, leading to coordinated, joint efforts and initiatives to advance the science and technology behind ABM.

Agent-Based Land Change Modeling of a Large Watershed: Space-Time Locations of Critical Threshold

Wenwu Tang and Jianxin Yang
Journal of Artificial Societies and Social Simulation 23 (1) 15

Kyeywords: Agent-Based Model, Land Use and Land Cover Change, Critical Threshold, Water Quality, North Carolina
Abstract: Land use and land cover change has been recognized to have significant environmental impacts in a watershed, such as regulation of water quality. However, the identification of potential regions that are sensitive to land change activities for the protection of water quality poses a grand challenge particularly in a large watershed. These potential regions are often associated with critical thresholds in terms of, for example, water quality. In this study, we developed an agent-based land change model to investigate the relationship between land development activities and water quality in eight North Carolina counties that cover the lower High Rock Lake Watershed area. This agent-based model, which is empirically calibrated, is used to identify space-time locations of those regions at critical thresholds of water quality in this study area. Our experimental results suggest that land development as a form of system stress is of pivotal importance in affecting water quality at sub watershed level and the state transition of water quality. The agent-based model developed in this study provides solid support for investigations on the impact of land development under alternative scenarios in a large watershed.

How Policy Decisions Affect Refugee Journeys in South Sudan: A Study Using Automated Ensemble Simulations

Diana Suleimenova and Derek Groen
Journal of Artificial Societies and Social Simulation 23 (1) 2

Kyeywords: Refugee Modelling, Agent-Based Modelling, Automation Toolkit, Policy Decisions, Validation, Sensitivity Analysis
Abstract: Forced displacement has a huge impact on society today, as more than 68 million people are forcibly displaced worldwide. Existing methods for forecasting the arrival of migrants, especially refugees, may help us to better allocate humanitarian support and protection. However, few researchers have investigated the effects of policy decisions, such as border closures, on the movement of these refugees. Recently established simulation development approaches have made it possible to conduct such a study. In this paper, we use such an approach to investigate the effect of policy decisions on refugee arrivals for the South Sudan refugee crisis. To make such a study feasible in terms of human effort, we rely on agent-based modelling, and have automated several phases of simulation development using the FabFlee automation toolkit. We observe a decrease in the average relative difference from 0.615 to 0.499 as we improved the simulation model with additional information. Moreover, we conclude that the border closure and a reduction in camp capacity induce fewer refugee arrivals and more time spend travelling to other camps. While a border opening and an increase in camp capacity result in a limited increase in refugee arrivals at the destination camps. To the best of our knowledge, we are the first to conduct such an investigation for this conflict.

Methodological Issues of Spatial Agent-Based Models

Steven Manson, Li An, Keith C. Clarke, Alison Heppenstall, Jennifer Koch, Brittany Krzyzanowski, Fraser Morgan, David O'Sullivan, Bryan C Runck, Eric Shook and Leigh Tesfatsion
Journal of Artificial Societies and Social Simulation 23 (1) 3

Kyeywords: Spatial, Agent-Based Model, Methods, Human-Environment Systems
Abstract: Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

LevelSpace: A NetLogo Extension for Multi-Level Agent-Based Modeling

Arthur Hjorth, Bryan Head, Corey Brady and Uri Wilensky
Journal of Artificial Societies and Social Simulation 23 (1) 4

Kyeywords: Multi-Level, Agent-Based Modeling, Modeling Tools, Netlogo
Abstract: Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM – coupling of heterogeneous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.

Cascading Impacts of Payments for Ecosystem Services in Complex Human-Environment Systems

Li An, Judy Mak, Shuang Yang, Rebecca Lewison, Douglas A. Stow, Hsiang Ling Chen, Weihua Xu, Lei Shi and Yu Hsin Tsai
Journal of Artificial Societies and Social Simulation 23 (1) 5

Kyeywords: Agent-Based Modeling, Payments for Ecosystem Services, Complex Human-Environment Systems, Guizhou Snub-Nosed Monkey, Migration, Land Use
Abstract: The theory and practice associated with payments for ecosystem services (PES) feature a variety of piecemeal studies related to impacts of socioeconomic, demographic, and environmental variables, lacking efforts in understanding their mutual relationships in a spatially and temporally explicit manner. In addition, PES literature is short of ecological metrics that document the consequences of PES other than land use and land cover and its change. Building on detailed survey data from Fanjingshan National Nature Reserve (FNNR), China, we developed and tested an agent-based model to study the complex interactions among human livelihoods (migration and resource extraction in particular), PES, and the Guizhou golden monkey habitat occupancy over 20 years. We then performed simulation-based experiments testing social and ecological impacts of PES payments as well as human population pressures. The results show that with a steady increase in outmigration, the number of land parcels enrolled in one of China’s major PES programs tends to increase, reach a peak, and then slowly decline, showing a convex trend that converges to a stable number of enrolled parcels regardless of payment levels. Simulated monkey occupancy responds to changes in PES payment levels substantially in edge areas of FNNR. Our model is not only useful for FNNR, but also applicable as a platform to study and further understand human and ecological roles of PES in many other complex human-environment systems, shedding light into key elements, interactions, or relationships in the systems that PES researchers and practitioners should bear in mind. Our research contributes to establishing a scientific basis of PES science that incorporates features in complex systems, offering more realistic, spatially and temporally explicit insights related to PES policy or related interventions.

‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models

Arika Ligmann-Zielinska, Peer-Olaf Siebers, Nicholas R Magliocca, Dawn C. Parker, Volker Grimm, Jing Du, Martin Cenek, Viktoriia Radchuk, Nazia N. Arbab, Sheng Li, Uta Berger, Rajiv Paudel, Derek T. Robinson, Piotr Jankowski, Li An and Xinyue Ye
Journal of Artificial Societies and Social Simulation 23 (1) 6

Kyeywords: Sensitivity Analysis, Agent-Based Model, Individual-Based Model, Review
Abstract: Designing, implementing, and applying agent-based models (ABMs) requires a structured approach, part of which is a comprehensive analysis of the output to input variability in the form of uncertainty and sensitivity analysis (SA). The objective of this paper is to assist in choosing, for a given ABM, the most appropriate methods of SA. We argue that no single SA method fits all ABMs and that different methods of SA should be used based on the overarching purpose of the model. For example, abstract exploratory models that focus on a deeper understanding of the target system and its properties are fed with only the most critical data representing patterns or stylized facts. For them, simple SA methods may be sufficient in capturing the dependencies between the output-input spaces. In contrast, applied models used in scenario and policy-analysis are usually more complex and data-rich because a higher level of realism is required. Here the choice of a more sophisticated SA may be critical in establishing the robustness of the results before the model (or its results) can be passed on to end-users. Accordingly, we present a roadmap that guides ABM developers through the process of performing SA that best fits the purpose of their ABM. This roadmap covers a wide range of ABM applications and advocates for the routine use of global methods that capture input interactions and are, therefore, mandatory if scientists want to recognize all sensitivities. As part of this roadmap, we report on frontier SA methods emerging in recent years: a) handling temporal and spatial outputs, b) using the whole output distribution of a result rather than its variance, c) looking at topological relationships between input data points rather than their values, and d) looking into the ABM black box – finding behavioral primitives and using them to study complex system characteristics like regime shifts, tipping points, and condensation versus dissipation of collective system behavior.

Calibrating Agent-Based Models with Linear Regressions

Ernesto Carrella, Richard Bailey and Jens Koed Madsen
Journal of Artificial Societies and Social Simulation 23 (1) 7

Kyeywords: Agent-Based Models, Indirect Inference, Estimation, Calibration, Simulated Minimum Distance, Approximate Bayesian Computation
Abstract: In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification, we can extend this approach to model selection. We present five example estimations: a statistical fit, a biological individual-based model, a simple real business cycle model, a non-linear biological simulation and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and uses them to parametrize models without running any direct minimization.

An Agent-Based Model of Firm Size Distribution and Collaborative Innovation

Inyoung Hwang
Journal of Artificial Societies and Social Simulation 23 (1) 9

Kyeywords: Agent-Based Modelling, Prisoner’s Dilemma, Pavlovian Cooperation, Collaborative Innovation, Firm Size Distribution, ICT Industry
Abstract: ICT-based Collaborative innovation has a significant impact on the economy by facilitating technological convergence and promoting innovation in other industries. However, research on innovation suggests that polarization in firm size distribution, which has grown since the early 2000s, can interfere with collaborative innovation among firms. In this paper, I modelled firms’ decision-making processes that led to collaborative innovation as a spatial N-person iterated Prisoner’s dilemma (NIPD) game using collaborative innovation data from Korean ICT firms. Using an agent-based model, I experimented with the effects of firm size heterogeneity on collaborative innovation. The simulation experiment results reveal that collaborative innovation in the industry increases as the size heterogeneity decreases. Findings suggest that policies promoting collaborative innovation should focus on mitigating structural inequalities in the industry.

Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action

Flaminio Squazzoni, Gary Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Émile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 23 (2) 10

Kyeywords: COVID-19, Pandemic Disease, Agent-Based Models, Modelling, Policy, Data
Abstract: The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Phase Transition in the Social Impact Model of Opinion Formation in Scale-Free Networks: The Social Power Effect

Alireza Mansouri and Fattaneh Taghiyareh
Journal of Artificial Societies and Social Simulation 23 (2) 3

Kyeywords: Opinion Formation, Noise, Agent-Based Modeling, Social Impact Model, Phase Transition
Abstract: Human interactions and opinion exchanges lead to social opinion dynamics, which is well described by opinion formation models. In these models, a random parameter is usually considered as the system noise, indicating the individual's inexplicable opinion changes. This noise could be an indicator of any other influential factors, such as public media, affects, and emotions. We study phase transitions, changes from one social phase to another, for various noise levels in a discrete opinion formation model based on the social impact theory with a scale-free random network as its interaction network topology. We also generate another similar model using the concept of social power based on the agents' node degrees in the interaction network as an estimation for their persuasiveness and supportiveness strengths and compare both models from phase transition viewpoint. We show by agent-based simulation and analytical considerations how opinion phases, including majority and non-majority, are formed in terms of the initial population of agents in opinion groups and noise levels. Two factors affect the system phase in equilibrium when the noise level increases: breaking up more segregated groups and dominance of stochastic behavior of the agents on their deterministic behavior. In the high enough noise levels, the system reaches a non-majority phase in equilibrium, regardless of the initial combination of opinion groups. In relatively low noise levels, the original model and the model whose agents' strengths are proportional to their centrality have different behaviors. The presence of a few high-connected influential leaders in the latter model consequences a different behavior in reaching equilibrium phase and different thresholds of noise levels for phase transitions.

