411 articles matched your search for
Social, Behavioral, Modeling, Game, Multiplayer
Giorgio Brajnik and Marji Lines
Journal of Artificial Societies and Social Simulation 1 (1) 2
Kyeywords: Qualitative Modeling, Qualitative Reasoning, Decision Making, Allocation
Abstract: This paper describes an application of recently developed qualitative reasoning techniques to complex, socio-economic allocation problems. We explain why we believe traditional optimization methods are inappropriate and how qualitative reasoning could overcome some of these shortcomings. A case study is presented where an authority is expected to devise a policy that satisfies certain constraints. We describe how sets of rules of thumb implementing such a policy can be analyzed and validated by the decision maker using a program which automatically builds and simulates qualitative models of the underlying dynamical system. Such a program constructs and simulates models from incomplete descriptions of initial states and functional relationships between variables. We show that it nevertheless gives sufficient information to the decision maker.
Federico Cecconi and Domenico Parisi
Journal of Artificial Societies and Social Simulation 1 (2) 1
Kyeywords: Artificial Life, Social Survival Strategies, Centralized Resources
Abstract: The paper introduces the concepts of individual survival strategies (ISS) and social survival strategies (SSS) and presents three sets of simulations of a particular type of SSS: the Central Store (CS) strategy, according to which the individuals in a group contribute part of their resources to a central mechanism that can redistribute these resources or make other uses of them. CS and ISS both allow a group of individuals to survive in a favourable environment although group size is slower to reach a steady state in the CS group because of the lower selective pressure on individuals' resource production. However, only CS groups survive in a less favourable environment apparently because the CS functions as a safety net for the individuals in the group. Although CS strategies can have this and other advantages over ISS, if individuals are left free to decide whether or not to give their resources to the CS, they tend not to do so. In other words, they abandon the CS strategy and revert to ISS. Because CS strategies characterize an increasing number of human societies since Neolithic times an important research problem is to identify and reproduce in the simulations, how groups of individuals that tend to act egoistically and not to give their resources to the CS, can be induced to do so.
Jaime Simão Sichman
Journal of Artificial Societies and Social Simulation 1 (2) 3
Kyeywords: Emergent Organizations, Dependence-Based Interaction, Social Behaviour, Open Systems
Abstract: This paper presents the main features and some simulation results for the DEPINT system, a multi-agent system conceived to illustrate some essential aspects of a social reasoning mechanism (Sichman, 1995), based on the notion of social dependence (Castelfranchi et al., 1992). This social reasoning mechanism is considered to be an essential building block of really autonomous agents, immersed in an open multi-agent system (MAS) context, i.e., where agents may dynamically enter or leave the society, without any global control. As the adaptation of an agent in such a scenario concerns, dependence relations allow an agent to know which of his goals are achievable and which of his plans are feasible at any moment. This way, an agent may dynamically choose a goal to pursuit and a plan to achieve it, being sure that every skill needed to accomplish the selected plan is available in the society. Concerning coalition formation, this model introduces the notion of dependence situation, which allows an agent to evaluate the susceptibility of other agents to adopt his goals, since agents are not necessarily supposed to be benevolent and therefore automatically adopt the goals of each other. Finally, as regardsbelief revision, the social reasoning mechanism allows an agent to detect that his representation of the others is inconsistent. Because agents' interactions are guided by their information about the others, it is exactly during these interactions that they may detect that this information is either incorrect or incomplete, and eventually revise it.
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.
Andreas Flache and Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 1 (3) 1
Kyeywords: Cellular Automata, Social Dynamics, Modelling
Abstract: The article argues that using cellular automata (CA) is a promising modelling approach to understand social dynamics. The first section introduces and illustrates the concept of CA. Section 2 gives a short history of CA in the social sciences. Section 3 describes and analyses a more complicated model of evolving support networks. The final section summarises the advantages of the CA approach.
Journal of Artificial Societies and Social Simulation 1 (3) 2
Kyeywords: Evolutionary Algorithms, Genetic Programming, Social Evolution, Selectionist Paradigm
Abstract: This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.
Nicole J. Saam and Andreas G. Harrer
Journal of Artificial Societies and Social Simulation 2 (1) 2
Kyeywords: Simulation of Norms, Social Inequality, Functions of Norms
Abstract: In this paper, we compare the computational and sociological study of norms, and resimulate previous simulations (Conte and Castelfranchi 1995a, Castelfranchi, Conte and Paolucci 1998) under slightly different conditions. First, we analyze the relation between norms, social inequality and functional change more closely. Due to our results, the hypothesis stating that the "finder-keeper" norm while controlling aggression efficaciously reduces social inequality holds only in quite egalitarian societies. Throughout a variety of inegalitarian societies, it instead increases social inequality. This argument which can be traced back to Marx is being investigated by use of computer simulations of artificial societies. Second, we remodel normative behaviour from a sociological point of view by implementing Haferkamp's theory of action approach to deviant behaviour. Following the game theoretic models, the computational study of norms has up to now ignored the importance of power in explaining how norms affect social behaviour, how norms emerge, become established and internalized, and change. By simulating Haferkamp and repeating the Conte and Castelfranchi experiments, we demonstrate that it is possible to integrate power into computational models of norms.
Journal of Artificial Societies and Social Simulation 2 (1) 3
Kyeywords: Multi-Level Simulation, Social Simulation, Lisp, Computer Modelling, Social Simulation Tookit
Abstract: A package of Lisp functions is described which implements a simple multi-level simulation toolkit, MLS. Its design owes a great deal to MIMOSE. MLS runs within Lisp-Stat. It offers a set of functions, macros and objects designed to make the specification of multi-level models straightforward and easy to understand. Lisp-Stat provides a Lisp environment, statistical functions and easy to use graphics, such as histograms, scatterplots and spin-plots, to make the results of multi-level simulations easy to visualise.
Rosaria Conte and Scott Moss
Journal of Artificial Societies and Social Simulation 2 (1) 4
Kyeywords: Agent-Based Social Simulation, Special Interest Group, AgentLink
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.
Journal of Artificial Societies and Social Simulation 2 (3) 5
Kyeywords: Population Studies, Marriage Rules, Demographic Constraints on Choice Behavior, Social Class, Social Anthropology
Abstract: This article presents and illustrates a new methodology for testing hypotheses about the departure of marriage choices from baseline models of random mating in an actual kinship and marriage network of a human population. The fact that demographic constraints can drastically affect the raw frequencies of different types of marriage suggests that we must reexamine or even throw out - as methodologically flawed - statistical conclusions regarding marriage "rules" from most of the existing empirical case studies. The development of the present methods, in contrast, enables researchers to decompose those behavioral tendencies that can be taken as agent-based social preferences, institutional "rules" or marriage structure from those behaviors whose divergent frequencies are merely a by-product or epiphenomena of demographic constraints on the availability of potential spouses. The family of random baseline models used here enables a researcher to identify overall global structures of marriage rules such as dual organization as well as more local of egocentric rules such as rules favoring marriage with certain kinds of relatives. Based on random permutations of the actual data in a manner that controls for the effects of demographic factors across different cases, the new methods are illustrated for three case studies: a village in Sri Lanka with a novel form of dual organization detected by this methodology, a cross-class analysis of a village in Indonesia, and an analysis of a farming village in Austria in which a structurally endogamous subset of villages is identified by the method and shown to form the backbone of a class-based landed property system.
Jürgen Klüver and Jörn Schmidt
Journal of Artificial Societies and Social Simulation 2 (3) 7
Kyeywords: Boolean Networks, Social Systems, Geometry, Dynamics, Theoretical Sociology, Control Parameters
Abstract: Structurally orientated sociologists tend to neglect the dynamical aspects of social systems, whereas theorists of social systems emphasize systems dynamics but only rarely analyze structural features of their domains. The aim of this paper is to integrate dynamical and structural approaches by means of the analysis of particular artificial systems, namely logical or Boolean networks, and their geometry. It is well known that the dynamics of Boolean networks and the logically similar cellular automata are governed by control parameters. Less well known is the fact that the geometry of these artificial systems, understood as their topology and metric, also contain specific control parameters. These "geometrical" control parameters can be expressed using graph theoretical concepts such as the density of graphs or geodetical properties. Further, the dynamics of those artificial systems depend on the values for the geometrical parameters. These mathematical investigations are quite important for social research: On the one hand, social dynamics and social structure appear to be two closely related aspects of social reality; on the other hand, a general hypothesis may be drawn from our results, namely that social structural inequality yields simple dynamics whereas social equality gives rise to complex dynamics. Therefore the dynamical complexity of modern democratic societies may be in part due to their democratic structures.
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.
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.
Journal of Artificial Societies and Social Simulation 3 (1) forum/1
Kyeywords: Simulation, Teaching, Social Processes, Programming Languages, Matlab
Abstract: Programming languages for social simulations are rapidly proliferating. The result is a Tower of Babel effect: Many of us find it increasingly effortful to learn and to teach more programming languages and increasingly difficult to sustain an audience beyond the programming dialect of our choice. We need a programming lingua franca. Here I argue why Matlab might be worth our consideration, especially to teach simulation programming techniques.
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
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.
Peter Tucker and Isobel Fletcher
Journal of Artificial Societies and Social Simulation 3 (3) 2
Kyeywords: Behavioural Change, Composting, Intervention, Material Diversion, Process Modelling, Social Interaction, Stochastic Modelling, Sustainable Development, Waste Management
Abstract: This paper describes a simulation model of community home composting behaviour based on distributions of individual households, each actively managing the organic fraction of their own domestic waste. The model predicts overall participation levels and the individual and collective flows and compositions of the materials diverted into the compost bin. The take up of home composting by new composters and the drop-out of existing composters are modelled through invoking staged or random discrete events which perturb the model attitudes, and other attributes held by individual householders. An attitude-behaviour model then determines whether these attitude changes result in behavioural change. Post-event evaluations of the compost produced are simulated by integrating an empirical, technical model of the composting process into the behavioural model. This was accomplished by matching the input/output requirements of the two models via a common vector of material flow, and by feeding back the technical process quality monitoring data into the social model, as instances of discrete events. The simulation results are compared with survey data, and simulation results are presented to predict the longer-term sustainability of home composting within the community.
Ramzi Suleiman and Ilan Fischer
Journal of Artificial Societies and Social Simulation 3 (4) 1
Kyeywords: Prisoner's Dilemma, Intergroup Conflict, Evolution of Cooperation, Social Influence, Representation, Elections Frequency
Abstract: The study explores the evolution of decision strategies and the emergence of cooperation in simulated societies. In the context of an inter-group conflict, we simulate three different institutions for the aggregation of attitudes. We assume that: (a) the conflict can be modeled as an iterated Prisoner's Dilemma played by two decision makers, each representing her group for a fixed duration; (b) the performance of each group's representative influences her group members and, consequently, her prospects to be reelected. Our main objectives are: (1) to investigate the effects of three power-delegation mechanisms: Random Representation, Mean Representation, and Minimal Winning Coalition representation, on the emergence of representatives' decision strategies, (2) to investigate the effect of the frequency of elections on the evolving inter-group relations. Outcomes of 1080 simulations show that the emergence of cooperation is strongly influenced by the delegation mechanism, the election frequency, and the interaction between these two factors.
Journal of Artificial Societies and Social Simulation 3 (4) 3
Kyeywords: Game Theory, Classical Iterated Prisoner's Dilemma, Cooperation
Abstract: This paper reports results obtained with a strategy for the Iterated Prisoner's Dilemma. The paper describes a strategy that tries to incorporate a technique to forgive strategies that have defected or retaliated, in the hope of (re-)establishing cooperation. The strategy is compared to well-known strategies in the domain and results presented. The initial findings, as well as echoing past findings, provides evidence to suggest a higher degree of forgiveness can be beneficial and may result in greater rewards.
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.
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:
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.
Wolfgang Balzer, Karl R. Brendel and Solveig Hofmann
Journal of Artificial Societies and Social Simulation 4 (2) 1
Kyeywords: Social Simulation, Game Theory, Discrete Event Simulation, Model Theory, Confirmation, Impossibility Theorem
Abstract: The aim of this note is to clarify and to correct some arguments which are used in the debate about the comparison of discrete social simulation with other methodologies used in the study of social phenomena, notably those of game theory. Though part of what will be said also applies to non-discrete simulation, the arguments are investigated only as far as the discrete case is concerned. The main claims against each of both scientific approaches are considered in particular, i.e. "impossibility" of game theory and "unsoundness" of simulation studies. Regarding the latter, arguments are presented that items occurring in simulation studies correspond to the formal constituents of a scientific theory, and thus a comparison of both approaches on the same level is justified. The question whether a superiority of one of the two approaches can be stated is illuminated in the light of four dimensions: empirical adequacy, theoretical fruitfulness, social relevance, and simplicity. This leads to the conclusion that both claims are unjustified and should be avoided in the debate about the role and merits of social simulation.
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.
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.
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.
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?
Nigel Gilbert, Andreas Pyka and Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 4 (3) 8
Kyeywords: Innovation, Simulation of Social Networks, Mobile Communications, Biotechnology, Kene
Abstract: A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate \'artefacts\' that they hope will be innovations. The success of the artefacts is judged by an oracle that evaluates each artefact using a criterion that is not available to the agents. Agents are able to follow strategies to improve their artefacts either on their own (through incremental improvement or by radical changes), or by seeking partners to contribute additional knowledge. It is shown though experiments with the model's parameters that it is possible to reproduce qualitatively the characteristics of innovation networks in two sectors: personal and mobile communications and biotechnology.
Rob Stocker, David Green and David Newth
Journal of Artificial Societies and Social Simulation 4 (4) 5
Kyeywords: Social networks, artificial societies, connectivity, communication, cohesion, influence, complexity, simulation
Abstract: Social structure emerges from the interaction and information exchange between individuals in a population. The emergence of groups in animal and human social systems suggests that such social structures are the result of a cooperative and cohesive society. Using graph based models, where nodes represent individuals in a population and edges represent communication pathways, we simulate individual influence and the communication of ideas in a population. Simulations of Dunbar’s hypothesis (that natural group size in apes and humans arises from the transition from grooming behaviour to language or gossip) indicate that transmission rate and neighbourhood size accompany critical transitions of the order proposed in Dunbar’s work. We demonstrate that critical levels of connectivity are required to achieve consensus in models that simulate individual influence.