Do Farm Characteristics or Social Dynamics Explain the Conversion to Organic Farming by Dairy Farmers? An Agent-Based Model of Dairy Farming in 27 French Cantons

Qing Xu, Sylvie Huet, Eric Perret and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 23 (2) 4

Kyeywords: Organic Farming, Adaptation, Theory of Reasoned Action, Agent-Based Model, Social Influence, Credibility
Abstract: The drivers of conversion to organic farming, which is still a residual choice in agriculture, are poorly understood. Many scholars argue that farm characteristics can determine this choice but do not exclude the role of social dynamics. To study this issue, we developed an agent-based model in which agents' decisions to shift to organic farming are based on a comparison between satisfaction with the current situation and potential satisfaction with an alternative farming strategy. A farmer agent’s satisfaction is modelled using the Theory of Reasoned Action. This makes it necessary to compare an agent's productions over time with those of other agents to whom the former attributes considerable credibility (“important others”). Moreover, farmers make technical changes that affect their productions by imitating other credible farmers. While we first used this model to examine simple and abstract farm populations, here we also adapted it for use with data from an Agricultural Census concerning the farm characteristics of dairy farming in 27 French “cantons”. Based on domain expertise, data and previous research, we propose certain laws for modelling the impact of conversion on the farm production of milk and the environment. The simulations with “real” populations of farms confirm the important impact of farm characteristics. However, our results also suggest a complex impact of social dynamics that can favour or impede the diffusion of organic farming through dynamic implicit networks of similarity and credibility. We confirm the great importance of demographic changes in farm characteristics.

Tension Between Stability and Representativeness in a Democratic Setting

Victorien Barbet, Juliette Rouchier, Noé Guiraud and Vincent Laperrière
Journal of Artificial Societies and Social Simulation 23 (2) 5

Kyeywords: Agent-Based Model, Communication, Opinion Dynamics, Democracy, Non-Profit Organization, Short Food Chain
Abstract: We present a model showing the evolution of an organization of agents who discuss democratically about good practices. This model feeds on a field study we did for about twelve years in France where we followed NPOs, called AMAP, and observed their construction through time at the regional and national level. Most of the hypothesis we make are here either based on the literature on opinion diffusion or on the results of our field study. By defining dynamics where agents influence each other, make collective decision at the group level, and decide to stay in or leave their respective groups, we analyse the effect of different forms of vertical communication that is meant to spread good practices within the organization. Our main indicators of the good functioning of the democratic dynamics are stability and representativeness. We show that if communication about norms is well designed, it has a positive impact on both stability and representativeness. Interestingly the effect of communication increases with the number of dimensions discussed in the groups. Communication about norms is thus a valuable tool to use in groups that wish to improve their democratic practices without jeopardizing stability.

The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, Gary Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Journal of Artificial Societies and Social Simulation 23 (2) 7

Kyeywords: Agent-Based Model, Individual-Based Model, Best Practice, Simulation Model, Standardization, Documentation
Abstract: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Emergence of Small-World Networks in an Overlapping-Generations Model of Social Dynamics, Trust and Economic Performance

Katarzyna Growiec, Jakub Growiec and Bogumił Kamiński
Journal of Artificial Societies and Social Simulation 23 (2) 8

Kyeywords: Social Network Structure, Social Network Dynamics, Trust, Willingness to Cooperate, Economic Performance, Agent-Based Model
Abstract: We study the impact of endogenous creation and destruction of social ties in an artificial society on aggregate outcomes such as generalized trust, willingness to cooperate, social utility and economic performance. To this end we put forward a computational multi-agent model where agents of overlapping generations interact in a dynamically evolving social network. In the model, four distinct dimensions of individuals’ social capital: degree, centrality, heterophilous and homophilous interactions, determine their generalized trust and willingness to cooperate, altogether helping them achieve certain levels of social utility (i.e., utility from social contacts) and economic performance. We find that the stationary state of the simulated social network exhibits realistic small-world topology. We also observe that societies whose social networks are relatively frequently reconfigured, display relatively higher generalized trust, willingness to cooperate, and economic performance – at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness.

A Software Architecture for Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models

Tuong Manh Vu, Charlotte Probst, Alexandra Nielsen, Hao Bai, Petra S. Meier, Charlotte Buckley, Mark Strong, Alan Brennan and Robin C. Purshouse
Journal of Artificial Societies and Social Simulation 23 (3) 1

Kyeywords: Agent-Based Modelling, Social Simulation, Software Architecture, Analytical Sociology, Abductive Reasoning
Abstract: This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

An Agent-Based Model for Simulating Inter-Settlement Trade in Past Societies

Angelos Chliaoutakis and Georgios Chalkiadakis
Journal of Artificial Societies and Social Simulation 23 (3) 10

Kyeywords: Agent-Based Modeling, Model-Based Archaeology, Spatial Interaction Model, GraphTheory, Trade Network, Minoan Civilization
Abstract: Social and computational archaeology focuses largely on the study of past societies and the evolution of human behaviour. At the same time, agent-based models (ABMs) allow the efficient modeling of human agency, and the quantitative representation and exploration of specific properties and patterns in archaeological information. In this work we put forward a novel agent-based trading model, for simulating the exchange and distribution of resources across settlements in past societies. The model is part of a broader ABM populated with autonomous, utility-seeking agents corresponding to households; with the ability to employ any spatial interaction model of choice. As such, it allows the study of the settlements’ trading ability and power, given their geo-location and their position within the trading network, and the structural properties of the network itself. As a case study we use the Minoan society during the Bronze Age, in the wider area of "Knossos" on the island of Crete, Greece. We instantiate two well-known spatial interaction sub-models, XTENT and Gravity, and conduct a systematic evaluation of the dynamic trading network that is formed over time. Our simulations assess the sustainability of the artificial Minoan society in terms of population size, number and distribution of agent communities, with respect to the available archaeological data and spatial interaction model employed; and, further, evaluate the resulting trading network’s structure (centrality, clustering, etc.) and how it affects inter-settlement organization, providing in the process insights and support for archaeological hypotheses on the settlement organization in place at the time. Our results show that when the trading network is modeled using Gravity, which focuses on the settlements' "importance" rather than proximity to each other, settlement numbers’ evolution patterns emerge that are similar to the ones that exist in the archaeological record. It can also be inferred by our simulations that a rather dense trading network, without a strict settlement hierarchy, could have emerged during the Late Minoan period, after the Theran volcanic eruption, a well documented historic catastrophic event. Moreover, it appears that the trading network's structure and interaction patterns are reversed after the Theran eruption, when compared to those in effect in earlier periods.

Price Formation in Parallel Trading Systems: Evidence from the Fine Wine Market

Marcin Czupryna, Michał Jakubczyk and Paweł Oleksy
Journal of Artificial Societies and Social Simulation 23 (3) 11

Kyeywords: Parallel Trading, Trading Systems, Price Formation, Wine Investment, Agent Based Modelling
Abstract: What drives the prices of fine wines is not easy to discern, in view of a multitude of confounding factors characterising the transactions across several markets. At the same time, understanding the quantitative relationships and mechanisms that determine the price level is important for policy making (e.g. predicting the outcomes of regulations) and methodological purposes (which elements to consider in modelling these markets). We examine the price formation of fine wines simultaneously across three markets: an automated electronic exchange (Liv-ex), intermediated auctions, and over-the-counter (OTC). We use a unique dataset consisting of 99,769 price data points for Premier Cru Bordeaux fine wines and we examine the price determinants with Bayesian modelling. We ascertain the mean price ranking (OTC market being the most expensive and Liv-ex the least, differing by about 4.5% and -0.8% from the auctions). We also find a slight price decrease for larger transactions (approx.~0.3% reduction for a 10% volume increase) and some platykurtosis in price distribution (greatest in Liv-ex), and observe the most stochastic noise in auctions. In an agent-based simulation, we discover that it is necessary to include trading mechanisms, commissions, and OTC market heterogeneity to reproduce the observed characteristics. Our results indicate which elements should be included in future fine wine markets models.

Impacts of Consensus Protocols and Trade Network Topologies on Blockchain System Performance

Xianhua Wei, Aiya Li and Zhou He
Journal of Artificial Societies and Social Simulation 23 (3) 2

Kyeywords: Blockchain, Consensus Protocol, Trade Network Topology, Agent-Based Model
Abstract: Blockchain can be viewed as a public ledger maintained collectively by a large number of participators based on consensus protocol. We are interested in how difference consensus protocols and trade network topologies affect the performance of a blockchain system, which has not been studied in the literature yet. In this paper, we proposed an agent-based model consisting of multiple trader and miner agents, and one system agent. We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. We find that both consensus protocol and trade network topology can impact the performance of blockchain system. PoS and DPoS are generally better than PoW in terms of increasing trade efficiency and equalizing wealth. Besides, scale-free trade network is not favorable because its trade efficiency is quite low, which moderates the price fluctuation and wealth inequality. Since connectivity inequality determines wealth inequality, it is crucial to increase the connectivity among participants when designing a sustainable blockchain system. We suggest that our findings could be useful to the designers, practitioner and researchers of blockchain system and token economy.

Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter

Nick Malleson, Kevin Minors, Le-Minh Kieu, Jonathan A. Ward, Andrew West and Alison Heppenstall
Journal of Artificial Societies and Social Simulation 23 (3) 3

Kyeywords: Agent-Based Modelling, Particle Filter, Data Assimilation, Crowd Simulation, Pedestrian Modelling
Abstract: Agent-based modelling is a valuable approach for modelling systems whose behaviour is driven by the interactions between distinct entities, such as crowds of people. However, it faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as those of pedestrian movements, to scenario testing on historic patterns rather than real-time simulation of the present. This paper demonstrates how a particle filter could be used to incorporate data into an agent-based model of pedestrian movements at run time. The experiments show that although it is possible to use a particle filter to perform online (real time) model optimisation, the number of individual particles required (and hence the computational complexity) increases exponentially with the number of agents. Furthermore, the paper assumes a one-to-one mapping between observations and individual agents, which would not be the case in reality. Therefore this paper lays some of the fundamental groundwork and highlights the key challenges that need to be addressed for the real-time simulation of crowd movements to become a reality. Such success could have implications for the management of complex environments both nationally and internationally such as transportation hubs, hospitals, shopping centres, etc.

A Weighted Balance Model of Opinion Hyperpolarization

Simon Schweighofer, Frank Schweitzer and David Garcia
Journal of Artificial Societies and Social Simulation 23 (3) 5

Kyeywords: Polarization, Balance Theory, Opinion Dynamics, Agent-Based Modeling
Abstract: Polarization is threatening the stability of democratic societies. Until now, polarization research has focused on opinion extremeness, overlooking the correlation between different policy issues. In this paper, we explain the emergence of hyperpolarization, i.e., the combination of extremeness and correlation between issues, by developing a new theory of opinion formation called "Weighted Balance Theory (WBT)". WBT extends Heider's cognitive balance theory to encompass multiple weighted attitudes. We validated WBT on empirical data from the 2016 National Election Survey. Furthermore, we developed an opinion dynamics model based on WBT, which, for the first time, is able to generate hyperpolarization and to explain the link between affective and opinion polarization. Finally, our theory encompasses other phenomena of opinion dynamics, including mono-polarization and backfire effects.

Comparing Actual and Simulated HFT Traders' Behavior for Agent Design

Masanori Hirano, Kiyoshi Izumi, Hiroyasu Matsushima and Hiroki Sakaji
Journal of Artificial Societies and Social Simulation 23 (3) 6

Kyeywords: Artificial Market, Multi-Agent Simulation, Data-Mining, High-Frequency Trade, Market-Making, Clustering
Abstract: Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-trader market-making (HFT-MM) strategy from both the real financial market and our simulation. Regarding the former, we extracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the difference between these traders' orders and the market price. In our simulation, we built an artificial market model with a continuous double auction system, stylized trader agents, and HFT-MM trader agents based on prior research. As an experiment, we compared the distribution of the order placements of HFT-MM traders in the real and simulated financial data. We found that the order placement distribution near the market or best price in both the real data and the simulations were similar. However, the orders far from the market or best price differed significantly when the real data exhibited a wider range of orders. This indicates that in order to build more realistic simulation of financial markets, integrating fine-grained data is essential.

An Agent-Based Approach to Integrated Assessment Modelling of Climate Change

Marcin Czupryna, Christian Franzke, Sascha Hokamp and Jürgen Scheffran
Journal of Artificial Societies and Social Simulation 23 (3) 7

Kyeywords: Climate Change, Climate Protection, Integrated Assessment Model, Agent-Based Modelling
Abstract: There is an ongoing discussion concerning the relationship between social welfare and climate change, and thus the required level and type of measures needed to protect the climate. Integrated assessment models (IAMs) have been extended to incorporate technological progress, heterogeneity and uncertainty, making use of a (stochastic) dynamic equilibrium approach in order to derive a solution. According to the literature, the IAM class of models does not take all the relationships among economic, social and environmental factors into account. Moreover, it does not consider these interdependencies at the micro-level, meaning that all possible consequences are not duly examined. Here, we propose an agent-based approach to analyse the relationship between economic welfare and climate protection. In particular, our aim is to analyse how the decisions of individual agents, allowing for the trade-off between economic welfare and climate protection, influence the aggregated emergent economic behaviour. Using this model, we estimate a damage function, with values in the order 3% - 4%for 2 C temperature increase and having a linear (or slightly concave) shape. We show that the heterogeneity of the agents, technological progress and the damage function may lead to lower GDP growth rates and greater temperature-related damage than what is forecast by models with solely homogeneous (representative) agents.

Grade Language Heterogeneity in Simulation Models of Peer Review

Thomas Feliciani, Ramanathan Moorthy, Pablo Lucas and Kalpana Shankar
Journal of Artificial Societies and Social Simulation 23 (3) 8

Kyeywords: Peer Review, Grade Language, Agent-Based Modeling
Abstract: Simulation models have proven to be valuable tools for studying peer review processes. However, the effects of some of these models’ assumptions have not been tested, nor have these models been examined in comparative contexts. In this paper, we address two of these assumptions which go in tandem: (1) on the granularity of the evaluation scale, and (2) on the homogeneity of the grade language (i.e. whether reviewers interpret evaluation grades in the same fashion). We test the consequences of these assumptions by extending a well-known agent-based model of author and reviewer behaviour with discrete evaluation scales and reviewers’ interpretation of the grade language. In this way, we compare a peer review model with a homogeneous grade language, as assumed in most models of peer review, with a more psychologically realistic model where reviewers interpret the grades of the evaluation scale heterogeneously. We find that grade language heterogeneity can indeed affect the predictions of a model of peer review.

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

Chaitanya Kaligotla, Jonathan Ozik, Nicholson Collier, Charles M. Macal, Kelly Boyd, Jennifer Makelarski, Elbert S. Huang and Stacy T. Lindau
Journal of Artificial Societies and Social Simulation 23 (4) 1

Kyeywords: Agent-Based Modeling, Model Exploration, High-Performance Computing, Active Learning
Abstract: This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

Halting SARS-CoV-2 by Targeting High-Contact Individuals

Gianluca Manzo and Arnout van de Rijt
Journal of Artificial Societies and Social Simulation 23 (4) 10

Kyeywords: Agent-Based Computational Models, Complex Social Networks, Virus Diffusion, Immunization Strategies, Epidemiological Models
Abstract: Network scientists have proposed that infectious diseases involving person-to-person transmission could be effectively halted by interventions targeting a minority of highly connected individuals. Could this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyzed population survey data showing that the distribution of close-range contacts across individuals is indeed characterized by a small proportion of individuals reporting very high frequency contacts. Strikingly, we found that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulated a population embedded in a network with empirically observed contact frequencies. Simulations showed that targeting hubs robustly improves containment.

BEN: An Architecture for the Behavior of Social Agents

Mathieu Bourgais, Patrick Taillandier and Laurent Vercouter
Journal of Artificial Societies and Social Simulation 23 (4) 12

Kyeywords: Social Simulation, Agent Architecture, BDI, Emotions, Personality, Emotional Contagion
Abstract: Over the last few years, the use of agent-based simulations to study social systems has spread to many domains (e.g., geography, ecology, sociology, economy). These simulations aim to reproduce real life situations involving human beings and thus need to integrate complex agents to match the behavior of the simulated people. Therefore, notions such as cognition, emotions, personality, social relationships or norms have to be taken into account, but there is currently no agent architecture that could incorporate all these features and be used by the majority of modelers, including those with low levels of skills in programming. In this paper, the BEN (Behavior with Emotions and Norms) architecture is introduced to tackle this issue. It is a modular architecture based on the BDI model of cognition and featuring modules to add emotions, emotional contagion, personality, social relationships and norms to agent behavior. This architecture is integrated into the GAMA simulation platform. An application of BEN to the simulation of the evacuation of a nightclub on fire is presented and shows the complexity of behaviors that may be developed with this architecture to create credible and expressive simulations.

RecovUS: An Agent-Based Model of Post-Disaster Household Recovery

Saeed Moradi and Ali Nejat
Journal of Artificial Societies and Social Simulation 23 (4) 13

Kyeywords: Disaster Recovery, Recovery Modeling, Agent-Based Modeling, Perceived Community
Abstract: The housing sector is an important part of every community. It directly affects people, constitutes a major share of the building market, and shapes the community. Meanwhile, the increase of developments in hazard-prone areas along with the intensification of extreme events has amplified the potential for disaster-induced losses. Consequently, housing recovery is of vital importance to the overall restoration of a community. In this relation, recovery models can help with devising data-driven policies that can better identify pre-disaster mitigation needs and post-disaster recovery priorities by predicting the possible outcomes of different plans. Although several recovery models have been proposed, there are still gaps in the understanding of how decisions made by individuals and different entities interact to output the recovery. Additionally, integrating spatial aspects of recovery is a missing key in many models. The current research proposes a spatial model for simulation and prediction of homeowners’ recovery decisions through incorporating recovery drivers that could capture interactions of individual, communal, and organizational decisions. RecovUS is a spatial agent-based model for which all the input data can be obtained from publicly available data sources. The model is presented using the data on the recovery of Staten Island, New York, after Hurricane Sandy in 2012. The results confirm that the combination of internal, interactive, and external drivers of recovery affect households’ decisions and shape the progress of recovery.

A Hybrid Agent-Based and Equation Based Model for the Spread of Infectious Diseases

Elizabeth Hunter, Brian Mac Namee and John Kelleher
Journal of Artificial Societies and Social Simulation 23 (4) 14

Kyeywords: Hybrid, Agent-Based, Equation Based, Infectious Disease, Simulation, Epidemiology
Abstract: Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at the town level and then the county level investigating different switch values and geographic levels of switching. We find that a hybrid model is able to save time compared to a fully agent-based model without losing a significant amount of fidelity.

Modeling Cultural Dissemination and Divergence Between Rural and Urban Regions

Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen and Adam Landsberg
Journal of Artificial Societies and Social Simulation 23 (4) 3

Kyeywords: Cultural Evolution, Cultural Transmission, Opinion Dynamics, Agent-Based Modeling, Cultural Dissemination
Abstract: The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as "cultural dissemination," is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.

WorkSim: An Agent-Based Model of Labor Markets

Jean-Daniel Kant, Gérard Ballot and Olivier Goudet
Journal of Artificial Societies and Social Simulation 23 (4) 4

Kyeywords: Agent-Based Simulation, Dual Labor Markets, Anticipations, Bounded Rationality, Policy Evaluation
Abstract: In this paper, we provide an overview of the WorkSim model, an agent-based framework designed to study labor markets. The first objective of this model was to reproduce, within rigorous stock-flow accounting, the gross flows of individuals between important work-states: i.e., employment (distinguishing fixed term contracts and open-ended contracts), unemployment and inactivity. French legal institutions of the labor market are modelled in some detail and constrain the decisions of the agents on job flows and worker flows. Firms and individuals are heterogeneous and all decisions are taken on the basis of bounded rationality, yet employers as well as workers form imperfect anticipations. One important theoretical novelty of the model is that we consider multi-job firms and shocks on the individual demand of the firms. Employers consider anticipated shocks when they decide on the types of contract. Once the model was calibrated, the secondary objective was to characterize the nature of the labor market under study, and notably the differentiated roles of the two types of contracts and their impact on unemployment. This is achieved, first by examining the patterns of flows and stocks of labor and secondly by sensitivity experiments, modifying certain exogenous parameters and variables such as total demand. We then used the model as a tool for experimenting labor market policies, including changes in the labor law in France.