Marie-Edith Bissey and Guido Ortona
Journal of Artificial Societies and Social Simulation 5 (2) 2
Kyeywords: Cooperation, Conventions, Prisoner's Dilemma, Social Simulation, SWARM
Abstract: This paper describes a study of the robustness of cooperative conventions. We observe the effect of the invasion of non-cooperating subjects into a community adopting a cooperative convention. The convention is described by an indefinitely repeated prisoner-dilemma game. We check the effects on the robustness of the cooperating convention of two characteristics of the game, namely the size of the prisonner-dilemma groups and the "intelligence" of the players. The relevance for real-world problems is considered. We find that the "intelligence" of the players plays a crucial role in the way players learn to cooperate. The simulation program is written in SWARM (Java version).
David Brichoux and Paul E. Johnson
Journal of Artificial Societies and Social Simulation 5 (3) 1
Kyeywords: Protest; Social Movements; Swarm; Simulation; Critical Mass
Abstract: This paper presents an agent-based simulation model of protest activity. Agents are located in a two dimensional grid and have limited ability to observe the behavior of other agents in the grid. The model is used to explore questions inspired by research on different theories of individual motivation and the so-called theory of critical mass. The simulations describe individuals who support an effort to change a policy, but acting in support of that effort is costly. When the marginal effect of participation reaches a certain level, people are more likely to get involved. With certain configurations of parameter values, the simulations produce no sustained widespread participation in protest regardless of the presence of activists; under other conditions high levels of protest are usually sustained, even without activists. However, the addition of a surprisingly small group of activists radically changes the aggregate behavior of the model under some conditions, making high and sustained protest possible when it otherwise would not have been.
Journal of Artificial Societies and Social Simulation 5 (3) 8
Kyeywords: Social behaviour, complex systems, synthetic method, modelling
Abstract: In this study we consider some of the philosophical issues that should be taken into account when simulating social behaviour. Even though the ideas presented here are philosophical, they should be of interest more to researchers simulating social behaviour than to philosophers, since we try to note some problems that researchers might not put much attention to. We give notions of what could be considered a social behaviour, and mention the problems that arise if we attempt to give a sharp definition of social behaviour in a broad context. We also briefly give useful concepts and ideas of complex systems and abstraction levels (Gershenson, 2002), since any society can be seen as a complex system. We discuss the problems that arise while modelling social behaviour, mentioning the synthetic method as a useful approach for contrasting social theories, because of the complexities of the phenomena they model. In addition, we note the importance of the study of social behaviour for the understanding of cognition. We hope that the ideas presented here motivate the interest and debate of researchers simulating social behaviour in order to pay attention to the problems mentioned in this work, and attempt to provide more suitable solutions to them than the ones proposed here.
John Scott and Scott Moss
Journal of Artificial Societies and Social Simulation 5 (3) 9
Kyeywords: European Social Simulation Association, development of social simulation research, education and application.
Abstract: There is growing agreement that the time has come to form a learned society to promote the development of social simulation. The undersigned wish to propose the formation of a European Social Simulation Association (ESSA). Recognising parallel interests and developments in North America, Latin America and Australasia, we would intend ESSA to coordinate with similar organisations in those and other regions to organise an international federation to support the development of social simulation research, education and application.
Journal of Artificial Societies and Social Simulation 5 (4) 4
Kyeywords: Norms, Reputation, Social Groups, Group Reputation, Stereotypes
Abstract: This paper demonstrates the role of group normative reputation in the promotion of an aggression reducing possession norm in an artificial society. A previous model of normative reputation is extended such that agents are given the cognitive capacity to categorise other agents as members of a group. In the previous model reputational information was communicated between agents concerning individuals. In the model presented here reputations are projected onto whole groups of agents (a form of "stereotyping"). By stereotyping, norm followers outperform cheaters (who do not follow the norm) under certain conditions. Stereotyping, by increasing the domain of applicability of a piece of reputational information, allows agents to make informed decisions concerning interactions with agents which no other agent has previously met. However, if conditions are not conducive, stereotyping can completely negate norm following behaviour. Group reputation can be a powerful mechanism, therefore, for the promotion of beneficent norms under the right conditions.
Journal of Artificial Societies and Social Simulation 5 (4) 5
Kyeywords: Contagion, evolutionary epidemiology of culture, cultural evolution, cultural selection, meme, allomeme, cultural trait, Murdock’s Ethnographic Atlas, Axelrod’s Cultural Model, Social Interaction Model, SIM.
Abstract: A simulation is presented of a grid of connected societies of reproducing agents. These agents are capable of horizontal and vertical transmission of non-genetic cultural traits (memes). This simulation exhibits the theoretically predicted effect that horizontally transmitted memes are less likely, overall, to be encountered in geographical isolation than strictly vertically transmitted ones. Furthermore, when horizontal memes are under cultural selection, and thus behave ‘contagiously’, their likelihood of geographical isolation is virtually eliminated. By contrast, natural selection has far weaker effects than cultural selection in reducing geographical isolation. Thus it should be possible to identify contagious memes by an examination of their geographical distribution. The degree of geographical isolation of 17 categories of postulated cultural traits in an ethnographic data set of 863 societies is then examined, and compared with the simulations, using z-tests. Using this method, the empirical data can be sorted into four broad categories, each with a different spectrum of probabilities of mode of transmission and contagion.
Laurie Brown and Ann Harding
Journal of Artificial Societies and Social Simulation 5 (4) 6
Kyeywords: Social modelling, microsimulation, public policy, Australia, NATSEM
Abstract: This paper provides an overview of social modelling and in particular a general introduction to and insight into the potential role and usefulness of micro-simulation in contributing to public policy. Despite having made a major contribution to the development of tax and cash transfer policies, there are many important areas of government policy to which microsimulation has not yet been applied or only slow progress has been made. The paper starts with a brief review of some of the main distinguishing characteristics of social models. This provides a contextual background to the main discussion on recent microsimulation modelling developments at the National Centre for Social and Economic Modelling (NATSEM) in Canberra, Australia, and how these models are being used to inform social and economic policy in Australia. Examples include: NATSEM’s static tax and cash transfer model (STINMOD); modelling the Australian Pharmaceutical Benefits Scheme; application of dynamic modelling for assessing future superannuation and retirement incomes; and the development of a regional microsimulation model (SYNAGI). Various technical aspects of the modelling are highlighted in order to illustrate how these types of socio-economic models are constructed and implemented. The key to effective social modelling is to recognise what type of model is required for a given task and to build a model that will meet the purposes for which it is intended. The potential of microsimulation models in the social security, welfare and health fields is very significant. However, it is important to recognise that policy decisions are going to involve value judgements - policies are created and implemented within a political environment. The aim is for social modelling, and in particular policy simulations, to contribute to a more rational analysis and informed debate. In this context, microsimulation models can make a significant contribution to the evaluation and implementation of ‘just and fair’ public policy.
Olivier Barreteau, Christophe Le Page and Patrick D'aquino
Journal of Artificial Societies and Social Simulation 6 (2) 10
Kyeywords: Role Playing Games, Participatory simulation, Companion Modelling, Artificial Societies
Abstract: This special collection of papers on Role-Playing Games, Models and Negotiation Processes presents a selection of papers from two thematic sessions at the International Society for Ecological Economics conference held in Sousse, Tunisia, in February 2002. The aim of these thematic sessions was to share experiments involving negotiation using models and role-playing games (RPG), in order to review the range of these experiments and the methodological difficulties encountered.
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.
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.
William's Daré and Olivier Barreteau
Journal of Artificial Societies and Social Simulation 6 (3) 6
Kyeywords: Role-Playing Game, Social Reality, Senegal, Irrigated System, Conversational Analysis
Abstract: Associations of multi-agent systems and role-playing games (RPG) have shown their relevance to tackle complex and dynamic social systems sharing common resources. Now, some are used in participatory processes as group decision support tools to promote information exchange of between stakeholders. In a RPG, stakeholders are placed in a virtual world where roles are allotted and rules are defined. In this approach, a question arises: do they adhere to the rules given by the game or do they use parts of their own reality? This article focuses on the link between play and reality in negotiation processes. The research was conducted in irrigated systems of the Senegal River valley. A methodology is proposed to test how reality is brought into the game. Qualitative interviews about negotiation processes in reality and in the game allowed us to analyze interactions and behaviors of participants. Results showed in the case presented that (1) stakeholders have accepted the schematic representation of their reality, (2) the social background of players interferes with roles playing in the game, (3) the game reveals to observers social relationships between players.
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.
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.
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.
Claudia Pahl-Wostl and Eva Ebenhöh
Journal of Artificial Societies and Social Simulation 7 (1) 3
Kyeywords: Social Simulation, Experimental Economics, Common Pool Resource Games, Adaptive Toolbox, Altruistic Punishment
Abstract: This article describes a social simulation model based on an economic experiment about altruistic behavior. The experiment by Fehr and Gächter showed that participants made frequent use of costly punishment in order to ensure continuing cooperation in a common pool resource game. The model reproduces not only the aggregated but also the individual data from the experiment. It was based on the data rather than theory. By this approach new insights about human behaviour and decision making may be found. The model was not designed as a stand-alone model, but as a starting point for a comprehensive Adaptive Toolbox Model. This may form a framework for modelling results from different economic experiments, comparing results and underlying assumptions, and exploring whether the insights thus gained also apply to more realistic situations.
Matthias Scheutz and Paul Schermerhorn
Journal of Artificial Societies and Social Simulation 7 (1) 4
Kyeywords: Conflict Resolution, Stopping Games, Signaling
Abstract: We investigate various strategies for stopping games embedded in the larger context of an artificial life simulation, where agents compete for food in order to survive and have offspring. In particular, we examine the utility of letting agents display their action tendencies (e.g., "continue to play" vs. "quitting the game" at any given point in the game), which agents can take into account when making their decisions. We set up a formal framework for analyzing these "embedded stopping games" and present results from several simulation studies with different kinds of agents. Our results indicate that while making use of action tendency cues is generally beneficial, there are situations in which agents using stochastic decision mechanisms perform better than agents whose decisions are completely determined by their own and their opponents' displayed tendencies, particularly when competing with agents who lie about their action tendencies.
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.
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.
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.
Nuno David, Maria Bruno Marietto, Jaime Simão Sichman and Helder Coelho
Journal of Artificial Societies and Social Simulation 7 (3) 4
Kyeywords: Interdisciplinary Research, Social-Scientific Models, Multiagent-Based Models, Verification and Validation
Abstract: This article reports an exploratory survey of the structure of interdisciplinary research in Agent-Based Social Simulation. One hundred and ninety six researchers participated in the survey completing an on-line questionnaire. The questionnaire had three distinct sections, a classification of research domains, a classification of models, and an inquiry into software requirements for designing simulation platforms. The survey results allowed us to disambiguate the variety of scientific goals and modus operandi of researchers with a reasonable level of detail, and to identify a classification of agent-based models used in simulation. In particular, in the interdisciplinary context of social-scientific modelling, agent-based computational modelling and computer engineering, we analyse the extent to which these paradigmatic models seem to be mutually instrumental in the field. We expect that our proposal may improve the viability of submitting, explaining and comparing agent-based simulations in articles, which is an important methodological requirement to consolidate the field. We also expect that it will motivate other proposals that could further validate, extend or change ours, in order to refine the classification with more types of models.
Ron Sun and Isaac Naveh
Journal of Artificial Societies and Social Simulation 7 (3) 5
Kyeywords: Cognition, Cognitive Architecture, Cognitive Modeling, Classification Decision Making
Abstract: Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance of the organization. It is suggested that the two disciplines, cognitive modeling and social simulation, which have so far been pursued in relative isolation from each other, can be profitably integrated.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 7 (4) 5
Kyeywords: Methodology, Modelling, Social Simulation, MABS, Theory, Philosophy
Abstract: This paper suggests procedures for decreasing misunderstanding between modellers in social simulation, aiming at helping modellers comprehending a certain phenomena from different perspectives, being aware of the relativity of each approach, and drawing conclusions from the different perspectives. A hierarchy of four levels of language, namely, cultural or natural language, modelling and theoretical paradigm, modelling language, and simulation programming language, is proposed and exemplified as a framework for examining simulation models - assumptions of language embedded in the model at each level are made explicit. Afterwards, switching between languages is suggested for achieving different interpretations and alternative explanations of a model; alongside, as a synthesis from different interpretations, to draw in an interpretive conclusion is suggested. In addition, Interpretive Systemology, a soft systems approach, is proposed as another innovative alternative for better understanding social simulation models, as it recommends undertaking the whole modelling process from different perspectives. The hierarchy of languages, and switching between languages, will be placed against the whole modelling process as understood by Edmonds (2000).
Johannes Kottonau and Claudia Pahl-Wostl
Journal of Artificial Societies and Social Simulation 7 (4) 6
Kyeywords: Attitude Formation, Social Simulation, Voting Behavior
Abstract: Understanding the dynamics of attitude formation is a key issue in social psychology. The paper presents a computational model for simulating the formation and change of attitudes and the influence of the strength of attitudes on behavior. The main conceptual challenge was to capture not only the traditional attitude concept but the full concept of attitude strength. This required combining different theoretical approaches within an integrated modeling framework. The dynamics of political attitudes of German citizens were chosen as specific application area because of the considerable amount of empirical data available. The model was tested by simulating the effects of different voting campaign strategies on the outcome of an election. Uncertainties in model parameters were accounted for by using Monte Carlo simulations. The implications of specific theoretical assumptions were investigated by performing model simulations for different model structures. The paper shows the potential of social simulation when it comes to bringing together different theoretical approaches. The integration within a model exposes gaps and inconsistencies and allows formulating hypotheses for further empirical investigations. The model has a modular structure and provides a rich repository for other modelers who are working in the field of attitude simulation.
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.
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]
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.