Housing Market Agent-Based Simulation with Loan-To-Value and Debt-To-Income

Tae-Sub Yun and Il-Chul Moon
Journal of Artificial Societies and Social Simulation 23 (4) 5

Kyeywords: Housing Market, Macro-Prudential Policy, Loan-To-Value, Debt-To-Income, Agent-Based Modeling, Policy Impact Analysis
Abstract: This paper introduces an agent-based model of a housing market with macro-prudential policy experiments. Specifically, the simulation model is used to examine the effects of a policy setting on loan-to-value (LTV) and debt-to-income (DTI), which are policy instruments several governments use to regulate the housing market. The simulation model illustrates the interactions among the households, the house suppliers, and the real estate brokers. We model each household in the population as either seller or buyer, and some of households may behave as speculators in the housing market. To better understand the impact of the policies, we used the real-world observations from the Korean housing market, which include various economic conditions, policy variables, and Korean census data. Our baseline model is quantitatively validated to the price index and the transaction volume of the past Korean housing market. After validation, we show the empirical effectiveness of setting LTV and DTI towards house prices, transaction volumes, and the amount of households' mortgages. Furthermore, we investigate the simulation results for the owner-occupier rate of households. These investigations provide the policy analyses in Korea's housing market, and other governments with LTV and DTI regulations.

Agent-Based Modelling of Values: The Case of Value Sensitive Design for Refugee Logistics

Christine Boshuijzen-van Burken, Ross Gore, Frank Dignum, Lamber Royakkers, Phillip Wozny and F. LeRon Shults
Journal of Artificial Societies and Social Simulation 23 (4) 6

Kyeywords: Agent Based Model, Value Sensitive Design, Simulation and Policy, Humanitarian Logistics, Refugees, Schwartz Values
Abstract: We have used value sensitive design as a method to develop an agent-based model of values in humanitarian logistics for refugees. Schwartz's theory of universal values is implemented in the model in such a way that agents can make value trade-offs, which are operationalized into a measure of refugee wellbeing and a measure of public opinion about how the refugee logistics is being handled. By trying out different ‘value-scenarios’, stakeholders who are responsible for, or involved in refugee logistics can have insights into the effects of various value choices. The model is visualized and made usable as a platform (interactive website) for decision-makers to understand the trade-offs in policies for government and non-government organizations.

Leveraging Modularity During Replication of High-Fidelity Models: Lessons from Replicating an Agent-Based Model for HIV Prevention

Wouter Vermeer, Arthur Hjorth, Samuel M. Jenness, C Hendrick Brown and Uri Wilensky
Journal of Artificial Societies and Social Simulation 23 (4) 7

Kyeywords: Replication, Agent-Based Models, Modular, High-Fidelity, HIV
Abstract: High-fidelity models are increasingly used to predict, and guide decision making. Prior work has emphasized the importance of replication in ensuring reliable modeling, and has yielded important replication strategies. However, this work is based on relatively simple theory generating models, and its lessons might not translate to high-fidelity models used for decision support. Using NetLogo we replicate a recently published high-fidelity model examining the effects of a HIV biomedical intervention. We use a modular approach to build our model from the ground up, and provide examples of the replication process investigating the replication of two sub-modules as well as the overall simulation experiment. For the first module, we achieved numerical identity during replication, whereas we obtained distributional equivalence in replicating the second module. We achieved relational equivalence among the overall model behaviors, with a 0.98 correlation across the two implementations for our outcome measure even without strictly following the original model in the formation of the sexual network. Our results show that replication of high-fidelity models is feasible when following a set of systematic strategies that leverage the modularity, and highlight the role of replication standards, modular testing, and functional code in facilitating such strategies.

The Unknown of the Pandemic: An Agent-Based Model of Final Phase Risks

Marco Cremonini and Samira Maghool
Journal of Artificial Societies and Social Simulation 23 (4) 8

Kyeywords: Stochastic Epidemic Model, Multi-Agent Simulation, Network Analysis, Agent-Based Model, Risk Analysis
Abstract: Lifting social restrictions is one of the most critical decisions that public health authorities have to face during a pandemic such as COVID-19. This work focuses on the risk associated with such a decision. We have called the period from the re-opening decision to epidemic expiration the ’final epidemic phase’, and considered the critical epidemic conditions which could possibly emerge in this phase. The factors we have considered include: the proportion of asymptomatic cases, a mitigation strategy based on testing and the average duration of infectious states. By assuming hypothetical configurations at the time of the re-opening decision and the partial knowledge concerning epidemic dynamics available to public health authorities, we have analyzed the risk of the re-opening decision based on possibly unreliable estimates. We have presented a discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations. Our results show the different outcomes produced by different proportions of undetected asymptomatic cases, different probabilities of asymptomatic cases detected and contained, and a multivariate analysis of risk based on the average duration of asymptomatic and contained states. Finally, our analysis highlights that enduring uncertainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies.

Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia

Beatrice Nöldeke, Etti Winter and Ulrike Grote
Journal of Artificial Societies and Social Simulation 23 (4) 9

Kyeywords: Information Diffusion, Social Networks, Agent-Based Modelling, Seeding, Zambia
Abstract: The successful adoption of innovations depends on the provision of adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social diffusion processes to effectively disseminate information. This study aims to identify the set of farmers who initially obtain information (‘seeds’) that optimises diffusion through the network. It systematically evaluates different criteria for seed selection, number of seeds, and their interaction effects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict diffusion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest diffusion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting different parts of the network, enhances diffusion. An increased number of seeds improves reach, but the marginal effects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information diffusion.

Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees

Ali Termos, Stefano Picascia and Neil Yorke-Smith
Journal of Artificial Societies and Social Simulation 24 (1) 2

Kyeywords: Rent-Gap Theory, Migration, Agent-Based Modelling, Urban Dynamics, Housing, Lebanon
Abstract: Rapid international migration of significant populations generates profound implications for countries in West Asia, Europe, and other regions. The motivation of this work is to develop an agent-based model (ABM) to capture the existence of such migrant and refugee flows, and to explore the effects of these flows on urban dynamics. Advances in agent-based modelling have led to theoretically-grounded spatial agent models of urban dynamics, capturing the dynamics of population, property prices, and regeneration. In this article we leverage such an extant agent-based model founded on the rent-gap theory, as a lens to study the effect of sizeable refugee migration upon a capital city in West Asia. In order to calibrate and validate the simulation model we construct indices for housing prices and other factors. Results from the model, implemented in NetLogo, show the impact of migration shock on the housing market, and identify the relative efficacy of housing intervention policies. Our work progresses towards a tool for policy makers asking what-if questions about the urban environment in the context of migration.

On the Macroeconomic Effect of Extortion: An Agent-Based Approach

Alejandro Platas-López, Alejandro Guerra-Hernández and Francisco Grimaldo
Journal of Artificial Societies and Social Simulation 24 (1) 3

Kyeywords: Extortion, Macroeconomic Signals, BAM, Agent-Based Model
Abstract: This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the fact that there is little data on this criminal activity given its hidden nature. We develop a Bottom-up Adaptive Macroeconomics (BAM) model that simulates a healthy economy, including a moderate inflation and a reasonable unemployment rate, and test the impact of extortion on various macroeconomic signals. The BAM model defines the usual interactions among workers, firms and banks in labour, goods and credit markets. Subsequently, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well as the efficiency of the police in terms of their probability of capturing these extortionists. The definition of BAM under Extortion Racket Systems (BAMERS) model is completed with a threshold for the firms rejecting extortion. These parameters are explored extensively and independently. Results show that even low propensity towards extortion is enough to find considerable negative effects such as a marked contraction of Gross Domestic Product and increased unemployment, consistent with the little known data of the macroeconomic effects of extortion. The effects on consumption, Gini index, inflation and wealth distribution are also reported. Interestingly, our results suggest that it is more convenient to prevent extortion, rather than combat it once deployed, i.e., no police efficiency level achieves the healthy macroeconomic signals observed without extortion.

Opinion Dynamics and Collective Risk Perception: An Agent-Based Model of Institutional and Media Communication About Disasters

Francesca Giardini and Daniele Vilone
Journal of Artificial Societies and Social Simulation 24 (1) 4

Kyeywords: Risk Perceptions, Opinion Dynamics, Social Influence, Agent-Based Model
Abstract: The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals' attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seemed to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.

Finding Core Members of Cooperative Games Using Agent-Based Modeling

Daniele Vernon-Bido and Andrew Collins
Journal of Artificial Societies and Social Simulation 24 (1) 6

Kyeywords: Agent-Based Modeling, Cooperative Game Theory, Modeling and Simulation, ABM, Cooperative Games
Abstract: Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modelled using cooperative game theory. In this paper, a heuristic algorithm, which can be embedded into an ABM to allow the agents to find a coalition, is described. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solutions (if they exist). The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison is achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of market economy game. Finding the traditional cooperative game solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers up to nine-player games. The results indicate that our heuristic approach achieves a core solution over 90% of the games considered in our experiment.

Justified Stories with Agent-Based Modelling for Local COVID-19 Planning

Jennifer Badham, Pete Barbrook-Johnson, Camila Caiado and Brian Castellani
Journal of Artificial Societies and Social Simulation 24 (1) 8

Kyeywords: Agent-Based Modelling, Epidemic, COVID-19, Descriptive Model, Social Distancing, Justified Stories
Abstract: This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis with limited access to data. The proximate purpose of JuSt-Social is description, as the model represents knowledge about both COVID-19 transmission and intervention effects. Its ultimate purpose is to generate stories that respond to the questions and concerns of local planners and policy makers and are justified by the quality of the representation. These justified stories organise the knowledge in way that is accessible, timely and useful at the local level, assisting the decision makers to better understand both their current situation and the plausible outcomes of policy alternatives. JuSt-Social and the concept of justified stories apply to the modelling of infectious disease in general and, even more broadly, modelling in public health, particularly for policy interventions in complex systems.

Can Ethnic Tolerance Curb Self-Reinforcing School Segregation? A Theoretical Agent Based Model

Lucas Sage and Andreas Flache
Journal of Artificial Societies and Social Simulation 24 (2) 2

Kyeywords: Agent-Based Model, Social Simulation, Segregation, School-Segregation, School-Choice, Discrete-Choice-Model
Abstract: Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Third, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers segregated to mixed schools. However, we also identify a “sweet spot” in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when parents have moderate preferences for nearby schools and there is only little residential segregation. Further experimentation unraveled the underlying mechanisms.

Introducing the Argumentation Framework Within Agent-Based Models to Better Simulate Agents' Cognition in Opinion Dynamics: Application to Vegetarian Diet Diffusion

Patrick Taillandier, Nicolas Salliou and Rallou Thomopoulos
Journal of Artificial Societies and Social Simulation 24 (2) 6

Kyeywords: Opinion Dynamics, Agent-Based Simulation, Argumentation Framework, Vegetarian Diets
Abstract: This paper introduces a generic agent-based model simulating the exchange and the diffusion of pro and con arguments. It is applied to the case of the diffusion of vegetarian diets in the context of a potential emergence of a second nutrition transition. To this day, agent-based simulation has been extensively used to study opinion dynamics. However, the vast majority of existing models have been limited to extremely abstract and simplified representations of the diffusion process. These simplifications impairs the realism of the simulations and disables the understanding of the reasons for the shift of an actor's opinion. The generic model presented here explicitly represents exchanges of arguments between actors in the context of an opinion dynamic model. In particular, the inner attitude towards an opinion of each agent is formalized as an argumentation graph and each agent can share arguments with other agents. Simulation experiments show that introducing attacks between arguments and a limitation of the number of arguments mobilized by agents has a strong impact on the evolution of the agents' opinion. We also highlight that when a new argument is introduced into the system, the quantity and the profile of the agents receiving the new argument will impact the evolution of the overall opinion. Finally, the application of this model to vegetarian diet adoption seems consistent with historical food behaviour dynamics observed during crises.