Pieter Buzing, A.E. Eiben and Martijn C. Schut
Journal of Artificial Societies and Social Simulation 8 (1) 2
Kyeywords: Social Simulation, Communication, Cooperation, Artificial Societies
Abstract: The main contribution of this paper is threefold. First, it presents a new software system for empirical investigations of evolving agent societies in SugarScape like environments. Second, it introduces a conceptual framework for modeling cooperation in an artificial society. In this framework the environmental pressure to cooperate is controllable by a single parameter, thus allowing systematic investigations of system behavior under varying circumstances. Third, it reports upon results from experiments that implemented and tested environments based upon this new model of cooperation. The results show that the pressure to cooperate leads to the evolution of communication skills facilitating cooperation. Furthermore, higher levels of cooperation pressure lead to the emergence of increased communication.
Journal of Artificial Societies and Social Simulation 8 (2) 7
Kyeywords: Anticipation, Autopoiesis, Social System, Incursion, Meaning
Abstract: Meaning can be communicated in addition to—and on top of—underlying processes of the information exchange. Meaning is provided to observations from the perspective of hindsight, while information processing follows the time axis. Simulations of anticipatory systems enable us to show how an observer can be generated within an information process, and how expectations can also be exchanged. Cellular automata will be used for the visualization. The exchange of observations among observers generates (a) uncertainty about the delineations in the observed system at each moment in time and (b) uncertainty about the dynamics of the interaction over time.
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".
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.
Petter Holme and Andreas Grönlund
Journal of Artificial Societies and Social Simulation 8 (3) 3
Kyeywords: Youth Culture, Adolescence, Multiagent Systems, Complex Networks, Social Networks
Abstract: What are the dynamics behind youth subcultures such as punk, hippie, or hip-hop cultures? How does the global dynamics of these subcultures relate to the individual's search for a personal identity? We propose a simple dynamical model to address these questions and find that only a few assumptions of the individual's behaviour are necessary to regenerate known features of youth culture.
Shah Jamal Alam, Frank Hillebrandt and Michael Schillo
Journal of Artificial Societies and Social Simulation 8 (3) 5
Kyeywords: Gift Exchange, Multiagent Systems, Habitus-Field Theory, Social Simulation
Abstract: In this paper, the implications of applying the idea of gift exchange mechanism, inspired from Pierre Bourdieu's sociological theories, into a market-based multiagent system are explored. Our work is directed in the continuation of investigations by Knabe (2002), who addressed the formation of different organizations structures between providers in a profit-oriented market. We nevertheless scrutinize various hypotheses centered to gift exchange in which an agent sacrifices its profit for a long-term binding relationship. The idea is to aim a larger profit through alliances that are formed as an effect of gift exchange. Our suggestion is that a multiagent system (MAS) based on the social mechanism of gift exchange performs a high level of robustness and durability. The market in our case comprises of customers and providers agents. The former calls for proposals for the tasks they introduce in the market, while the latter proceed with the execution of tasks based on their abilities and other circumstances. In well defined cases, the providers are able to delegate tasks to other providers. This allows them to give presents to other providers so that the gift exchange mechanism becomes possible. The agents are either profit-oriented or the ones who prefer exchanging gifts and are in pursuit of others who also practice this mechanism. A number of interesting scenarios are examined that include preservation of a hierarchical structure in the market, situations resulting in the forming of an alliance between two providers, and split of profit-oriented and gift-giving agents.
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.
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.
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.
Petra Ahrweiler and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 8 (4) 14
Kyeywords: Evaluation, Social Simulation, Standard View, Constructivist View, User Community
Abstract: This contribution deals with the assessment of the quality of a simulation by discussing and comparing "real-world" and scientific social simulations. We use the example of the Caffè Nero in Guildford as a 'real-world' simulation of a Venetian café. The construction of everyday simulations like Caffè Nero has some resemblance to the construction procedure of scientific social simulations. In both cases, we build models from a target by reducing the characteristics of the latter sufficiently for the purpose at hand; in each case, we want something from the model we cannot achieve easily from the target. After briefly discussing the 'ordinary' method of evaluating simulations called the 'standard view' and its adversary, a constructivist approach asserting that 'anything goes', we heed these similarities in the construction process and apply evaluation methods typically used for everyday simulations to scientific simulation and vice versa. The discussion shows that a 'user community view' creates the foundation for every evaluation approach: when evaluating the Caffè Nero simulation, we refer to the expert community (customers, owners) who use the simulation to get from it what they would expect to get from the target; similarly, for science, the foundation of every validity discussion is the ordinary everyday interaction that creates an area of shared meanings and expectations. Therefore, the evaluation of a simulation is guided by the expectations, anticipations and experience of the community that uses it – for practical purposes (Caffè Nero), or for intellectual understanding and for building new knowledge (science simulation).
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.
Bernd-O. Heine, Matthias Meyer and Oliver Strangfeld
Journal of Artificial Societies and Social Simulation 8 (4) 4
Kyeywords: Computer Simulation, Stylised Facts, Methodology, Groves Mechanism, Collusion, Game Theory
Abstract: The application of computer simulation as a research method raises two important questions: (1) Does simulation really offer added value over established methods? (2) How can the danger of arbitrariness caused by the extended modelling possibilities be minimised? We present the concept of stylised facts as a methodological basis for approaching these questions systematically. In particular, stylised facts provide a point of reference for a comparative analysis of models intended to explain an observable phenomenon. This is shown with reference to a recent discussion in the "economic analysis of accounting" literature where established methods, i.e. game theory, as well as computer simulations are used: the susceptibility of the "Groves mechanism" to collusion. Initially, we identify six stylised facts on the stability of collusion in empirical studies. These facts serve as a basis for the subsequent comparison of four theoretical models with reference to the above questions: (1) We find that the simulation models of Krapp and Deliano offer added value in comparison to the game theoretical models. They can be related to more stylised facts, achieve a better reproduction and exhibit far greater potential for incorporating yet unaddressed stylised facts. (2) Considered in the light of the stylised facts to which the models can be related, Deliano's simulation model exhibits considerable arbitrariness in model design and lacks information on its robustness. In contrast, Krapp demonstrates that this problem is not inherent to the method. His simulation model methodically extends its game theoretical predecessors, leaving little room for arbitrary model design or questionable parameter calibration. All in all, the stylisedfactsconcept proved to be very useful in dealing with the questions simulation researchers are confronted with. Moreover, a "research landscape" emerges from the derived stylised facts pinpointing issues yet to be addressed.
Guillaume Deffuant, Scott Moss and Wander Jager
Journal of Artificial Societies and Social Simulation 9 (1) 1
Kyeywords: Social Simulations, Epistemology, Validation, Simulation Methods
Abstract: The paper relates virtual dialogues about social simulation, with the implicit reference to Galieo\'s \'dialogues concerning two new sciences\'.
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.
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.
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.
David Joyce, John Kennison, Owen Densmore, Stephen Guerin, Shawn Barr, Eric Charles and Nicholas S. Thompson
Journal of Artificial Societies and Social Simulation 9 (2) 4
Kyeywords: Game Theory; Altruism; Prisoners' Dilemma; TIT FOR TAT; MOTH; Docking; Netlogo
Abstract: There are three prominent solutions to the Darwinian problem of altruism, kin selection, reciprocal altruism, and trait group selection. Only one, reciprocal altruism, most commonly implemented in game theory as a TIT FOR TAT strategy, is not based on the principle of conditional association. On the contrary, TIT FOR TAT implements conditional altruism in the context of unconditionally determined associates. Simulations based on Axelrod\'s famous tournament have led many to conclude that conditional altruism among unconditional partners lies at the core of much human and animal social behavior. But the results that have been used to support this conclusion are largely artifacts of the structure of the Axelrod tournament, which explicitly disallowed conditional association as a strategy. In this study, we modify the rules of the tournament to permit competition between conditional associates and conditional altruists. We provide evidence that when unconditional altruism is paired with conditional association, a strategy we called MOTH, it can out compete TIT FOR TAT under a wide range of conditions.
Journal of Artificial Societies and Social Simulation 9 (2) 5
Kyeywords: Social Cognition, Imitation, Cultural Co-Evolution, Differentiation, Reflexivity, Metacognition, Stochastic Game Theory, Endogenous Distributions, Metamimetic Games, Counterfactual Equilibrium
Abstract: Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive capacities are the ground for a new class of mimetic rules, we propose a formal framework, metamimetic games, that enables to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium — counterfactually stable state — and attractor are introduced. Finally, we give an interpretation of social differenciation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to immutable criteria that exist prior to the activity of agents.
Journal of Artificial Societies and Social Simulation 9 (2) 8
Kyeywords: Social Simulation, Economic Theory, User Community
Abstract: This paper presents the analysis of a dataset of publications in economics that makes use of simulations. Data areas explored in order to obtain information about diffusion of simulation techniques in time and across sub-disciplines. Moreover, following Robert Axelrod\'s concerns about the difficulties in sharing simulation models and their outputs, some peculiarities in the communication process among \'simulators\' are highlighted.
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.
Rainer Hegselmann and Ulrich Krause
Journal of Artificial Societies and Social Simulation 9 (3) 10
Kyeywords: Opinion Dynamics, Consensus/dissent, Bounded Confidence, Truth, Social Epistemology
Abstract: The paper analyzes the chances for the truth to be found and broadly accepted under conditions of cognitive division of labour combined with a social exchange process. Cognitive division of labour means, that only some individuals are active truth seekers, possibly with different capacities. The social exchange process consists in an exchange of opinions between all individuals, whether truth seekers or not. We de- velop a model which is investigated by both, mathematical tools and computer simulations. As an analytical result the Funnel theorem states that under rather weak conditions on the social process a consensus on the truth will be reached if all individuals posses an arbitrarily small inclination for truth seeking. The Leading the pack theorem states that under certain conditions even a single truth seeker may lead all individuals to the truth. Systematic simulations analyze how close and how fast groups can get to the truth depending on the frequency of truth seekers, their capacities as truth seekers, the position of the truth (more to the extreme or more in the centre of an opinion space), and the willingness to take into account the opinions of others when exchanging and updating opinions. A tricky movie visualizes simulations results in a parameter space of higher dimensions.
Journal of Artificial Societies and Social Simulation 9 (3) 4
Kyeywords: Social Influences, Persuasion Processes, Group Processes, Minority Influence, Computer Simulation, Modelling, Theory Verification, Simulation Experiments
Abstract: Very often in the history of mankind, social changes took place because a minority was successful in persuading the dominant majority of a new idea. Social psychology provides empirically well-founded theories of social influence that can explain the power of minorities at individual level. In this contribution, we present an agent-based computer simulation of one such theory, the Elaboration Likelihood Model (ELM). After introducing the theoretical background and our agent model, we present three simulation experiments that confirm past laboratory research but also go beyond its findings by adopting the method of computer simulation. First, we found that even a minority with low argument quality can be successful as long as it has positive peripheral cues. Second, our results suggest that a higher personal relevance of a topic for the majority led it to be more receptive to minority influence only when the minority showed neutral peripheral cues and very good arguments. Third, we found evidence that a neutral or only slightly biased majority is influenced more easily than a strongly biased one. To sum up, we consider these results to illustrate the notion that a well-presented, comprehensible and valid computer simulation provides a useful tool for theory development and application in an exploratory manner as long as it is well founded in terms of the model and theory.
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.
Journal of Artificial Societies and Social Simulation 9 (3) 6
Kyeywords: Financial Markets, Simulation, Minority Game, Mix-Game
Abstract: This paper studies the simulation of financial markets using an agent-based mix-game model which is a variant of the minority game (MG). It specifies the spectra of parameters of mix-game models that fit financial markets by investigating the dynamic behaviors of mix-game models under a wide range of parameters. The main findings are (a) in order to approach efficiency, agents in a real financial market must be heterogeneous, boundedly rational and subject to asymmetric information; (b) an active financial market must be dominated by agents who play a minority game; otherwise, the market would die; (c) the system could be stable if agents who play a majority game have a faster learning rate than those who play a minority game; otherwise, the system could be unstable. The paper then induces the rules for simulating financial markets with mix-game models and gives an example. Finally, the appendix of this paper presents background information about \'El Farol bar\', MG and mix-games.
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.
Lilian N. Alessa, Melinda Laituri and C. Michael Barton
Journal of Artificial Societies and Social Simulation 9 (4) 6
Kyeywords: Community-Based Complex Models, Mathematics, Social Sciences
Abstract: To date, many communities of practice (COP) in the social sciences have been struggling with how to deal with rapidly growing bodies of information. Many CoPs across broad disciplines have turned to community frameworks for complexity modeling (CFCMs) but this strategy has been slow to be discussed let alone adopted by the social sciences communities of practice (SS-CoPs). In this paper we urge the SS-CoPs that it is timely to develop and establish a CBCF for the social sciences for two major reasons: the rapid acquisition of data and the emergence of critical cybertools which can facilitate agent-based, spatially-explicit models. The goal of this paper is not to prescribe how a CFCM might be set up but to suggest of what components it might consist and what its advantages would be. Agent based models serve the establishment of a CFCM because they allow robust and diverse inputs and are amenable to output-driven modifications. In other words, as phenomena are resolved by a SS-CoP it is possible to adjust and refine ABMs (and their predictive ability) as a recursive and collective process. Existing and emerging cybertools such as computer networks, digital data collections and advances in programming languages mean the SS-CoP must now carefully consider committing the human organization to enabling a cyberinfrastructure tool. The combination of technologies with human interfaces can allow scenarios to be incorporated through 'if' 'then' rules and provide a powerful basis for addressing the dynamics of coupled and complex social ecological systems (cSESs). The need for social scientists to be more engaged participants in the growing challenges of characterizing chaotic, self-organizing social systems and predicting emergent patterns makes the application of ABMs timely. The enabling of a SS-CoP CFCM human-cyberinfrastructure represents an unprecedented opportunity to synthesize, compare and evaluate diverse sociological phenomena as a cohesive and recursive community-driven process.
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.