Dynamics of Public Opinion: Diverse Media and Audiences’ Choices

Zhongtian Chen and Hanlin Lan
Journal of Artificial Societies and Social Simulation 24 (2) 8

Kyeywords: Opinion Dynamics, Social Media, Polarization, Agent-Based Modeling, Opinion Guidance
Abstract: Studies on the fundamental role of diverse media in the evolution of public opinion can protect us from the spreading of brainwashing, extremism, and terrorism. Many fear the information cocoon may result in polarization of the public opinion. Hence, in this work, we investigate how audiences' choices among diverse media might influence public opinion. Specifically, we aim to figure out how peoples' horizons (i.e., range of available media) and quantity, as well as the distribution of media, may shape the space of public opinion. We propose a novel model of opinion dynamics that considers different influences and horizons for every individual, and we carry out simulations using a real-world social network. Numerical simulations show that diversity in media can provide more choices to the people, although individuals only choose media within the bounds of their horizons, extreme opinions are more diluted, and no opinion polarizations emerge. Furthermore, we find that the distribution of media's opinions can effectively influence the space for public opinion, but when the number of media grows to a certain level, its effect will reach a limitation. Finally, we show that the effect of campaigns for consciousness or education can be improved by constructing the opinion of media, which can provide a basis for the policy maker in the new media age.

Modeling COVID-19 for Lifting Non-Pharmaceutical Interventions

Matthew Koehler, David M Slater, Garry Jacyna and James R Thompson
Journal of Artificial Societies and Social Simulation 24 (2) 9

Kyeywords: Agent-Based Modeling, Covid-19, Contact Networks, Non-Pharmaceutical Interventions
Abstract: As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19. We apply the model to the 24 county-equivalents of Maryland and illustrate that different NPI strategies can be employed in different parts of the state. Our objective is to outline a modeling process that combines the critical disease factors and factors relevant to decision-makers who must balance the health of the population with the health of the economy.

Cascades Across Networks Are Sufficient for the Formation of Echo Chambers: An Agent-Based Model

Jan-Philipp Fränken and Toby Pilditch
Journal of Artificial Societies and Social Simulation 24 (3) 1

Kyeywords: Echo Chambers, Source Credibility, Information Cascades, Agent-Based Modelling, Bayesian Modelling, Single Interaction
Abstract: Investigating how echo chambers emerge in social networks is increasingly crucial, given their role in facilitating the retention of misinformation, inducing intolerance towards opposing views, and misleading public and political discourse. Previously, the emergence of echo chambers has been attributed to psychological biases and inter-individual differences, requiring repeated interactions among network-users and rewiring or pruning of social ties. Using an idealised population of social network users, the present results suggest that when combined with positive credibility perceptions of a communicating source, social media users’ ability to rapidly share information with each other through a single cascade can be sufficient to produce echo chambers. Crucially, we show that this requires neither special psychological explanation (e.g., bias or individual differences), nor repeated interactions—though these may be exacerbating factors. In fact, this effect is made increasingly worse the more generations of peer-to-peer transmissions it takes for information to permeate a network. This raises important questions for social network architects, if truly opposed to the increasing prevalence of deleterious societal trends that stem from echo chamber formation.

Using Agent-Based Models for Prediction in Complex and Wicked Systems

Gary Polhill, Matthew Hare, Tom Bauermann, David Anzola, Erika Palmer, Doug Salt and Patrycja Antosz
Journal of Artificial Societies and Social Simulation 24 (3) 2

Kyeywords: Prediction, Complex Systems, Wicked Systems, Agent-Based Modelling, Cellular Automata, Turing Machines
Abstract: This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.

Sustaining Collective Action in Urban Community Gardens

Arthur Feinberg, Elena Hooijschuur, Nicole Rogge, Amineh Ghorbani and Paulien Herder
Journal of Artificial Societies and Social Simulation 24 (3) 3

Kyeywords: Community Gardens, Agent-Based Model, Institutional Modelling, Theory of Reasoned Action, Design Principles for Collective Action
Abstract: This paper presents an agent-based model that explores the conditions for ongoing participation in community gardening projects. We tested the effects of Ostrom's well-known Design Principles for collective action and used an extensive database collected in 123 cases in Germany and two case studies in the Netherlands to validate it. The model used the Institutional Analysis and Development (IAD) framework and integrated decision mechanisms derived from the Theory of Reasoned Action (TRA). This allowed us to analyse volunteer participation in urban community gardens over time, based on the garden's institutions (Design Principles) and the volunteer's intention to join gardening. This intention was influenced by the volunteer's expectations and past experiences in the garden (TRA). We found that not all Design Principles lead to higher levels of participation but rather, participation depends on specific combinations of the Design Principles. We highlight the need to update the assumption about sanctioning in such systems: sanctioning is not always beneficial, and may be counter-productive in certain contexts.

Social Network Metric-Based Interventions? Experiments with an Agent-Based Model of the COVID-19 Pandemic in a Metropolitan Region

Ben Vermeulen, Matthias Müller and Andreas Pyka
Journal of Artificial Societies and Social Simulation 24 (3) 6

Kyeywords: Epidemic, Agent-Based Model, Policy Laboratory, COVID-19, Coronavirus
Abstract: We present and use an agent-based model to study interventions for suppression, mitigation, and vaccination in coping with the COVID-19 pandemic. Unlike metapopulation models, our agent-based model permits experimenting with micro-level interventions in social interactions at individual sites. We compare common macro-level interventions applicable to everyone (e.g., keep distance, close all schools) to targeted interventions in the social network spanned by households based on specific (potential) transmission rates (e.g., prohibit visiting spreading hubs or bridging ties). We show that, in the simulation environment, micro-level measures of 'locking' of a number of households and ‘blocking’ access to a number of sites (e.g., workplaces, schools, recreation areas) using social network centrality metrics permits refined control on the positioning on the immunity-mortality curve. In simulation results, social network metric-based vaccination of households offers refined control and reduces the spread saliently better than random vaccination.

Understanding the Effects of China’s Agro-Environmental Policies on Rural Households’ Labor and Land Allocation with a Spatially Explicit Agent-Based Model

Ying Wang, Qi Zhang, Srikanta Sannigrahi, Qirui Li, Shiqi Tao, Richard Bilsborrow, Jiangfeng Li and Conghe Song
Journal of Artificial Societies and Social Simulation 24 (3) 7

Kyeywords: Spatially Explicit Agent-Based Model, Social-Ecological Systems, Land Use, Labor Allocation, Agro-Environmental Policies
Abstract: Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we developed a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households’ land and labor allocation decisions and investigated the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs revealed that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns showed that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

Actor Behaviour and Robustness of Industrial Symbiosis Networks: An Agent-Based Modelling Approach

Kasper Lange, Gijsbert Korevaar, Igor Nikolic and Paulien Herder
Journal of Artificial Societies and Social Simulation 24 (3) 8

Kyeywords: Circular Economy, Industrial Symbiosis, Cooperative Networks, Agent-Based Modelling, Theory of Planned Behaviour, Eco-Oriented Behaviour
Abstract: Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.

Comparing Mechanisms of Food Choice in an Agent-Based Model of Milk Consumption and Substitution in the UK

Matthew Gibson, Raphael Slade, Joana Portugal Pereira and Joeri Rogelj
Journal of Artificial Societies and Social Simulation 24 (3) 9

Kyeywords: Food Choice, Milk Consumption, Consumer Behaviour, Agent-Based Modelling, Calibration Optimisation, Global Temporal Sensitivity Analysis
Abstract: Substitution of food products will be key to realising widespread adoption of sustainable diets. We present an agent-based model of decision-making and influences on food choice, and apply it to historically observed trends of British whole and skimmed (including semi) milk consumption from 1974 to 2005. We aim to give a plausible representation of milk choice substitution, and test different mechanisms of choice consideration. Agents are consumers that perceive information regarding the two milk choices, and hold values that inform their position on the health and environmental impact of those choices. Habit, social influence and post-decision evaluation are modelled. Representative survey data on human values and long-running public concerns empirically inform the model. An experiment was run to compare two model variants by how they perform in reproducing these trends. This was measured by recording mean weekly milk consumption per person. The variants differed in how agents became disposed to consider alternative milk choices. One followed a threshold approach, the other was probability based. All other model aspects remained unchanged. An optimisation exercise via an evolutionary algorithm was used to calibrate the model variants independently to observed data. Following calibration, uncertainty and global variance-based temporal sensitivity analysis were conducted. Both model variants were able to reproduce the general pattern of historical milk consumption, however, the probability-based approach gave a closer fit to the observed data, but over a wider range of uncertainty. This responds to, and further highlights, the need for research that looks at, and compares, different models of human decision-making in agent-based and simulation models. This study is the first to present an agent-based modelling of food choice substitution in the context of British milk consumption. It can serve as a valuable pre-curser to the modelling of dietary shift and sustainable product substitution to plant-based alternatives in Britain.

VIDA: A Simulation Model of Domestic Violence in Times of Social Distancing

Lígia Mori Madeira, Bernardo Alves Furtado and Alan Dill
Journal of Artificial Societies and Social Simulation 24 (4) 1

Kyeywords: Domestic Violence, Violence Against Women, Agent-Based Models, Pandemics, Simulation, Metropolitan Regions
Abstract: Violence against women occurs predominantly in the family and domestic context. The COVID-19 pandemic has led Brazil to recommend and at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense coexistence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to formalize and illustrate a multitude of factors that influence events which could trigger violence. A central part of the model is the construction of a stress indicator, created as a probability trigger of domestic violence occurring within the family environment. Having a formal model that replicates observed patterns of violence based on internal familial characteristics enables us to experiment with altering dynamics. We first tested the (a) absence or presence of the deterrence system of domestic violence against women and then (b) the existence of measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neighborhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA suggests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formalizing, to the best of our knowledge, the first model of domestic violence through agent-based modeling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban (census sectors) and household level.