Sylvie Huet, Margaret Edwards and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 10 (1) 10
Kyeywords: Aggregate; Individual-Based Model; Innovation Diffusion; Mean Field Approximation; Model Comparison; Social Network Effect
Abstract: We compare the individual-based \'threshold model\' of innovation diffusion in the version which has been studied by Young (1998), with an aggregate model we derived from it. This model allows us to formalise and test hypotheses on the influence of individual characteristics upon global evolution. The classical threshold model supposes that an individual adopts a behaviour according to a trade-off between a social pressure and a personal interest. Our study considers only the case where all have the same threshold. We present an aggregated model, which takes into account variations of the neighbourhood sizes, whereas previous work assumed this size fixed (Edwards et al. 2003a). The comparison between the aggregated models (the first one assuming a neighbourhood size and the second one, a variable one) points out an improvement of the approximation in most of the value of parameter space. This proves that the average degree of connectivity (first aggregated model) is not sufficient for characterising the evolution, and that the node degree variability has an impact on the diffusion dynamics. Remaining differences between both models give us some clues about the specific ability of individual-based model to maintain a minority behaviour which becomes a majority by an addition of stochastic effects.
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.
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.
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.
Klaus Jaffe and Roberto Cipriani
Journal of Artificial Societies and Social Simulation 10 (1) 7
Kyeywords: Social Simulation, Interactions, Group Size, Selfish Heard, Cultural Evolution, Biological Evolution
Abstract: A one dimensional cellular automata model describes the evolutionary dynamics of cooperation when grouping by cooperators provides protection against predation. It is used to compare the dynamics of evolution of cooperation in three settings. G: only vertical transmission of information is allowed, as an analogy of genetic evolution with heredity; H: only horizontal information transfer is simulated, through diffusion of the majority\'s opinion, as an analogy of opinion dynamics or social learning; and C: analogy of cultural evolution, where information is transmitted both horizontally (H) and vertically (V) so that learned behavior can be transmitted to offspring. The results show that the prevalence of cooperative behavior depends on the costs and benefits of cooperation so that: a- cooperation becomes the dominant behavior, even in the presence of free-riders (i.e., non-cooperative obtaining benefits from the cooperation of others), under all scenarios, if the benefits of cooperation compensate for its cost; b- G is more susceptible to selection pressure than H achieving a closer adaptation to the fitness landscape; c- evolution of cooperative behavior in H is less sensitive to the cost of cooperation than in G; d- C achieves higher levels of cooperation than the other alternatives at low costs, whereas H does it at high costs. The results suggest that a synergy between H and V is elicited that makes the evolution of cooperation much more likely under cultural evolution than under the hereditary kind where only V is present.
Thomas Malsch, Christoph Schlieder, Peter Kiefer, Maren Lübcke, Rasco Perschke, Marco Schmitt and Klaus Stein
Journal of Artificial Societies and Social Simulation 10 (1) 9
Kyeywords: Communication, Communication-Oriented Modelling, Message Sign, Dynamic Networks, Bottom-up Approach, Temporality, Social Visibility, Reputation, Socionics
Abstract: Focusing on observable message signs and referencing structures, communication processes can be described and analysed as message reference networks which are characterized by dynamic pattern evolution. Computational simulation provides a way of obtaining insights into the factors driving such processes. Our paper describes a theoretical framework for communication-oriented modelling — the COM approach — that is centred around the notion of social visibility as a reputation mechanism. The approach contrasts with agent-based social networks on the one hand, and with bibliometric document networks on the other. In introducing our simulation environment COM/TE, typical properties of message reference networks are discussed in terms of a case study which deals with the impact of different media and styles of communication on emergent patterns of social visibility.
Thorsten Chmura and Thomas Pitz
Journal of Artificial Societies and Social Simulation 10 (2) 1
Kyeywords: Congestion Game, Minority Game, Laboratory Experiments, Reinforcement Algorithm, Payoff Sum Model, Game Theory, Experimental Economics
Abstract: The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is an asymmetric variation of the minority game with linear payoff functions. For each game, simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the player\'s behaviour.
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.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 10 (3) 2
Kyeywords: Dynamics, Network, Game Theory, Model,Simulation, Equilibrium, Complexity
Abstract: This article studies the dynamics in the formation processes of a mutual consent network in game theory setting: the Co-Author Model. In this article, a limited observation is applied and analytical results are derived. Then, 2 parameters are varied: the number of individuals in the network and the initial probability of the links in the network in its initial state. A simulation result shows a finding that is consistent with an analytical result for a state of equilibrium while it also shows different possible equilibria.
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.
András Németh and Károly Takács
Journal of Artificial Societies and Social Simulation 10 (3) 4
Kyeywords: Altruism, Teaching, Knowledge Transfer, Spatially Structured Social Dilemmas
Abstract: The evolution of altruism in humans is still an unresolved puzzle. Helping other individuals is often kinship-based or reciprocal. Several examples show, however, that altruism goes beyond kinship and reciprocity and people are willing to support unrelated others even when this is at a cost and they receive nothing in exchange. Here we examine the evolution of this "pure" altruism with a focus on altruistic teaching. Teaching is modeled as a knowledge transfer which enhances the survival chances of the recipient, but reduces the reproductive efficiency of the provider. In an agent-based simulation we compare evolutionary success of genotypes that have willingness to teach with those who do not in two different scenarios: random matching of individuals and spatially structured populations. We show that if teaching ability is combined with an ability to learn and individuals encounter each other on a spatial proximity basis, altruistic teaching will attain evolutionary success in the population. Settlement of the population and accumulation of knowledge are emerging side-products of the evolution of altruism. In addition, in large populations our simple model also produces a counterintuitive result that increasing the value of knowledge keeps fewer altruists alive.
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.
Stéphane Airiau, Sabyasachi Saha and Sandip Sen
Journal of Artificial Societies and Social Simulation 10 (3) 7
Kyeywords: Repeated Games, Evolution, Simulation
Abstract: Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2×2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.
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
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.
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.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 11 (1) 2
Kyeywords: Social Conventions, Fast and Frugal Heuristic Theory, Emergence of Lexicon, Data Mining, Signaling Games
Abstract: This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper, the agents are endowed with the cognitive skills necessary to categorize the input in a lexicographic way, a categorization process that is implemented by the means of data mining techniques. The second aspect is the social one, represented by the process of reiterate interactions among the agents who compose a population. The framework of this social process is that of evolutionary game theory, with a population of agents who are randomly matched in each period in order to play a game that, in this paper, is a kind of signaling game. The simulations show that the emergence of a socially shared meaning associated to a combination of symbols is, under the assumptions of this model, a statistically inevitable occurrence.
Journal of Artificial Societies and Social Simulation 11 (1) 5
Kyeywords: Social Simulation, Validation, Companion Modelling, Data Generating Mechanisms, Complexity
Abstract: This paper draws on the metaphor of a spectrum of models ranging from the most theory-driven to the most evidence-driven. The issue of concern is the practice and criteria that will be appro- priate to validation of different models. In order to address this concern, two modelling approaches are investigated in some detailed – one from each end of our metaphorical spectrum. Windrum et al. (2007) (http://jasss.soc.surrey.ac.uk/10/2/8.html) claimed strong similarities between agent based social simulation and conventional social science – specifically econometric – approaches to empirical modelling and on that basis considered how econometric validation techniques might be used in empirical social simulations more broadly. An alternative is the approach of the French school of \'companion modelling\' associated with Bousquet, Barreteau, Le Page and others which engages stakeholders in the modelling and validation process. The conventional approach is con- strained by prior theory and the French school approach by evidence. In this sense they are at opposite ends of the theory-evidence spectrum. The problems for validation identified by Windrum et al. are shown to be irrelevant to companion modelling which readily incorporate complexity due to realistically descriptive specifications of individual behaviour and social interaction. The result combines the precision of formal approaches with the richness of narrative scenarios. Companion modelling is therefore found to be practicable and to achieve what is claimed for it and this alone is a key difference from conventional social science including agent based computational economics.
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.
Journal of Artificial Societies and Social Simulation 11 (1) 8
Kyeywords: Imitation, Evolution of Cooperation, Helping Game, Indirect Reciprocity
Abstract: The relation between imitation and cooperation in evolutionary settings presents complex aspects. From one hand, in any environment where egoists are favored over cooperators by selection processes, imitation should lead to a further spreading of the former ones due to the combined processes of individual selection and replication of successful behaviors. On the other hand, if cooperators succeed in forming clusters of mutual helping individuals, imitation may have a positive effect on cooperation by further reproducing this locally dominant behavior. This paper explores the relationship between imitation and cooperation by mean of a simulation model based on two different Helping games. Our model shows that different imitation mechanisms can favor the spreading of cooperation under a wide range of conditions. Moreover, the interplay of imitation and other factors — e.g. the possibility of performing “conditional associations” strategies — can further foster the success of cooperative agents.
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.
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]
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.
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.
Gero Schwenk and Torsten Reimer
Journal of Artificial Societies and Social Simulation 11 (3) 4
Kyeywords: Decision Making; Cognition; Heuristics; Small World Networks; Social Influence; Bounded Rationality
Abstract: The concept of heuristic decision making is adapted to dynamic influence processes in social networks. We report results of a set of simulations, in which we systematically varied: a) the agents\' strategies for contacting fellow group members and integrating collected information, and (b) features of their social environment—the distribution of members\' status, and the degree of clustering in their network. As major outcome variables, we measured the speed with which the process settled, the distributions of agents\' final preferences, and the rate with which high-status members changed their initial preferences. The impact of the agents\' decision strategies on the dynamics and outcomes of the influence process depended on features of their social environment. This held in particular true when agents contacted all of the neighbors with whom they were connected. When agents focused on high-status members and did not contact low-status neighbors, the process typically settled more quickly, yielded larger majority factions and fewer preference changes. A case study exemplifies the empirical application of the model.
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.
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.
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]
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.
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.
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.
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.
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.
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.
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.
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.
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.
Patrick Groeber, Frank Schweitzer and Kerstin Press
Journal of Artificial Societies and Social Simulation 12 (2) 4
Kyeywords: Social Norms, Conventions, Bounded Confidence, Dynamic Networks
Abstract: A local culture denotes a set of rules on business behaviour among firms in a cluster. Similar to social norms or conventions, it is an emergent feature of interaction in an economic network. To model its emergence, we consider a distributed agent population, representing cluster firms. Further, we build on a continuous opinion dynamics model with bounded confidence (ε), which assumes that two agents only interact if differences in their behaviour are less than ε. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. Two aspects extend this framework: (i) The agent\'s in-group consisting of acquainted interaction partners is explicitly taken into account, leading to an effective agent behaviour as agents try to continue to interact with past partners and thus seek to stay sufficiently close to them. (ii) The in-group network structure changes over time, as agents form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, the model introduces a feedback mechanism of agent behaviour and in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low ε) the feedback mechanism helps find consensus more often, whereas for open-minded agents (high ε) this does not necessarily hold. Overall, the dynamics of agent interaction in clusters as modelled here, are conducive to consensus among all or a majority of agents.
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.
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.
Francesc S. Beltran, Salvador Herrando, Doris Ferreres, Marc-Antoni Adell, Violant Estreder and Marcos Ruiz-Soler
Journal of Artificial Societies and Social Simulation 12 (3) 5
Kyeywords: Cellular Automata, Computational Simulations, Language, Social Dynamics
Abstract: Language extinction as a consequence of language shifts is a widespread social phenomenon that affects several million people all over the world today. An important task for social sciences research should therefore be to gain an understanding of language shifts, especially as a way of forecasting the extinction or survival of threatened languages, i.e., determining whether or not the subordinate language will survive in communities with a dominant and a subordinate language. In general, modeling is usually a very difficult task in the social sciences, particularly when it comes to forecasting the values of variables. However, the cellular automata theory can help us overcome this traditional difficulty. The purpose of this article is to investigate language shifts in the speech behavior of individuals using the methodology of the cellular automata theory. The findings on the dynamics of social impacts in the field of social psychology and the empirical data from language surveys on the use of Catalan in Valencia allowed us to define a cellular automaton and carry out a set of simulations using that automaton. The simulation results highlighted the key factors in the progression or reversal of a language shift and the use of these factors allowed us to forecast the future of a threatened language in a bilingual community.
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.
Journal of Artificial Societies and Social Simulation 12 (4) 11
Kyeywords: Replication, Social Dilemma Situations, Trust, Simulation Methodology, Cooperation
Abstract: The paper at hand aimes at identifying the assumptions that lead to the results presented in an article by Michael Macy and Yoshimichi Sato published in PNAS. In answer to a failed replication, the authors provided the source code of their model and here the results of carefully studying that code are presented. The main finding is that the simulation program implements an assumption that is most probably an unwilling, unintended, and unwanted implication of the code. This implied assumption is never mentioned in Macy and Sato's article and if the authors wanted to program what they describe in their article then it is due to a programming error. After introducing the reader to the discussion, data that stem from a new replication based on the assumptions extracted from the source code is compared with the results published in Macy and Sato's original article. The replicated results are sufficiently similar to serve as a strong indicator that this new replication implements the same relevant assumptions as the original model. Afterwards it is shown that a removal of the dubious assumption leads to results that are dramatically different from those published in Macy and Sato's PNAS article.
Matthias Meyer, Iris Lorscheid and Klaus G. Troitzsch
Journal of Artificial Societies and Social Simulation 12 (4) 12
Kyeywords: Citation Analysis, Co-Citation Analysis, Lines of Research, Multidisciplinary, Science Studies, Social Simulation
Abstract: Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.
Marco A. Janssen
Journal of Artificial Societies and Social Simulation 12 (4) 13
Kyeywords: Replication, Model Analysis, Model-Based Archaeology, Population Dynamics, Social-Ecological Systems
Abstract: A replication and analysis of the Artificial Anasazi model is presented. It is shown that the success of replicating historical data is based on two parameters that adjust the carrying capacity of the Long House Valley. Compared to population estimates equal to the carrying capacity the specific agent behavior contributes only a modest improvement of the model to fit the archaeological records.
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.
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.
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.
Stuart Rossiter, Jason Noble and Keith R.W. Bell
Journal of Artificial Societies and Social Simulation 13 (1) 10
Kyeywords: Social Simulation, Methodology, Epistemology, Ideology, Validation
Abstract: Because of features that appear to be inherent in many social systems, modellers face complicated and subjective choices in positioning the scientific contribution of their research. This leads to a diversity of approaches and terminology, making interdisciplinary assessment of models highly problematic. Such modellers ideally need some kind of accessible, interdisciplinary framework to better understand and assess these choices. Existing texts tend either to take a specialised metaphysical approach, or focus on more pragmatic aspects such as the simulation process or descriptive protocols for how to present such research. Without a sufficiently neutral treatment of why a particular set of methods and style of model might be chosen, these choices can become entwined with the ideological and terminological baggage of a particular discipline. This paper attempts to provide such a framework. We begin with an epistemological model, which gives a standardised view on the types of validation available to the modeller, and their impact on scientific value. This is followed by a methodological framework, presented as a taxonomy of the key dimensions over which approaches are ultimately divided. Rather than working top-down from philosophical principles, we characterise the issues as a practitioner would see them. We believe that such a characterisation can be done 'well enough', where 'well enough' represents a common frame of reference for all modellers, which nevertheless respects the essence of the debate's subtleties and can be accepted as such by a majority of 'methodologists'. We conclude by discussing the limitations of such an approach, and potential further work for such a framework to be absorbed into existing, descriptive protocols and general social simulation texts.
Jennifer Badham and Rob Stocker
Journal of Artificial Societies and Social Simulation 13 (1) 11
Kyeywords: Social Networks, Network Generation, Clustering Coefficient, Assortativity
Abstract: Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 13 (1) 2
Kyeywords: Empirical Modeling, Genetic Optimization, Falsification
Abstract: The pioneering works in Agent-Based Modeling (ABM) - notably Schelling (1969) and Epstein and Axtell (1996) - introduced the method for testing hypotheses in "complex thought experiments" (Cederman 1997, 55). Although purely theoretical experiments can be important, the empirical orientation of the social sciences demands that the gap between modeled "thought experiments" and empirical data be as narrow as possible. In an ideal setting, an underlying theory of real-world processes would be tested directly with empirical data, according to commonly accepted technical and methodological standards. A possible procedure for narrowing the gap between theoretical assumptions and empirical data comparison is presented in this paper. It introduces a two-stage process of optimizing a model and then reviewing it critically, both from a quantitative and qualitative point of view. This procedure systematically improves a model's performance until the inherent limitations of the underlying theory become evident. The reference model used for this purpose simulates air traffic movements in the approach area of JFK International Airport in New York. This phenomenon was chosen because it provides a testbed for evaluating an empirical ABM in an application of sufficient complexity. The congruence between model and reality is expressed in simple distance measurements and is visually contrasted in Google Earth. Context knowledge about the driving forces behind controlled approaches and genetic optimization techniques are used to optimize the results within the range of the underlying theory. The repeated evaluation of a model's 'fitness' - defined as the ability to hit a set of empirical data points - serves as a feedback mechanism that corrects its parameter settings. The successful application of this approach is demonstrated and the procedure could be applied to other domains.
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.
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.
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.
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.
Mehdi Saqalli, Charles L. Bielders, Bruno Gerard and Pierre Defourny
Journal of Artificial Societies and Social Simulation 13 (2) 1
Kyeywords: Rule-Based Modelling, Rural Sahel, Confidence Building, Low-Data Context, Social Criteria
Abstract: Development issues in developing countries belong to complex situations where society and environment are intricate. However, such sites lack the necessary amount of reliable, checkable data and information, while these very constraining factors determine the populations' evolutions, such as villagers living in Sahelian environments. Beyond a game-theory model that leads to a premature selection of the relevant variables, we build an individual-centered, empirical, KIDS-oriented (Keep It Descriptive & Simple), and multidisciplinary agent-based model focusing on the villagers\' differential accesses to economic and production activities according to social rules and norms, mainly driven by social criteria from which gender and rank within the family are the most important, as they were observed and registered during individual interviews. The purpose of the work is to build a valid and robust model that overcome this lack of data by building a individual specific system of behaviour rules conditioning these differential accesses showing the long-term catalytic effects of small changes of social rules. The model-building methodology is thereby crucial: the interviewing process provided the behaviour rules and criteria while the context, i.e. the economic, demographic and agro-ecological environment is described following published or unpublished literature. Thanks to a sensitivity analysis on several selected parameters, the model appears fairly robust and sensitive enough. The confidence building simulation outputs reasonably reproduces the dynamics of local situations and is consistent with three authors having investigated in our site. Thanks to its empirical approach and its balanced conception between sociology and agro-ecology at the relevant scale, i.e. the individual tied to social relations, limitations and obligations and connected with his/her biophysical and economic environment, the model can be considered as an efficient "trend provider" but not an absolute "figure provider" for simulating rural societies of the Nigrien Sahel and testing scenarios on the same context. Such ABMs can be a useful interface to analyze social stakes in development projects.
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.
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.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 13 (3) 2
Kyeywords: Prisoner\'s Dilemma Game, Tags, Parochial Cooperation, Clustering, Small-World-Ness, NetLogo
Abstract: Researchers from many disciplines have been interested in the maintenance of cooperation in animal and human societies using the Prisoner\'s Dilemma game. Recent studies highlight the roles of cognitively simple agents in the evolution of cooperation who read tags to interact either discriminately or selectively with tolerably similar partners. In our study on a one-shot Prisoner\'s Dilemma game, artificial agents with tags and tolerance perceive dissimilarities to local neighbors to cooperate with in-group and otherwise defect. They imitate tags and learn tolerance from more successful neighbors. In terms of efficiency, society-wide cooperation can evolve even when the benefits of cooperation are relatively low. Meanwhile, tolerance however decreases as agents become homogenized. In terms of stability, parochial cooperators are gullible to the deviants defectors displaying tolerably similar tags. We find that as the benefits of cooperation increase and the dimensions of tag space become larger, emergent societies can be more tolerant towards heterogeneous others. We also identify the effects of clustering and small-world-ness on the dynamics of tag-based parochial cooperation in spite of its fundamental vulnerability to those deviants regardless of network topology. We discuss the issue of tag changeability in search for alternative societies in which tag-based parochial cooperation is not only efficient but also robust.
Klaus Jaffe and Luis Zaballa
Journal of Artificial Societies and Social Simulation 13 (3) 4
Kyeywords: Altruism, Cooperation, Social, Prosocial, Cohesion, Evolution, Punishment, Retribution
Abstract: Most current attempts to explain the evolution - through individual selection - of pro-social behavior (i.e. behavior that favors the group) that allows for cohesive societies among non related individuals, focus on altruistic punishment as its evolutionary driving force. The main theoretical problem facing this line of research is that in the exercise of altruistic punishment the benefits of punishment are enjoyed collectively while its costs are borne individually. We propose that social cohesion might be achieved by a form of punishment, widely practiced among humans and animals forming bands and engaging in mob beatings, which we call co-operative punishment. This kind of punishment is contingent upon - not independent from - the concurrent participation of other actors. Its costs can be divided among group members in the same way as its benefits are, and it will be favoured by evolution as long as the benefits exceed the costs. We show with computer simulations that co-operative punishment is an evolutionary stable strategy that performs better in evolutionary terms than non-cooperative punishment, and demonstrate the evolvability and sustainability of pro-social behavior in an environment where not necessarily all individuals participate in co-operative punishment. Co-operative punishment together with pro-social behavior produces a self reinforcing system that allows the emergence of a 'Darwinian Leviathan' that strengthens social institutions.
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.
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.
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.
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.
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.
Krzysztof Malarz, Piotr Gronek and Krzysztof Kulakowski
Journal of Artificial Societies and Social Simulation 14 (1) 2
Kyeywords: Mass Opinion; Computer Simulations; Social Networks;
Abstract: Recent formulation of the Zaller model of mass opinion is generalized to include the interaction between agents. The mechanism of interaction is close to the bounded confidence model. The outcome of the simulation is the probability distribution of opinions on a given issue as dependent on the mental capacity of agents. Former result was that a small capacity leads to a strong belief. Here we show that an intensive interaction between agents also leads to a consensus, accepted without doubts.
Christoph Salge and Daniel Polani
Journal of Artificial Societies and Social Simulation 14 (1) 5
Kyeywords: Information Theory, Collective Behaviour, Inadvertent Social Information, Infotaxis, Digested Information, Bayesian Update
Abstract: Within a universal agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents.
Richard Frank, Vahid Dabbaghian, Andrew Reid, Suraj Singh, Jonathan Cinnamon and Patricia Brantingham
Journal of Artificial Societies and Social Simulation 14 (1) 6
Kyeywords: Crime Attractor, Directionality of Crime, Mathematical Modeling, Computational Criminology
Abstract: The spatial distribution of crime has been a long-standing interest in the field of criminology. Research in this area has shown that activity nodes and travel paths are key components that help to define patterns of offending. Little research, however, has considered the influence of activity nodes on the spatial distribution of crimes in crime neutral areas - those where crimes are more haphazardly dispersed. Further, a review of the literature has revealed a lack of research in determining the relative strength of attraction that different types of activity nodes possess based on characteristics of criminal events in their immediate surrounds. In this paper we use offenders' home locations and the locations of their crimes to define directional and distance parameters. Using these parameters we apply mathematical structures to define rules by which different models may behave to investigate the influence of activity nodes on the spatial distribution of crimes in crime neutral areas. The findings suggest an increasing likelihood of crime as a function of geometric angle and distance from an offender's home location to the site of the criminal event. Implications of the results are discussed.
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.
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.
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.
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.
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.
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.
Martin Neumann, Andreas Braun, Eva-Maria Heinke, Mehdi Saqalli and Armano Srbljinovic
Journal of Artificial Societies and Social Simulation 14 (3) 9
Kyeywords: Social Conflicts, Conflict Models, Modelling Challenges, Polysemy, Rationality, Emotions
Abstract: This discussion paper originates from the preceding annual workshop of the Special Interest Group on Social Conflict and Social Simulation (SIG-SCSS) of the ESSA. The workshop especially focused on the need to identify and examine challenges to modeling social conflicts. It turned out that the polysemous nature of social conflicts makes it very difficult to get a grasp of their complexity. In order to deal with this complexity, various dimensions have to be taken into consideration, beginning with the question of how to identify a conflict in the first place. Other dimensions include the relation of conflict and rationality and how to include non-rational factors into conflict models. This involves a conception of organized action. Finally, guiding principles for model development are being discussed. We would like to invite readers of the Journal of Artificial Societies and Social Simulation to 'sow the seeds' of this debate.
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.
Wolfgang Balzer and Klaus Manhart
Journal of Artificial Societies and Social Simulation 14 (4) 11
Kyeywords: Social Simulation, Process, Science, Theory, Social Science, Philosophy of Science
Abstract: We lay open a position concerning the difference between scientific processes and processes in science. Not all processes in science are scientific. This leads into the center of social simulation. More scientific theories should be incorporated in social simulations, and this should lead to more united structural approaches.
Bruce Edmonds, Nigel Gilbert, Petra Ahrweiler and Andrea Scharnhorst
Journal of Artificial Societies and Social Simulation 14 (4) 14
Kyeywords: Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes
Abstract: Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.
Hang Ye, Fei Tan, Mei Ding, Yongmin Jia and Yefeng Chen
Journal of Artificial Societies and Social Simulation 14 (4) 20
Kyeywords: Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment
Abstract: An important way to maintain human cooperation is punishing defection. However, since punishment is costly, how can it arise and evolve given that individuals who contribute but do not punish fare better than the punishers? This leads to a violation of causality, since the evolution of punishment is prior to the one of cooperation behaviour in evolutionary dynamics. Our public goods game computer simulations based on generalized Moran Process, show that, if there exists a \'behaviour-based sympathy\' that compensates those who punish at a personal cost, the way for the emergence and establishment of punishing behaviour is paved. In this way, the causality violation dissipates. Among humans sympathy can be expressed in many ways such as care, praise, solace, ethical support, admiration, and sometimes even adoration; in our computer simulations, we use a small amount of transfer payment to express \'behaviour-based sympathy\'. Our conclusions indicate that, there exists co-evolution of sympathy, punishment and cooperation. According to classical philosophy literature, sympathy is a key factor in morality and justice is embodied by punishment; in modern societies, both the moral norms and the judicial system, the representations of sympathy and punishment, play an essential role in stable social cooperation.
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.
Flaminio Squazzoni and Károly Takács
Journal of Artificial Societies and Social Simulation 14 (4) 3
Kyeywords: Peer Review, Social Simulation, Social Norms, Selection Biases, Science Policy
Abstract: This article suggests to view peer review as a social interaction problem and shows reasons for social simulators to investigate it. Although essential for science, peer review is largely understudied and current attempts to reform it are not supported by scientific evidence. We suggest that there is room for social simulation to fill this gap by spotlighting social mechanisms behind peer review at the microscope and understanding their implications for the science system. In particular, social simulation could help to understand why voluntary peer review works at all, explore the relevance of social sanctions and reputational motives to increase the commitment of agents involved, cast light on the economic cost of this institution for the science system and understand the influence of signals and social networks in determining biases in the reviewing process. Finally, social simulation could help to test policy scenarios to maximise the efficacy and efficiency of various peer review schemes under specific circumstances and for everyone involved.
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.
Journal of Artificial Societies and Social Simulation 14 (4) 7
Kyeywords: Philosophy, Science, Simulation, Social Processes, Evolutionary Models, Sociology
Abstract: This briefly reviews some philosophy of science that might be relevant to simulating the social processes of science. It also includes a couple of examples from the sociology of science because these are inextricable from the philosophy.
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.
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.
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.
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.
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.
Journal of Artificial Societies and Social Simulation 15 (1) 7
Kyeywords: R&D Subsidies, Rivalry Versus Cooperation, Dynamic-Stochastic Games, Simulations
Abstract: By means of a simulated funding-agency/supported-firm stochastic dynamic game, this paper shows that the level of the subsidy provided by a funding (public) agency, normally used to correct for firm R&D shortage, might be severely underprovided. This is due to the "externalities" generated by the agency-firm strategic relationship, as showed by comparing two versions of the model: one assuming "rival" behaviors between companies and agency (i.e., the current setting), and one associated to the "cooperative" strategy (i.e. the optimal Pareto-efficient benchmark). The paper looks also at what "welfare" implications are associated to different degrees of persistency in the funding effect on corporate R&D. Three main conclusions are thus drawn: (i) the relative quota of the subsidy to R&D is undersized in the rival compared to the cooperative model; (ii) the rivalry strategy generates distortions that favor the agency compared to firms; (iii) when passing from less persistent to more persistent R&D additionality/crowding-out effect, the lower the distortion the greater the variance is and vice versa. As for the management of R&D funding policies, we suggest that all the elements favouring greater collaboration between agency and firm objectives may help current R&D support to approach its social optimum.