Hard Work, Risk-Taking, and Diversity in a Model of Collective Problem Solving

Amin Boroomand and Paul E. Smaldino
Journal of Artificial Societies and Social Simulation 24 (4) 10

Kyeywords: Teams, NK Landscape, Risk, Collective Decision Making, Agent-Based Model
Abstract: We studied an agent-based model of collective problem solving in which teams of agents search on an NK landscape and share information about newly found solutions. We analyzed the effects of team members’ behavioral strategies, team size, and team diversity on overall performance. Depending on the landscape complexity and a team’s features a team may eventually find the best possible solution or become trapped at a local maximum. Hard-working agents can explore more solutions per unit time, while risk-taking agents inject randomness in the solutions they test. We found that when teams solve complex problems, both strategies (risk-taking and hard work) have positive impacts on the final score, and the positive effect of moderate risk-taking is substantial. However, risk-taking has a negative effect on how quickly a team achieves its final score. If time restrictions can be relaxed, a moderate level of risk can produce an improved score. If the highest priority is instead to achieve the best possible score in the shortest amount of time, the hard work strategy has the greatest impact. When problems are simpler, risk-taking behavior has a negative effect on performance, while hard work decreases the time required to solve the problem. We also find that larger teams generally solved problems more effectively, and that some of this positive effect is due to the increase in diversity. We show more generally that increasing the diversity of teams has a positive impact on the team’s final score, while more diverse teams also require less time to reach their final solution. This work contributes overall to the larger literature on collective problem solving in teams.

PastoralScape: An Environment-Driven Model of Vaccination Decision Making Within Pastoralist Groups in East Africa

Matthew Sottile, Richard Iles, Craig McConnel, Ofer Amram and Eric Lofgren
Journal of Artificial Societies and Social Simulation 24 (4) 11

Kyeywords: Agent-Based Model, Random Field Ising Model, Livestock Health, Rift Valley Fever, Contagious Bovine Pleuropneumonia, Economic Decision Making
Abstract: Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

Youth and Their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-Based Model of Interactional Theory of Delinquency

JoAnn Lee and Andrew Crooks
Journal of Artificial Societies and Social Simulation 24 (4) 2

Kyeywords: Agent-Based Modeling, Antisocial Behaviors, Delinquency, Risk Factors, Youth, Social Work
Abstract: Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk . This paper presents an agent-based model (ABM) which explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors , and their environments have likelihoods of presenting prosocial or antisocial opportunities. Results from systematically adjusting family, school, and neighborhood risk and promotive levels suggest that environmental risk and promotive factors play a role in shaping youth outcomes. As such the model shows promise for increasing our understanding of delinquency.

Cultural Dissemination: An Agent-Based Model with Social Influence

Ngan Nguyen, Hongfei Chen, Benjamin Jin, Walker Quinn, Conrad Tyler and Adam Landsberg
Journal of Artificial Societies and Social Simulation 24 (4) 5

Kyeywords: Cultural Dissemination, Agent-Based Modeling, Cultural Evolution, Opinion Dynamics, Cultural Transmission, Bounded Confidence Models
Abstract: We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod's original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.

Modeling Interaction in Collaborative Groups: Affect Control Within Social Structure

Nikolas Zöller, Jonathan H. Morgan and Tobias Schröder
Journal of Artificial Societies and Social Simulation 24 (4) 6

Kyeywords: Agent Based Modeling, Affect Control Theory, Expectation States Theory, Networks, Online Collaboration, Group Dynamics
Abstract: This paper studies the dynamics of identity and status management within groups in collaborative settings. We present an agent-based simulation model for group interaction rooted in social psychological theory. The model integrates affect control theory with networked interaction structures and sequential behavior protocols as they are often encountered in task groups. By expressing status hierarchy through network structure, we build a bridge between expectation states theory and affect control theory, and are able to reproduce central results from the expectation states research program in sociological social psychology. Furthermore, we demonstrate how the model can be applied to analyze specialized task groups or sub-cultural domains by combining it with empirical data sources. As an example, we simulate groups of open-source software developers and analyze how cultural expectations influence the occupancy of high status positions in these groups.

Long-Term Dynamics of Institutions: Using ABM as a Complementary Tool to Support Theory Development in Historical Studies

Molood Ale Ebrahim Dehkordi, Amineh Ghorbani, Giangiacomo Bravo, Mike Farjam, René van Weeren, Anders Forsman and Tine De Moor
Journal of Artificial Societies and Social Simulation 24 (4) 7

Kyeywords: Institutional Modelling, Historical Data, CPRs, Institutional Evolution
Abstract: Historical data are valuable resources for providing insights into social patterns in the past. However, these data often inform us at the macro-level of analysis but not about the role of individuals’ behaviours in the emergence of long-term patterns. Therefore, it is difficult to infer ‘how’ and ‘why’ certain patterns emerged in the past. Historians use various methods to draw hypotheses about the underlying reasons for emerging patterns and trends, but since the patterns are the results of hundreds if not thousands of years of human behaviour, these hypotheses can never be tested in reality. Our proposition is that simulation models and specifically, agent-based models (ABMs) can be used as complementary tools in historical studies to support hypothesis building. The approach that we propose and test in this paper is to design and configure models in such a way as to generate historical patterns, consequently aiming to find individual-level explanations for the emerging pattern. In this work, we use an existing, empirically validated, agent-based model of common pool resource management to test hypotheses formulated based on a historical dataset. We first investigate whether the model can replicate various patterns observed in the dataset, and second, whether it can contribute to a better understanding of the underlying mechanism that led to the observed empirical trends. We showcase how ABM can be used as a complementary tool to support theory development in historical studies. Finally, we provide some guidelines for using ABM as a tool to test historical hypotheses.

Using Agent-Based Modelling to Assess Scenarios for Enhanced Soil and Water Conservation in the Boset District, Ethiopia

Samuel Assefa, Aad Kessler and Luuk Fleskens
Journal of Artificial Societies and Social Simulation 24 (4) 8

Kyeywords: Social Simulation, Farmers, Soil and Water Conservation, Scenario Analysis, Ethiopia
Abstract: The sustainability of the ongoing Campaign-Based Watershed Management (CBWM) program in Ethiopia is questionable due to poor planning and implementation of the Soil and Water Conservation (SWC) structures. This study uses an empirically based, agent-based model to explore the effect of six scenarios on both area of land covered by, as well as the quality of SWC structures in three Kebeles (villages) of Boset District. The analysis revealed that integrating multiple interventions enhanced SWC most in all Kebeles. Furthermore, increasing the commitment of local government through capacity building generated most effect and yet required the lowest investment. Motivating farmers, introducing alternative livelihood opportunities and establishing and strengthening micro-watershed associations had limited, but differential influence on the outcomes across the Kebeles. However, all alternative scenarios had some added value compared to doing business as usual. Hence, in order to enhance the outcomes and sustainability of the ongoing CBWM program in the study area and other similar localities, it is crucial to pay much more attention to increasing the commitment of local government actors through capacity building. This empowers local government actors to (1) plan and more efficiently implement the program in consultation with other local actors, and (2) integrate locally sensitive need-based adaptation of the program.

The Dynamical Relation Between Individual Needs and Group Performance: A Simulation of the Self-Organising Task Allocation Process

Shaoni Wang, Kees Zoethout, Wander Jager and Yanzhong Dang
Journal of Artificial Societies and Social Simulation 24 (4) 9

Kyeywords: Individual Needs, Motivation, Group Performance, Self-Organisation, Task Allocation, Agent-Based Modelling
Abstract: Team performance can be considered a macro-level outcome that depends on three sets of micro-level factors: individual workers contributing to the task, team composition, and task characteristics. For a number of reasons, the complex dynamics between individuals in the task allocation process are difficult to systematically explore in traditional experimental settings: the motivational dynamics, the complex dynamics of task allocation processes, and the lack of experimental control over team composition imply an ABM-approach being more feasible. For this reason, we propose an updated version of the WORKMATE model that has been developed to explore the dynamics of team performance. In doing so, we added Deci and Ryan’s SDT theory, stating that people are motivated by three psychological needs, competence, autonomy, and belongingness. This paper is aimed at explaining the architecture of the model, and some first simulation runs as proof of concept. The experimental results show that: 1) an appropriate motivation threshold will help the team have the lowest performance time; 2) the time needed for the task allocation process is related to the importance of different motivations; 3) highly satisfied teams are more likely composed of members valuing autonomy.

A Comparative Study on Apprenticeship Systems Using Agent-Based Simulation

Amir Hosein Afshar Sedigh, Martin Purvis, Tony Bastin Roy Savarimuthu, Christopher Konstantin Frantz and Maryam Purvis
Journal of Artificial Societies and Social Simulation 25 (1) 1

Kyeywords: Apprenticeship, Agent-Based Modelling, Social Simulations, Comparative Systems, Institutions, Historical Systems
Abstract: In this paper, we investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. Apprenticeship is one of the major means to transfer skills in a society. We consider five societies: the Old Britain system (AD 1300s−1600s), the British East India Company (AD 1600s − 1800s), Armenian merchants of New-Julfa (AD 1600s − 1700s), contemporary German apprenticeship (1990s), and the “Modern Apprenticeship” in Britain (2001). In comparing these systems, using an agent-based simulation model, we identified six characteristics which impact the success of an apprenticeship programme in a society, which we measured by considering three parameters, namely the number of skilled agents produced by the apprenticeships, programme completion, and the contribution of programmes to the Gross Domestic Income (GDI) of the society. We investigate different definitions for success of an apprenticeship and some hypothetical societies to test some common beliefs about apprenticeships' performance. The simulations suggest that a) it is better to invest in a public educational system rather than subsidising private contractors to train apprentices, b) having a higher completion ratio for apprenticeship programme does not necessarily result in a higher contribution in the GDI, and c) governors (e.g. mayors or government) that face significant emigration should also consider employing policies that persuade apprentices to complete their programme and stay in the society after completion to improve apprenticeship efficacy.

An Integrated Ecological-Social Simulation Model of Farmer Decisions and Cropping System Performance in the Rolling Pampas (Argentina)

Sebastián Pessah, Diego Omar Ferraro, Daniela Blanco and Rodrigo Castro
Journal of Artificial Societies and Social Simulation 25 (1) 5

Kyeywords: Land Use Change, Agent-Based Models, Cropping Systems, Emergy, Cell-DEVS
Abstract: Changes in agricultural systems are a multi-causal process involving climate change, globalization and technological change. These complex interactions regulate the landscape transformation process by imposing land use and cover change (LUCC) dynamics. In order to better understand and forecast the LUCC process we developed a spatially explicit agent-based model in the form of a Cellular Automata: the AgroDEVS model. The model was designed to project viable LUCC dynamics along with their associated economic and environmental changes. AgroDEVS is structured with behavioral rules and functions representing a) crop yields, b) weather conditions, c) economic profits, d) farmer preferences, e) adoption of technology levels and f) natural resource consumption based on embodied energy accounting. Using data from a typical location of the Pampa region (Argentina) for the period 1988-2015, simulation exercises showed that economic goals were achieved, on average, each 6 out of 10 years, but environmental thresholds were only achieved in 1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns converge quickly towards the most profitable crop sequences, with no noticeable trade-off between economic and environmental conditions.