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.
Mercedes Bleda and Simon Shackley
Journal of Artificial Societies and Social Simulation 15 (2) 2
Kyeywords: Risk Perceptions, Cultural Theory, Simulation Modeling, BSE
Abstract: This paper presents a computer based simulation model which analyses the dynamics of public perceptions of risk using Bovine Spongiform Encephalopathy (BSE) ('mad cow disease') in the UK as a case study. The model is based upon a theoretically-derived understanding of the concept of perception of risk, and employs Cultural Theory and the archetypes it identifies as distinctive forms of social organization and cultural bias in the formation of perceptions. Cultural Theory is used as a theoretical lens for understanding the different interpretations of the risk associated with BSE/nvCJD, the subsequent risk amplification by the media, and the effect of trust and reliance in science and government in their construction. The analysis helps achieve a better understanding of the dynamics of public perceptions of risk, and it is therefore of interest both for academics and policy makers. In particular, the model allows exploring the influence that the occurrence of risk-related events, their media coverage, and trust in government responses has in the process by which people construct their risk perceptions.
Journal of Artificial Societies and Social Simulation 15 (3) 1
Kyeywords: Philosophy of Social Science, Causal Explanation, Functional Explanation, Mechanism Explanation, Analytic Sociology
Abstract: What kind of knowledge can we obtain from agent-based models? The claim that they help us to study the social world needs unpacking. I will defend agent-based modelling against a recent criticism that undermines its potential as a method to investigate underlying mechanisms and provide explanations of social phenomena. I show that the criticism is unwarranted and the problem can be resolved with an account of explanation that is associated with the social sciences anyway, the mechanism account of explanation developed in Machamer et al. (2000). I finish off discussing the mechanism account with relation to prediction in agent-based modelling.
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.
Marco A. Janssen and Nathan Rollins
Journal of Artificial Societies and Social Simulation 15 (3) 5
Kyeywords: Pattern-Oriented Modeling, Competition, Calibration, Empirical Data, Behavioral Experiments
Abstract: This paper reports the results of the inaugural modeling competition sponsored by the Network for Computational SocioEcological Sciences (CoMSES Network). Competition participants were provided with a dataset collected from human-subjects experiments and were asked to develop an agent-based model that replicated behavioral patterns reflected in the data with the goal of using the model to predict behavioral changes in a slightly modified experimental treatment. The data were collected in a resource foraging experiment in which human subjects moved avatars on a computer screen to harvest tokens in a common pool resource. In the original experiments, on which the competition participants based their models, the subjects possessed full information about the state of the resource and the actions of the other group members sharing the resource. The competition challenged participants to predict what would happen if the experimental subjects had limited vision. Using only the data from the original experiment, participants had to design a model that would predict the behavioral changes that would be observed in the new experiment treatment. We compared the models on their assumptions about speed, direction, and harvesting decisions agents make. All the submitted models underestimated the amount of resources harvested. The best performing model was the simplest model submitted and had the best fit with the original dataset provided.
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.
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.
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.
Paul Smaldino, Cynthia Pickett, Jeffrey Sherman and Jeffrey Schank
Journal of Artificial Societies and Social Simulation 15 (4) 7
Kyeywords: Optimal Distinctiveness, ODT, Group Size, Social Cognition, Spatial Models
Abstract: According to optimal distinctiveness theory (ODT; Brewer 1991), individuals prefer social groups that are relatively distinct compared to other groups in the individuals' social environment. Distinctive groups (i.e., groups of moderate relative size) are deemed "optimal" because they allow for feelings of inclusion and social connection while simultaneously providing a basis for differentiating the self from others. However, ODT is a theory about individual preferences and, as such, does not address the important question of what types of groups are actually formed as a function of these individual-level preferences for groups of a certain size. The goal of the current project was to address this gap and provide insight into how the nature of the social environment (e.g., the size of the social neighborhood) interacts with individual-level group size preferences to shape group formation. To do so, we developed an agent-based model in which agents adopted a social group based on an optimal group size preference (e.g., a group whose size represented 20% of the social neighborhood). We show that the assumptions of optimal distinctiveness theory do not lead to individually satisfactory outcomes when all individuals share the same social environment. We were able to produce results similar to those predicted by ODT when social neighborhoods were local and overlapping. These results suggest that the effectiveness of a social identity decision strategy is highly dependent on sociospatial structure.
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.
Amir Hossein Shirazi, Ali Namaki, Amir Ahmad Roohi and Gholam Reza Jafari
Journal of Artificial Societies and Social Simulation 16 (1) 1
Kyeywords: Social and Economical Networks, Information Transparency, Super-Nodes and Monopolies
Abstract: A power law degree distribution is displayed in many complex networks. However, in most real social and economic networks, deviation from power-law behavior is observed. Such networks also have giant hubs far from the tail of the power law distribution. We propose a model based on information 'transparency' (i.e. how much information is visible to others), which can explain the power structure in societies with non-transparency in information delivery. The emergence of very high degree nodes is explained as a direct result of censorship. Based on these assumptions, we define four distinct transparency regions: perfectly non-transparent, low transparent, perfectly transparent regions and regions where information is exaggerated. We observe the emergence of some very high degree nodes in low transparency networks. We show that the low transparency networks are more vulnerable to attack and the controllability of low transparent networks is more difficult than for the others. Also, the low transparency networks have a smaller mean path length and higher clustering coefficients than the other regions.
Flaminio Squazzoni and Niccolò Casnici
Journal of Artificial Societies and Social Simulation 16 (1) 10
Kyeywords: JASSS, Social Simulation, Bibliometric Analysis, Impact, Inter-Journal Citations
Abstract: This paper examines the bibliometric impact of JASSS on other ISI- and Scopus-indexed sources by examining inward and outward citations and their inter-relation. Given the prestige of JASSS, this analysis can measure the growth and dynamics of social simulation and give us an indication of the direction in which social simulation is moving. Results show that the impact of JASSS is higher in computer sciences, physics and ecology than it is in the social sciences, even though JASSS-indexed articles tend to be more concerned with social science-related topics. Looking at inter-journal citations revealed an interesting citation structure: JASSS collected its largest percentage of citations from non-social science-focused journals while directing more citations within its own articles toward works published in social science journals. On the one hand, this would confirm that social simulation is not yet recognised in the social science mainstream. On the other hand, this may indicate that the cross-disciplinary nature of JASSS allows it to promulgate social science theories and findings in other distant communities.
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.
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.
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.
Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 16 (1) 4
Kyeywords: Cooperation, Evolution, Green Beard, Social Parasitism, Chromodynamics
Abstract: Cooperation is essential for complex biological and social systems and explaining its evolutionary origins remains a central question in several disciplines. Tag systems are a class of models demonstrating the evolution of cooperation between selfish replicators. A number of previous models have been presented but they have not been widely explored. Though previous researchers have concentrated on the effects of one or several parameters of tag models, exploring exactly what is meant by cheating in a tag system has received little attention. Here we re-implement three previous models of tag-mediated altruism and introduce four definitions of cheaters. Previous models have used what we consider weaker versions of cheaters that may not exploit cooperators to the degree possible, or to the degree observed in natural systems. We find that the level of altruism that evolves in a population is highly contingent on how cheaters are defined. In particular when cheaters are defined as agents that display an appropriate tag but have no mechanism for participating in altruistic acts themselves, a population is quickly invaded by cheaters and all altruism collapses. Even in the intermediate case where cheaters may revert back to a tag-tolerance mode of interaction, only minimal levels of altruism evolve. Our results suggest that models of tag-mediated altruism using stronger types of cheaters may require additional mechanisms, such as punishment strategies or multi-level selection, to evolve meaningful levels of altruism.
Marc Spraragen, Peter Landwehr, Balakrishnan Ranganathan, Michael Zyda, Kathleen Carley, Yu-Han Chang and Rajiv Maheswaran
Journal of Artificial Societies and Social Simulation 16 (1) 9
Kyeywords: Social, Behavioral, Modeling, Game, Multiplayer
Abstract: Massively Multiplayer Online Games (MMOGs), in their aspect as online communities, represent an exciting opportunity for studying social and behavioral models. For that purpose we have developed Cosmopolis, an MMOG designed to appeal to a wide variety of player types, and containing several key research-oriented features. The course of development has revealed several challenges in integrating behavioral models with an MMOG test bed. However, the Human Social, Cultural, and Behavioral (HSCB) research value of Cosmopolis has been demonstrated with a number of prototype studies, and based on these studies and challenges we propose an ongoing experimental plan largely driven by collaboration with HSCB researchers.
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.
Philippe Giabbanelli and Rik Crutzen
Journal of Artificial Societies and Social Simulation 16 (2) 10
Kyeywords: Conceptual Exploration, Drinking Motives, Social Influence
Abstract: Binge drinking is a complex social problem linked to an array of detrimental health effects. While binge drinking in youth has been analyzed extensively using traditional methods (e.g., regressions analyses), the adult population has received less attention, and recent work has exemplified the potential for simulations to help scholars and practitioners better understand the problem. In this paper, we used agent-based social network models to test a number of hypotheses on important aspects of binge drinking in a sample representative of the adult Dutch population. In particular, we found that a combination of simple social rules (choosing peers who are similar, being prompted to drink if at least a fraction of them drinks, and incorporating the context) was sufficient to correctly predict the behaviour of half of the binge drinkers and 4 out of 5 non binge drinkers. Furthermore, we used factorial analyses to examine the contribution and combination of hypotheses in predicting the behaviour of individuals, with results indicating that who we interact with may not matter so much as how we interact. Finally, we evaluated the potential for interventions that mediate interactions between people in order to reduce the prevalence of binge drinking and found that the impact of such interventions was non linear: moderate interventions would yield benefits, but stronger interventions may only be of limited further benefit.
Alessio Emanuele Biondo, Alessandro Pluchino and Andrea Rapisarda
Journal of Artificial Societies and Social Simulation 16 (2) 11
Kyeywords: Brain Drain, Return Migration, Human Capital, Social Capital
Abstract: The Brain Drain phenomenon is particularly heterogeneous and is characterized by peculiar specifications. It influences the economic fundamentals of both the country of origin and the host one in terms of human capital accumulation. Here, the brain drain is considered from a microeconomic perspective: more precisely we focus on the individual rational decision to return, referring it to the social capital owned by the worker. The presented model compares utility levels to justify agent's migration conduct and to simulate several scenarios within a computational environment. In particular, we developed a simulation framework based on two fundamental individual features, i.e. risk aversion and initial expectation, which characterize the dynamics of different agents according to the evolution of their social contacts. Our main result is that, according to the value of risk aversion and initial expectation, the probability of return migration depends on their ratio, with a certain degree of approximation: when risk aversion is much bigger than the initial expectation, the probability of returns is maximal, while, in the opposite case, the probability for the agents to remain abroad is very high. In between, when the two values are comparable, it does exist a broad intertwined region where it is very difficult to draw any analytical forecast.
Journal of Artificial Societies and Social Simulation 16 (2) 6
Kyeywords: Evolution of Cooperation, Complex Network, Spatial Game, Conditional Cooperation
Abstract: The investigation of how cooperation is achieved on graphs in the field of spatial game or network reciprocity has received proliferating attention in the biological and sociological literature. In line of the research, this paper provides an new account of how cooperation could evolve in complex networks when actors use information of network characteristics to strategize whether to cooperate or not. Different from past work that focuses exclusively on the evolution of unconditional cooperation, we are proposing new strategies that are choosy in whom to cooperate with, conditional on the structural attributes of the nodes occupied by actors. In a series of evolutionary tournaments conducted by computer simulation, the model shows that a pair of simple strategies-cooperating respectively with higher and lower nodal-attribute neighbors-can be advantageous in adaptive fitness when competing against unconditional cooperation and defection. In particular, these strategies of conditional cooperation work well in random graphs-a network known for being unfavorable to the selection of cooperation. This paper contributes to the literature by showing how network characteristics can serve as a mechanism to sustain cooperation in some hostile network environments where unconditional cooperation is unable to evolve. The cognitive foundations of the mechanism and its implications are discussed.
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.
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.
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.
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.
Andrzej Nowak, Agnieszka Rychwalska and Wojciech Borkowski
Journal of Artificial Societies and Social Simulation 16 (3) 12
Kyeywords: Computer Simulations, Mental Models, Benefits of Simulations, Recommendations for Modeling
Abstract: Computer simulations, one of the most powerful tools of science, have many uses. This paper concentrates on the benefits to the social science researcher. Based on our, somewhat paradoxical experiences we had when working with computer simulations, we argue that the main benefit for the researchers who work with computer simulations is to develop a mental model of the abstract process they are simulating. The development of a mental model results in a deeper understating of the process and in the capacity to predict both the behavior of the system and its reaction to changes of control parameters and interventions. By internalizing computer simulations as a mental model, however, the researcher also internalizes the limitations of the simulation. Limitations of the computer simulation may translate into unconscious constrains in thinking when using the mental model. This perspective offers new recommendations for the development of computer simulations and highlights the importance of visualization. The recommendations are different from the recommendations for developing efficient and fast running simulations; for example, to visualize the dynamics of the process it may be better for the program to run slowly.
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.
Piter Dykstra, Corinna Elsenbroich, Wander Jager, Gerard Renardel de Lavalette and Rineke Verbrugge
Journal of Artificial Societies and Social Simulation 16 (3) 4
Kyeywords: Dialogical Logic, Opinion Dynamics, Social Networks
Abstract: We present DIAL, a model of group dynamics and opinion dynamics. It features dialogues, in which agents gamble about reputation points. Intra-group radicalisation of opinions appears to be an emergent phenomenon. We position this model within the theoretical literature on opinion dynamics and social influence. Moreover, we investigate the effect of argumentation on group structure by simulation experiments. We compare runs of the model with varying influence of the outcome of debates on the reputation of the agents.