A Bad Barrel Spoils a Good Apple: How Uncertainty and Networks Affect Whether Matching Rules Can Foster Cooperation

Carlos A. de Matos Fernandes, Andreas Flache, Dieko M. Bakker and Jacob Dijkstra
Journal of Artificial Societies and Social Simulation 25 (1) 6

Kyeywords: Cooperation, Meritocratic Matching, Information, Homophily, Threshold Model, Learning
Abstract: Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less effective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks differentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other’s behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, non-cooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters.

The Role of Reinforcement Learning in the Emergence of Conventions: Simulation Experiments with the Repeated Volunteer’s Dilemma

Hendrik Nunner, Wojtek Przepiorka and Chris Janssen
Journal of Artificial Societies and Social Simulation 25 (1) 7

Kyeywords: Conventions, Repeated Games, Volunteer’s Dilemma, Agent-Based Simulation, Reinforcement Learning, Cognitive Modeling
Abstract: We use reinforcement learning models to investigate the role of cognitive mechanisms in the emergence of conventions in the repeated volunteer’s dilemma (VOD). The VOD is a multi-person, binary choice collective goods game in which the contribution of only one individual is necessary and sufficient to produce a benefit for the entire group. Behavioral experiments show that in the symmetric VOD, where all group members have the same costs of volunteering, a turn-taking convention emerges, whereas in the asymmetric VOD, where one “strong” group member has lower costs of volunteering, a solitary-volunteering convention emerges with the strong member volunteering most of the time. We compare three different classes of reinforcement learning models in their ability to replicate these empirical findings. Our results confirm that reinforcement learning models can provide a parsimonious account of how humans tacitly agree on one course of action when encountering each other repeatedly in the same interaction situation. We find that considering contextual clues (i.e., reward structures) for strategy design (i.e., sequences of actions) and strategy selection (i.e., favoring equal distribution of costs) facilitate coordination when optima are less salient. Furthermore, our models produce better fits with the empirical data when agents act myopically (favoring current over expected future rewards) and the rewards for adhering to conventions are not delayed.

PolicySpace2: Modeling Markets and Endogenous Public Policies

Bernardo Alves Furtado
Journal of Artificial Societies and Social Simulation 25 (1) 8

Kyeywords: Public Policies, Real Estate Market, Agent-Based Modeling, Simulation, Spatial Analysis, Metropolitan Regions
Abstract: Policymakers' role in decision making on alternative policies is facing restricted budgets and an uncertain future. The need to decide on priorities and handle effects across policies has made their task even more difficult. For instance, housing policies involve heterogeneous characteristics of the properties themselves and the intricacy of housing markets within the spatial context of cities. Here, we have proposed PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks by integrating economic, spatial and transport research. PS2 is applied here as a comparison of three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers and (c) monetary aid. Within the model context, monetary aid, that is smaller amounts of help for a larger number of households, improves the economy in terms of production, consumption, reduction of inequality and maintenance of financial duties. PS2 is also a framework that can be further adapted to a number of related research questions.

Calibrating Agent-Based Models Using Uncertainty Quantification Methods

Josie McCulloch, Jiaqi Ge, Jonathan A. Ward, Alison Heppenstall, Gary Polhill and Nick Malleson
Journal of Artificial Societies and Social Simulation 25 (2) 1

Kyeywords: Calibration, Optimisation, History Matching, Proximate Bayesian Computation, Uncertainty, Agent-Based Modelling
Abstract: Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such as NetLogo make the creation of ABMs relatively easy, a number of key methodological issues, including the quantification of uncertainty, remain. In this paper we draw on state-of-the-art approaches from the fields of uncertainty quantification and model optimisation to describe a novel framework for the calibration of ABMs using History Matching and Approximate Bayesian Computation. The utility of the framework is demonstrated on three example models of increasing complexity: (i) Sugarscape to illustrate the approach on a toy example; (ii) a model of the movement of birds to explore the efficacy of our framework and compare it to alternative calibration approaches and; (iii) the RISC model of farmer decision making to demonstrate its value in a real application. The results highlight the efficiency and accuracy with which this approach can be used to calibrate ABMs. This method can readily be applied to local or national-scale ABMs, such as those linked to the creation or tailoring of key policy decisions.

An Agent-Based Model of Motor Insurance Customer Behaviour in the UK with Word of Mouth

Rei England, Iqbal Owadally and Douglas Wright
Journal of Artificial Societies and Social Simulation 25 (2) 2

Kyeywords: Insurance, Word-Of-Mouth, Agent-Based-Model, Networks, Customer Service, Renewal Premium
Abstract: Attracting and retaining loyal customers is a key driver of insurance profit. An important factor is the customers' opinion of an insurer's service quality. If a customer has a bad experience with an insurer, they will be less likely to buy from them again. Word-of-mouth networks allow information to spread between customers. In this paper we build an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network. We find that the existence of the network acts as a persistent memory, causing a systemic bias whereby an insurer's early reputation achieved by random chance tends to persist and leads to unequal market shares. This occurs even when the transmission of information is very low. This suggests that newer insurers might benefit more from a higher service quality as they build their reputation. Insurers with a higher service quality earn more profit, even when the customer preference for better service quality is small. The UK regulator is intending to ban the practice of charging new customers less than renewing customers. When the model is run with this scenario, the retention rates increase substantially and there is less movement away from insurers with a good initial reputation. This increases the skewness in market concentrations, but there is a greater incentive for good service quality.

Agent-Based Modelling of Future Dairy and Plant-Based Milk Consumption for UK Climate Targets

Matthew Gibson, Joana Portugal Pereira, Raphael Slade and Joeri Rogelj
Journal of Artificial Societies and Social Simulation 25 (2) 3

Kyeywords: Plant-Based Milk, Dairy Reduction, Sustainable Diets, Agent-Based Modelling, Calibration, Scenario Analysis
Abstract: A reduction in the production and consumption of meat and dairy across much of the world is critical for climate change mitigation, the alleviation of ecological stress, and improved health. We update an agent-based model (ABM) of historic UK milk consumption and apply it to scenarios of dairy reduction and adoption of plant-based milk (PBM) out to 2050. The updated model is comprised of a cognitive function, where agents perceive the physical, health and environmental characteristics of milk choice, which is modified by habit and social influence. We use European Social Survey 2018 and British Social Attitudes 2008 survey data to empirically inform the model. Taking a backcasting approach, we calibrate parameters against published UK dairy reduction targets (2030 and 2050), and test how different price relationships, and characterisations of environmental concern, may affect simulated milk consumption from 2020 to 2050. Scenarios for core targets (20% less dairy by 2030 and 35% by 2050) largely produced plausible consumption trajectories. However, at current pricing of dairy and PBM, simulated consumption was mostly unable to deliver on desired core targets, but this improved markedly with dairy prices set to organic levels. The influence of changing environmental concern on milk choice resulted in higher levels of dairy milk reduction. When modelled as transient, intense shocks to public concern, consumption patterns did not fundamentally change. However, small, incremental but permanent changes to concern did produce structural changes to consumption patterns, with dairy falling below plant-based alternatives at around 2030. This study is the first to apply an ABM in the context of scenarios for dairy reduction and PBM adoption in service to UK climate-related consumption targets. It can serve as valuable bottom-up, alternative, evidence on the feasibility of dietary shift targets, and poses policy implications for how to address impediments to behavioural change.

ReMoTe-S. Residential Mobility of Tenants in Switzerland: An Agent-Based Model

Anna Pagani, Francesco Ballestrazzi, Emanuele Massaro and Claudia R. Binder
Journal of Artificial Societies and Social Simulation 25 (2) 4

Kyeywords: Household Mobility, Household Relocation, Housing, Human-Environment Systems, Sustainability, Agent-Based Modelling
Abstract: Sustainable housing is a key priority for Switzerland. To provide both environmentally and socio-culturally sustainable housing, Swiss property owners need to navigate the complex and context-specific system that articulates the match between households’ preferences and the dwellings available to them-i.e. residential mobility. In response to this need, this paper outlines ReMoTe-S, an agent-based model of tenants’ residential mobility in Switzerland. The model design is based on empirical research conducted with the tenants of three multifamily housing providers. It accounts for the life course of dwellings and households, during which the latter attempt to maximise their satisfaction, which is calculated as the correspondence between their desired housing functions (e.g. a status symbol) and the functions of dwellings. To illustrate the model’s potential uses, we explore the sensitivity of its outputs to changes in dwellings’ and buildings’ qualitative and quantitative features by looking at two key indicators of housing sustainability: floor space per capita and vacancy rate. We firstly observe that a supply dominated by medium-to-large dwellings and the application of less strict occupancy rules can result in housing underoccupancy. Secondly, it emerges that certain combinations of housing features engender a lower vacancy rate inasmuch as they more successfully generate housing functions. We conclude that by enabling housing providers to explore the complex human-environment interactions of the housing system, ReMoTe-S can be used to inform a sustainable management of housing stock.

Generation of Synthetic Populations in Social Simulations: A Review of Methods and Practices

Kevin Chapuis, Patrick Taillandier and Alexis Drogoul
Journal of Artificial Societies and Social Simulation 25 (2) 6

Kyeywords: Synthetic Population, Agent-Based Simulation, Model Initialisation, Data-Driven Social Simulation
Abstract: To build realistic models of social systems, designers of agent-based models tend to incorporate a considerable amount of data, which influence the model outcomes. Data concerning the attributes of social agents, which compose synthetic populations, are particularly important but usually difficult to collect and therefore use in simulations. In this paper, we have reviewed state of the art methodologies and theories for building realistic synthetic populations for agent-based simulation models and practices in social simulations. We also highlight the discrepancies between theory and practice and outline the challenges in bridging this gap through a quantitative and narrative review of work published in JASSS between 2011 and 2021. Finally, we present several recommendations that could help modellers adopt best practices for synthetic population generation.