Sabine Zinn, Jan Himmelspach, Adelinde M. Uhrmacher and Jutta Gampe
Journal of Artificial Societies and Social Simulation 16 (3) 5
Kyeywords: Continuous-Time Microsimulation, Framework, Plug-In, Demography, Modeling, Simulation
Abstract: Often new modeling and simulation software is developed from scratch with no or only little reuse. The benefits that can be gained from developing a modeling and simulation environment by using (and thus reusing components of) a general modeling and simulation framework refer to reliability and efficiency of the developed software, which eventually contributes to the quality of simulation experiments. Developing the tool Mic-Core which supports continuous-time micro modeling and simulation in demography based on the plug-in-based modeling and simulation framework JAMES II will illuminate some of these benefits of reuse. Thereby, we will focus on the development process itself and on the quality of simulation studies, e.g., by analyzing the impact of random number generators on the reliability of results and of event queues on efficiency. The "lessons learned" summary presents a couple of insights gained by using a general purpose framework for M&S as a base to create a specialized M&S software.
Mark Abdollahian, Zining Yang and Hal Nelson
Journal of Artificial Societies and Social Simulation 16 (3) 6
Kyeywords: Infrastructure Siting, Policy Informatics, Computational Economics, Community Based Organizations, Citizen Participation, Game Theory
Abstract: Technical, environment, social, economic and political constraints are critical barriers to the development of new renewable energy supplies. SEMPro is an agent-based, predictive analytics model of energy siting policy in the techno-social space that simulates how competing interests shape siting outcomes to identify beneficial policy for sustainable energy infrastructure. Using a high voltage transmission line as a case study, we integrate project engineering and institutional factors with GIS data on land use attributes and US Census residential demographics. We focus on modeling citizen attitudinal, Community Based Organization (CBO) emergence and behavioral diffusion of support and opposition with Bilateral Shapley Values from cooperative game theory. We also simulate the competitive policy process and interaction between citizens, CBOs and regulatory, utility and governmental stakeholders using non-cooperative game theory. We find CBO formation, utility message and NGO messaging have a positive impact on citizen comments submitted as a part of the Environmental Impact Statement process, while project need and procedure have a negative impact. As citizens communicate and exchange political opinions across greater distances with more neighbors, less CBOs form but those that do are more effective, increasing the number of messages citizens send.
Max Hartshorn, Artem Kaznatcheev and Thomas Shultz
Journal of Artificial Societies and Social Simulation 16 (3) 7
Kyeywords: Ethnocentrism, Evolution of Cooperation, Evolutionary Game Theory, Minimal Cognition, Prisoner's Dilemma
Abstract: Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.
Journal of Artificial Societies and Social Simulation 16 (3) 9
Kyeywords: Social Networks, Cultural Transmission, Spatial Analysis, Network Structure, Network Properties, Archaeology
Abstract: Space plays an important role in the transfer of information in most societies that archaeologists study. Social networks that mediate learning and the transmission of cultural information are situated in spatial environments. This paper uses an abstract agent-based model to represent the transmission of the value of a single "stylistic" variable among groups linked together within a social network, the spatial structure of which is varied using a few simple parameters. The properties of the networks are shown to clearly affect both the overall amount of variability that is produced by the cultural transmission process and the spatial organization of that variability. The relationships between network structure, network properties, and assemblage variability in this simple model are patterned and predictable. This suggests that changes in the spatial structure of social networks may have important implications for interpreting patterns of artifact variability in large-scale archaeological assemblages.
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.
Dietmar Heinke, Gregory Carslaw and Julie Christian
Journal of Artificial Societies and Social Simulation 16 (4) 10
Kyeywords: Destigmatization, Intergroup Contact, Social Psychology
Abstract: In this paper, we propose a novel approach to exploring the destimgatisation process, an agent-based model called the destigmatization model (DSIM). In the DSIM, we demonstrate that, even if individual interactions (intergroup contact) are based on rules of the self-fulfilling prophecy, it can lead to destigmatisation. In a second study, we empirically verify a prediction that there is a positive relationship between minority group size and perceived stigmatization. Finally, we confirm that DSIM successfully implements Allport's (1954) four moderators, in turn decreasing the level of perceived stigma of the minority group. Interestingly, however, some of Allport's moderators influence the speed of destigmatisation, rather than having a lasting impact on the process of prejudice reduction. The findings suggest that moderators of 'intergroup contact' can function in one of two ways, either by improving how much contact helps to reduce stigmatization or by improving how quickly destigmatization can occur.
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.
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.
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.
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.
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.
Christophe Sibertin-Blanc, Pascal Roggero, Françoise Adreit, Bertrand Baldet, Paul Chapron, Joseph El-Gemayel, Matthias Mailliard and Sandra Sandri
Journal of Artificial Societies and Social Simulation 16 (4) 8
Kyeywords: Organization Modeling, Meta-Model, Sociology of Organizations, Cooperative Behavior, Power Relationships
Abstract: This paper is a comprehensive presentation of a framework for the modeling, the simulation and the analysis of power relationships in social organizations, and more generally in systems of organized action. This framework relies on, and slightly extends, the Crozier and Freidberg's sociology of organized action, which supports a methodology for understanding why, in an organizational context, people behave as they do. SocLab intends to complement the discursive statement of sociological analyses with a formal formulation easing the objectivization of findings. It consists of a meta-model of organizations, a model of bounded-rational social actors and analytical tools for the study of the internal properties of organizations.
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.
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.
Journal of Artificial Societies and Social Simulation 17 (1) 17
Kyeywords: Social Practice, Consumption
Abstract: Changing consumer behaviour is key to reducing the environmental effects of industrialised societies. Social practice theories provide an integrated approach to understanding consumer behaviour. The mechanisms underlying the emergence and diffusion of social practices are however until now poorly understood. This paper presents a conceptual framework and an abstract agent-based simulation model for generating social practices which use and extend approaches from social practice theories. The main results are twofold. First, the simulation model is able to generate social practices, what confirms that the conceptual framework captures relevant elements and processes. Second, a new mechanism for behavioural lock-in is identified that provides additional insights into the widely acknowledged challenge of changing social practices and respective consumption.
Birnur Özbaş, Onur Özgün and Yaman Barlas
Journal of Artificial Societies and Social Simulation 17 (1) 19
Kyeywords: Real Estate Modeling, Housing Cycles, Price Oscillations, System Dynamics, Socio-Economic Simulation
Abstract: The purpose of this study is to model and analyze by simulation the dynamics of endogenously created oscillations in real estate (housing) prices. A system dynamics simulation model is built to understand some of the structural sources of cycles in the key housing market variables, from the perspective of construction companies. The model focuses on the economic balance dynamics between supply and demand. Because of the unavoidable delays in the perception of the real estate market conditions and construction of new buildings, prices and related market variables exhibit strong oscillations. Two policies are tested to reduce the oscillations: decreasing the construction time, and taking into account the houses under construction in starting new projects. Both policies yield significantly reduced oscillations, more stable behaviors.
Rory Sie, Peter B. Sloep and Marlies Bitter-Rijpkema
Journal of Artificial Societies and Social Simulation 17 (1) 3
Kyeywords: Coalition Formation, Networked Innovation, Creativity, Simulation of Social Networks, Social Behaviour, Complex Networks
Abstract: The present article uses agent-based social simulation to study rational behaviour in networked innovation. A simulation model that includes network characteristics and network participant’s characteristics is run using parameter sweeping, yielding 1450 simulation cases. The notion of coalitions was used to denote partnerships in networked innovation. Coalitions compete against each other and several variables were observed for winning coalitions. Close analysis of the variations and their influence on the average power per winning coalition was analysed using stepwise multiple regression analysis. The analysis brought forward two main conclusions. First, as average betweenness centrality per winning coalition increases, the average power per winning coalition decreases. This implies that having high betweenness centrality as a network participant makes it easier to build a successful coalition, as a coalition needs lower average power to succeed. Second, as the number of network participants increases, the average power per winning coalition decreases. This implies that in a larger network, it may be easier to form a successful coalition. The results form the basis for the development of a utility-based recommendation system that helps people choose optimal partners in an innovation network.
Francisco J. León-Medina, Francisco José Miguel Quesada and Vanessa Alcaide Lozano
Journal of Artificial Societies and Social Simulation 17 (1) 4
Kyeywords: Public Goods, Collective Behaviour, Decision Making, Social Networks
Abstract: This paper presents a multi-agent simulation of the production of step-level public goods in social networks. In previous public goods experimental research the design of the sequence ordering of decisions have been limited because of the necessity of simplicity taking priority over realism, which means they never accurately reproduce the social structure that constrains the available information. Multi-agent simulation can help us to overcome this limitation. In our model, agents are placed in 230 different networks and each networks’ success rates are analyzed. We find that some network attributes -density and global degree centrality and heterogeneity-, some initial parameters of the strategic situation -the provision point- and some agents’ attributes -beliefs about the probability that others will cooperate-, all have a significant impact on the success rate. Our paper is the first approach to an explanation for the scalar variant of production of public goods in a network using computational simulation methodology, and it outlines three main findings. (1) A less demanding collective effort level does not entail more success: the effort should neither be as high as to discourage others, nor so low as to be let to others. (2) More informed individuals do not always produce a better social outcome: a certain degree of ignorance about other agents’ previous decisions and their probability of cooperating are socially useful as long as it can lead to contributions that would not have occurred otherwise. (3) Dense horizontal groups are more likely to succeed in the production of step-level public goods: social ties provide information about the relevance of each agent’s individual contribution. This simulation demonstrates the explanatory power of the structural properties of a social system because agents with the same decision algorithm produce different outcomes depending on the properties of their social network.
Pierre Bommel, Francisco Dieguez, Danilo Bartaburu, Emilio Duarte, Esteban Montes, Marcelo Pereira Machín, Jorge Corral, Carlos José Pereira de Lucena and Hermes Morales Grosskopf
Journal of Artificial Societies and Social Simulation 17 (1) 6
Kyeywords: Participative Modeling, Collaborative Modeling, Executable UML, Activity Diagram Interpretation, Rangeland Management, Livestock
Abstract: This paper focuses on the collective design and immediate execution of an agent-based model (ABM) by dynamically interpreting the activity diagrams of agent behaviours. To reach this objective, we have implemented an ABM of livestock producers facing drought conditions in Uruguay. The first step consists in implementing a standard ABM with pasture growth, herd dynamics and simple agents roughly imitating farmers’ strategies. The second step is more participative since it consists in assessing the model with the real cattle farmers. As with most modelling processes, this evaluation phase requires feedback on model design. In order to make this assessment more lively and efficient, we have conceived a tool for drawing diagrams that can be immediately interpreted by the agents. Thanks to this new editor, the actors have quickly understood how the model worked and were able to criticize and modify it. Thus, this innovative modelling tool enables the involvement of stakeholders in co-designing ABM for participatory foresight studies. We hope it will facilitate the emergence of new and more efficient practices for farm management that can account for climate changes.
Robert Aguirre and Timothy Nyerges
Journal of Artificial Societies and Social Simulation 17 (1) 7
Kyeywords: Social Actors, Public Participation, Decision Making, Sustainability Management, Geodesign, Geographic Information Systems (GIS)
Abstract: This article reports on an agent-based simulation of public participation in decision making about sustainability management. Agents were modeled as socially intelligent actors who communicate using a system of symbols. The goal of the simulation was for agents to reach consensus about which situations in their regional environment to change and which ones not to change as part of a geodesign process for improving water quality in the greater Puget Sound region. As opposed to studying self-organizing behavior at the scale of a local “commons,” our interest was in how online technology supports the self-organizing behavior of agents distributed over a wide regional area, like a watershed or river basin. Geographically-distributed agents interacted through an online platform similar to that used in online field experiments with actual human subjects. We used a factorial research design to vary three interdependent factors each with three different levels. The three factors included 1) the social and geographic distribution of agents (local, regional, international levels), 2) abundance of agents (low, medium, high levels), and 3) diversity of preconceptions (blank slate, clone, social actor levels). We expected that increasing the social and geographic distribution of agents and the diversity of their preconceptions would have a significant impact on agent consensus about which situations to change and which ones not to change. However, our expectations were not met by our findings, which we trace all the way back to our conceptual model and a theoretical gap in sustainability science. The theory of self-organizing resource users does not specify how a group of social actors’ preconceptions about a situation is interdependent with their social and geographic orientation to that situation. We discuss the results of the experiment and conclude with prospects for research on the social and geographic dimensions of self-organizing behavior in social-ecological systems spanning wide regional areas.
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.
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.
Jiongming Su, Baohong Liu, Qi Li and Hongxu Ma
Journal of Artificial Societies and Social Simulation 17 (2) 4
Kyeywords: Opinion Dynamics, Directed Adaptive Networks, Social Group, Coevolving Networks
Abstract: In the interactions of a social group, people usually update and express their opinions through the observational learning behaviors. The formed directed networks are adaptive which are influenced by the evolution of opinions; while in turn modify the dynamic process of opinions. We extend the Hegselmann-Krause (HK) model to investigate the coevolution of opinions and observational networks (directed Erdös-Rényi network). Directed links can be broken with a probability if the difference of two opinions exceeds a certain confidence level ε, but new links can form randomly. Simulation results reveal that both the static networks and adaptive networks have three types: more than one cluster (fragmented) with small ε, consensus with a certain probability with moderate ε, always consensus with large ε. Also, on both networks, the tendencies of average of opinion clusters, consensus probability and average of convergence rounds are similar, and the fewest of average of opinion clusters satisfies the rough 1/(2 ε)-rule. On static networks, final opinions are influenced by percolation properties of networks; but on directed adaptive networks, it is basically determined by the rewiring probability, which increases the average degree of networks. When rewired probability is larger than zero, the results of adaptive networks are getting better than static networks. However, after the final average in- and out-degree of both networks exceeds a threshold, there is little improvement on the results.
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.
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.
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.