On the Interplay Among Multiple Factors: Effects of Factor Configuration in a Proof-Of-Concept Migration Agent-Based Model

Woi Sok Oh, Alvaro Carmona-Cabrero, Rafael Muñoz-Carpena and Rachata Muneepeerakul
Journal of Artificial Societies and Social Simulation 25 (2) 7

Kyeywords: Agent-Based Model, Human Migration, Factor Configuration, Decision-Making Process, Social Ties
Abstract: Many researchers have addressed what factors should be included in their models of coupled natural-human systems (CNHSs). However, few studies have explored how these factors should be incorporated (factor configuration). Theoretical underpinning of the factor configuration may lead to a better understanding of systematic patterns and sustainable CNHS management. In particular, we ask: (1) can factor configuration explain CNHS behaviors based on its theoretical implications? and (2) when disturbed by shocks, do CNHSs respond differently under varying factor configurations? A proof-of-concept migration agent-based model (ABM) was developed and used as a platform to investigate the effects of factor configuration on system dynamics and outcomes. Here, two factors, social ties and water availability, were assumed to have alternative substitutable, complementary, or adaptable relationships in influencing migration decisions. We analyzed how populations are distributed over different regions along a water availability gradient and how regions are culturally mixed under different factor configurations. We also subjected the system to a shock scenario of dropping 50% of water availability in one region. We found that substitutability acted as a bu er against the effect of water deficiency and prevented cultural mixing of the population by keeping residents in their home regions and slowing down residential responses against the shock. Complementarity led to the sensitive migration behavior of residents, accelerating regional migration and cultural mixing. Adaptability caused residents to stay longer in new regions, which gradually led to a well-mixed cultural condition. All together, substitutability, complementarity, and adaptability gave rise to different emergent patterns. Our findings highlight the importance of how, not just what, factors are included in a CNHS ABM, a lesson that is particularly applicable to models of interdisciplinary problems where factors of diverse nature must be incorporated.

Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling

Alexander Michels, Jeon-Young Kang and Shaowen Wang
Journal of Artificial Societies and Social Simulation 25 (2) 8

Kyeywords: Agent-Based Modeling, Particle Swarm Optimization, Calibration, CyberGIS, Influenza
Abstract: A challenge in computational Agent-Based Models (ABMs) is the amount of time and resources required to tune a set of parameters for reproducing the observed patterns of phenomena being modeled. Well-tuned parameters are necessary for models to reproduce real-world multi-scale space-time patterns, but calibration is often computationally intensive and time consuming. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm that has found wide use for complex optimization including nonconvex and noisy problems. In this study, we propose to use PSO for calibrating parameters in ABMs. We use a spatially explicit ABM of influenza transmission based in Miami, Florida, USA as a case study. Furthermore, we demonstrate that a standard implementation of PSO can be used out-of-the-box to successfully calibrate models and out-performs Monte Carlo in terms of optimization and efficiency.

Integrating Equity Considerations into Agent-Based Modeling: A Conceptual Framework and Practical Guidance

Tim G Williams, Daniel G Brown, Seth D Guikema, Tom M Logan, Nicholas R Magliocca, Birgit Müller and Cara E Steger
Journal of Artificial Societies and Social Simulation 25 (3) 1

Kyeywords: Agent-Based Model, Fairness, Justice, Reflexivity, Best Practice, Simulation
Abstract: Advancing equity is a complex challenge for society, science, and policy. Agent-based models are increasingly used as scientific tools to advance understanding of systems, inform decision-making, and share knowledge. Yet, equity has not received due attention within the agent-based modeling (ABM) literature. In this paper, we develop a conceptual framework and provide guidance for integrating equity considerations into ABM research and modeling practice. The framework conceptualizes ABM as interfacing with equity outcomes at two levels (the science-society interface and within the model itself) and the modeler as a filter and lens that projects knowledge between the target system and the model. Within the framework, we outline three complementary, equity-advancing action pathways: (1) engage stakeholders, (2) acknowledge positionality and bias, and (3) assess equity with agent-based models. For Pathway 1, we summarize existing guidance within the participatory modeling literature. For Pathway 2, we introduce the positionality and bias document as a tool to promote modeler and stakeholder reflexivity throughout the modeling process. For Pathway 3, we synthesize a typology of approaches for modeling equity and offer a set of preliminary suggestions for best practice. By engaging with these action pathways, modelers both reduce the risks of inadvertently perpetuating inequity and harness the opportunities for ABM to play a larger role in creating a more equitable future.

An Agent-Based Model to Support Infection Control Strategies at School

Daniele Baccega, Simone Pernice, Pietro Terna, Paolo Castagno, Giovenale Moirano, Lorenzo Richiardi, Matteo Sereno, Sergio Rabellino, Milena Maria Maule and Marco Beccuti
Journal of Artificial Societies and Social Simulation 25 (3) 2

Kyeywords: Agent-Based Simulation, SARS-CoV-2, Non-Pharmaceutical Interventions, Surveillance Testing, School
Abstract: Many governments enforced physical distancing measures during the COVID-19 pandemic to avoid the collapse of often fragile and overloaded health care systems. Following the physical distancing measures, school closures seemed unavoidable to keep the transmission of the pathogen under control, given the potentially high-risk of these environments. Nevertheless, closing schools was considered an extreme and the last resort of governments, and so various non-pharmaceutical interventions in schools were implemented to reduce the risk of transmission. By means of an agent-based model, we studied the efficacy of active surveillance strategies in the school environment. Simulations settings provided hypothetical although realistic scenarios which allowed us to identify the most suitable control strategy to avoid massive school closures while adapting to contagion dynamics. Reducing risk by means of public policies explored in our study is essential for both health authorities and school administrators.

Structural Effects of Agent Heterogeneity in Agent-Based Models: Lessons from the Social Spread of COVID-19

D. Cale Reeves, Nicholas Willems, Vivek Shastry and Varun Rai
Journal of Artificial Societies and Social Simulation 25 (3) 3

Kyeywords: Agent-Based Model, Diffusion Model, Empirical Data-Driven Model, Heterogeneous Population, Model Performance, COVID-19
Abstract: Modeling human behavior in the context of social systems in which we are embedded realistically requires capturing the underlying heterogeneity in human populations. However, trade-offs associated with different approaches to introducing heterogeneity could either enhance or obfuscate our understanding of outcomes and the processes by which they are generated. Thus, the question arises: how to incorporate heterogeneity when modeling human behavior as part of population-scale phenomena such that greater understanding is obtained? We use an agent-based model to compare techniques of introducing heterogeneity at initialization or generated during the model’s runtime. We show that initializations with unstructured heterogeneity can interfere with a structural understanding of emergent processes, especially when structural heterogeneity might be a key part of driving how behavioral responses dynamically shape emergence in the system. We find that incorporating empirical population heterogeneity – even in a limited sense – can substantially contribute to improved understanding of how the system under study works.

Calibrating Agent-Based Models of Innovation Diffusion with Gradients

Florian Kotthoff and Thomas Hamacher
Journal of Artificial Societies and Social Simulation 25 (3) 4

Kyeywords: Agent-Based Modeling, Multi-Agent Simulation, Innovation Diffusion, Adoption Model, Decision Making, Calibration
Abstract: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R2 ≃ 0.7.

Egalitarian Sharing Explains Food Distributions in a Small-Scale Society

Marcos Pinheiro
Journal of Artificial Societies and Social Simulation 25 (3) 5

Kyeywords: Hunter-Gatherers, Food Sharing, Evolution of Cooperation, Egalitarianism, Agent-Based Model
Abstract: Among social anthropologists, there is virtual consensus that the food-sharing practices of small-scale non-agricultural groups cannot be understood in isolation from the broader repertoire of leveling strategies that prevent would-be dominants from exercising power and influence over likely subordinates. In spite of that widespread view, quantitatively rigorous empirical studies of food sharing and cooperation in small-scale human groups have typically ignored the internal connection between leveling of income and political power, drawing inspiration instead from evolutionary models that are neutral about social role asymmetries. In this paper, I introduce a spatially explicit agent-based model of hunter-gatherer food sharing in which individuals are driven by the goal of maximizing their own income while minimizing income asymmetries among others. Model simulation results show that seven basic patterns of inter-household food transfers described in detail for the Hadza hunters of Tanzania can be simultaneously reproduced with striking accuracy under the assumption that agents selectively support and carry on sharing interactions in ways that maximize their income leveling potential.

How Culture Influences the Management of a Pandemic: A Simulation of the COVID-19 Crisis

Kurt Kreulen, Bart de Bruin, Amineh Ghorbani, René Mellema, Christian Kammler, Lois Vanhée, Virginia Dignum and Frank Dignum
Journal of Artificial Societies and Social Simulation 25 (3) 6

Kyeywords: COVID-19, Agent-Based Modelling, Culture, Values, Epidemiological Models, Pandemic
Abstract: Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.

The Ethics of Agent-Based Social Simulation

David Anzola, Pete Barbrook-Johnson and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 25 (4) 1

Kyeywords: Agent-Based Modelling, Research Ethics, Ethical Standards, Responsible Science, Scientific Integrity, Code of Ethics
Abstract: The academic study and the applied use of agent-based modelling of social processes has matured considerably over the last thirty years. The time is now right to engage seriously with the ethics and responsible practice of agent-based social simulation. In this paper, we first outline the many reasons why it is appropriate to explore an ethics of agent-based modelling and how ethical issues arise in its practice and organisation. We go on to discuss different approaches to standardisation as a way of supporting responsible practice. Some of the main conclusions are organised as provisions in a draft code of ethics. We intend for this draft to be further developed by the community before being adopted by individuals and groups within the field informally or formally

Multimodal Evolutionary Algorithms for Easing the Complexity of Agent-Based Model Calibration

Juan Francisco Robles, Enrique Bermejo, Manuel Chica and Óscar Cordón
Journal of Artificial Societies and Social Simulation ()

Kyeywords: Agent-Based Modelling, Model Validation, Automatic Calibration, Multimodal Optimisation, Multimodal Evolutionary Algorithms
Abstract: Agent-based modelling usually involves a calibration stage where a set of parameters needs to be estimated. The calibration process can be automatically performed by using calibration algorithms which search for an optimal parameter configuration to obtain quality model fittings. This issue makes the use of multimodal optimisation methods interesting for calibration as they can provide diverse solution sets with similar and optimal fitness. In this contribution, we compare nine competitive multimodal evolutionary algorithms, both classical and recent, to calibrate agent-based models. We analyse the performance of each multimodal evolutionary algorithm on 12 problem instances of an agent-based model for marketing (i.e. 12 different virtual markets) where we calibrate 24 to 129 parameters to generate two main outputs: historical brand awareness and word-of-mouth volume. Our study shows a clear dominance of SHADE, L-SHADE, and NichePSO over the rest of the multimodal evolutionary algorithms. We also highlight the benefits of these methods for helping modellers to choose from among the best calibrated solutions.