Nils Schuhmacher, Laura Ballato and Paul van Geert
Journal of Artificial Societies and Social Simulation 17 (3) 1
Kyeywords: Risk Behavior in Adolescence, Dynamic Systems, Friendship Formation, Peer Homogeneity, Behavioral Change
Abstract: Adolescents tend to adopt behaviors that are similar to those of their friends, and also tend to become friends with peers that have similar interests and behaviors. This tendency towards homogeneity applies not only to conventional behaviors such as working for school and participating in sports activities, but also to risk behaviors such as drug use, oppositional behavior or unsafe sex. The current study aims at building an agent model to answer the following related questions: how do friendship groups evolve and what is the role of behavioral similarity in friendship formation? How does homogeneity among peers emerge, with regard to conventional as well as risk behaviors? On the basis of the theoretical and empirical literature on friendship selection and influences on risk behavior during adolescence we first developed a conceptual framework, which was then translated into a mathematical model of a dynamic system and implemented as an agent-based computer simulation consisting of simple behavioral rules and principles. Each agent in the model holds distinct property matrices including an individual behavioral profile with a list of risky (i.e., alcohol use, aggressiveness, soft drugs) and conventional behaviors (i.e., school attendance, sports, work). The computer model simulates the development, during one school year, of a social network (i.e., formation of friendships and cliques), the (dyadic) interactions between pupils and their behavioral profiles. During the course of simulation, the agents’ behavioral profiles change on the basis of their interactions resulting in individual developmental curves of conventional and risk behaviors. These profiles are used to calculate the (behavioral) similarity and differences between the various agents. Generally, the model output is analyzed by means of visual inspection (i.e., plotting developmental curves of behavior and social networks), systematic comparison and by calculating additional measures (i.e., using specific social analysis software packages). Simulation results conclusively indicate model validity. The model simulates qualitative properties currently found in research on adolescent development, namely the role of homophily, the appearance of friendship clusters, and the increase in behavioral homogeneity among friends. The model not only converges with empirical findings, but furthermore helps to explain social psychological phenomena (e.g., the emergence of homophily among adolescents).
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.
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.
Adam Wierzbicki, Paulina Adamska, Katarzyna Abramczuk, Thanasis Papaioannou, Karl Aberer and Emilia Rejmund
Journal of Artificial Societies and Social Simulation 17 (3) 6
Kyeywords: Credibility, Reputation, Game Theory, Incentives, Online Communities
Abstract: The Internet has become an important source of information that significantly affects social, economical and political life. The content available in the Web is the basis for the operation of the digital economy. Moreover, Web content has become essential for many Web users that have to make decisions. Meanwhile, more and more often we encounter Web content of low credibility due to incorrect opinions, lack of knowledge, and, even worse, manipulation attempts for the benefit of the authors or content providers. While mechanisms for the support of credibility evaluation in practice have been proposed, little is known about their effectiveness, and about their influence on the global picture of Web content production and consumption. We use a game-theoretic model to analyze the impact of reputation on the evaluation of content credibility by consumers with varying expertise, in the presence of producers who have incentives to manipulate information.
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.
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.
Moira Zellner, Cristy Watkins, Dean Massey, Lynne Westphal, Jeremy Brooks and Kristen Ross
Journal of Artificial Societies and Social Simulation 17 (4) 11
Kyeywords: Collective Decision-Making, Ethnographic Data, Ecological Restoration, Empirical Modeling
Abstract: Ecological restoration actions generally result from collective decision-making processes and can involve diverse, at times contentious, views. As such, it is critical to understand these processes and the factors that might influence the resolution of diverse perspectives into a set of coordinated actions. This paper describes the adaptation and calibration of a stylized collective decision-making agent-based model using ethnographic data, to advance theory on how decisions emerge in the context of ecological restoration in the Chicago Wilderness. The prototypical model provided structure and organization of the empirical data of two Chicago Wilderness member groups and revealed organizational structures, patterns of interactions via formal and informal meetings, and parameter values for the various mechanisms. The organization of the data allowed us to identify where our original model mechanisms required adaptation. After model modifications were completed, baseline scenarios were contrasted with observations for final parameter calibration and to elaborate explanations of the study cases. This exercise allowed us to identify the components and mechanisms in the system to which the outputs are most sensitive. We constructed relevant hypothetical scenarios around these critical components, and found that key liaisons, agents with high interaction frequencies and high mutual respect values are useful in promoting efficient decision processes but are limited in their ability to change the collective position with respect to a restoration practice. Simulations suggest that final collective position can be changed when there is a more equitable distribution of agents across groups, or the key liaison is very persuasive (i.e. interacts frequently and is highly respected) but is non-reciprocal (i.e. does not respect others highly). Our work advances our understanding of key mechanisms influencing collective decision processes and illustrates the value of agent-based modeling and its integration with ethnographic data analysis to advance the theory of collective decision making.
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.
Rodrigo Castro and Pablo Jacovkis
Journal of Artificial Societies and Social Simulation 18 (1) 13
Kyeywords: Global Models, Social Processes, Complex Systems, History of Science, Computer Simulation, Latin American Modeling
Abstract: During the 1960s but mainly in the 1970s, large mathematical dynamic global models were implemented in computers to simulate the entire world, or large portions of it. Several different but interrelated subjects were considered simultaneously, and their variables evolved over time in an attempt to forecast the future, considering decades as time horizons. Global models continued to be developed while evidencing an increasing bias towards environmental aspects, or at least the public impact of models with such a focus became prevalent. In this paper we analyze the early evolution of computer-based global modeling and provide insights on less known pioneering works by South American modelers in the 1960s (Varsavsky and collaborators). We revisit relevant methodological aspects and discuss how they influenced different modeling endeavors. Finally, we overview how distinctive systemic approaches in global modeling evolved into the currently well-established discipline of complex systems.
Corinna Elsenbroich and Jennifer Badham
Journal of Artificial Societies and Social Simulation 18 (1) 16
Kyeywords: Gilbert Number, Social Contagion
Abstract: This article analyses a series of emails thanking Nigel for his stewardship of JASSS and the characteristics of their authors. It identifies a correlation between two measures of author activity in social simulation research, but no pattern between these activity measures and the email timing. Instead, the sequence suggests a classic standing ovation effect.
Sukaina Bharwani, Mònica Coll Besa, Richard Taylor, Michael Fischer, Tahia Devisscher and Chrislain Kenfack
Journal of Artificial Societies and Social Simulation 18 (1) 3
Kyeywords: Knowledge Elicitation, Decision-Making, Climate Adaptation, Verification and Validation, Social Simulation, Tacit Knowledge
Abstract: This paper describes a participatory and collaborative process for formalising qualitative data, using research from southeast Cameroon, how these results can provide input to an social simulation model, and what insights they can provide in better understanding decision-making in the region. Knowledge Elicitation Tools (KnETs) have been used to support a body of existing research on local strategies that build community adaptive capacity and support sustainable forest management under a range of socio-environmental and climatic stressors. The output of this approach is a set of decision rules which complements previous analysis of differentiated vulnerability of forest communities. Improvements to the KnETs methodology, such as new statistical measurements, make it easier to generate inputs for a social simulation model, such as agent attributes and heterogeneity, as well as informing which scenarios to prioritise during model development and testing. The KnETs process served as a vehicle to structure a large volume of empirical data, to identify the most salient drivers of decision-making amongst different actors, to uncover tacit knowledge and to make recommendations about which strategic interventions should be further explored in a social simulation and by local organizations planning interventions. It was notable that there were many common rule drivers for men and women from the same households, though they participated in the game-interviews separately. At the same time, though strategies were common to both poor and better-off farmers, differences lay in the package of strategies chosen – the number and type of strategies as well the drivers factors – and how they were prioritised with respect to each farmer’s goal.
Cara H. Kahl and Hans Hansen
Journal of Artificial Societies and Social Simulation 18 (1) 4
Kyeywords: Creativity, Social Psychology, Mihaly Csikszentmihalyi, Social Systems, Cultural Evolution, Information Theory
Abstract: Psychological research on human creativity focuses primarily on individual creative performance. Assessing creative performance is, however, also a matter of expert evaluation. Few psychological studies model this aspect explicitly as a human process, let alone measure creativity longitudinally. An agent-based model was built to explore the effects contextual factors such as evaluation and temporality have on creativity. Mihaly Csikszentmihalyi’s systems perspective of creativity is used as the model’s framework, and stylized facts from the domain of creativity research in psychology provide the model’s contents. Theoretical experimentation with the model indicated evaluators and their selection criteria play a bearing role in constructing human creativity. This insight has major implications for designing future creativity research in psychology.
Mike Farjam, Marco Faillo, Ida Sprinkhuizen-Kuyper and Pim Haselager
Journal of Artificial Societies and Social Simulation 18 (1) 5
Kyeywords: Public Goods Games, Punishment, Cooperation, Reciprocity, Evolution of Cooperation
Abstract: In social dilemmas punishment costs resources, not just from the one who is punished but often also from the punisher and society. Reciprocity on the other side is known to lead to cooperation without the costs of punishment. The questions at hand are whether punishment brings advantages besides its costs, and how its negative side-effects can be reduced to a minimum in an environment populated by agents adopting a form of reciprocity. Various punishment mechanisms have been studied in the economic literature such as unrestricted punishment, legitimate punishment, cooperative punishment, and the hired gun mechanism. In this study all these mechanisms are implemented in a simulation where agents can share resources and may decide to punish other agents when the other agents do not share. Through evolutionary learning agents adapt their sharing/punishing policy. When the availability of resources was restricted, punishment mechanisms in general performed better than no-punishment, although unrestricted punishment was performing worse. When resource availability was high, performance was better in no-punishment conditions with indirect reciprocity. Unrestricted punishment was always the worst performing mechanism. Summarized, this paper shows that, in certain environments, some punishment mechanisms can improve the efficiency of cooperation even if the cooperating system is already based on indirect reciprocity.
Matthew Jarman, Andrzej Nowak, Wojciech Borkowski, David Serfass, Alexander Wong and Robin Vallacher
Journal of Artificial Societies and Social Simulation 18 (1) 6
Kyeywords: Cellular Automata, Social Influence, Opinion Dynamics
Abstract: To maintain stability yet retain the flexibility to adapt to changing circumstances, social systems must strike a balance between the maintenance of a shared reality and the survival of minority opinion. A computational model is presented that investigates the interplay of two basic, oppositional social processes—conformity and anticonformity—in promoting the emergence of this balance. Computer simulations employing a cellular automata platform tested hypotheses concerning the survival of minority opinion and the maintenance of system stability for different proportions of anticonformity. Results revealed that a relatively small proportion of anticonformists facilitated the survival of a minority opinion held by a larger number of conformists who would otherwise succumb to pressures for social consensus. Beyond a critical threshold, however, increased proportions of anticonformists undermined social stability. Understanding the adaptive benefits of balanced oppositional forces has implications for optimal functioning in psychological and social processes in general.
William Rand, Jeffrey Herrmann, Brandon Schein and Neža Vodopivec
Journal of Artificial Societies and Social Simulation 18 (2) 1
Kyeywords: Urgent Diffusion, Diffusion of Information, News, Social Networks, Twitter
Abstract: During a crisis, understanding the diffusion of information throughout a population will provide insights into how quickly the population will react to the information, which can help those who need to respond to the event. The advent of social media has resulted in this information spreading quicker then ever before, and in qualitatively different ways, since people no longer need to be in face-to-face contact or even know each other to pass on information in an crisis situation. Social media also provides a wealth of data about this information diffusion since much of the communication happening within this platform is publicly viewable. This data trove provides researchers with unique information that can be examined and modeled in order to understand urgent diffusion. A robust model of urgent diffusion on social media would be useful to any stakeholders who are interested in responding to a crisis situation. In this paper, we present two models, grounded in social theory, that provide insight into urgent diffusion dynamics on social networks using agent-based modeling. We then explore data collected from Twitter during four major urgent diffusion events including: (1) the capture of Osama Bin Laden, (2) Hurricane Irene, (3) Hurricane Sandy, and (4) Election Night 2012. We illustrate the diffusion of information during these events using network visualization techniques, showing that there appear to be differences. After that, we fit the agent-based models to the observed empirical data. The results show that the models fit qualitatively similarly, but the diffusion patterns of these events are indeed quite different from each other.
Tommaso Venturini, Pablo Jensen and Bruno Latour
Journal of Artificial Societies and Social Simulation 18 (2) 11
Kyeywords: Simulations, Big Data, Social Science, Micro Macro, Science Policy, Modeling
Abstract: In the last few years, electronic media brought a revolution in the traceability of social phenomena. As particles in a bubble chamber, social trajectories leave digital trails that can be analyzed to gain a deeper understanding of collective life. To make sense of these traces a renewed collaboration between social and natural scientists is needed. In this paper, we claim that current research strategies based on micro-macro models are unfit to unfold the complexity of collective existence and that the priority should instead be the development of new formal tools to exploit the richness of digital data.
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.
Alexis Kirke and Eduardo Miranda
Journal of Artificial Societies and Social Simulation 18 (2) 16
Kyeywords: Social Networks, Music, Emotion
Abstract: In this article a multi-agent system is presented which generates melody pitch sequences with a hierarchical structure. The agents have no explicit melodic intelligence and generate the pitches as a result of artificial emotional influence and communication between agents, and the melody’s hierarchical structure is a result of the emerging agent social structure. The system is not a mapping from multi-agent interaction onto musical features, but actually utilizes music for the agents to communicate artificial emotions. Each agent in the society learns its own growing tune during the interaction process. Experiments are presented demonstrating that diverse and non-trivial melodies can be generated, as well as a hierarchical musical 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.
Daniel Villatoro, Giulia Andrighetto, Rosaria Conte and Jordi Sabater-Mir
Journal of Artificial Societies and Social Simulation 18 (2) 2
Kyeywords: Self-Organisation, Norms, Emergent Behavior, Cognitive Modelling, Artificial Social Systems
Abstract: In the seminal work "An Evolutionary Approach to Norms", Axelrod identified internalization as one of the key mechanisms that supports the spreading and stabilization of norms. But how does this process work? This paper advocates a rich cognitive model of different types, degrees and factors of norm internalization. Rather than a none-or-all phenomenon, we claim that norm internalization is a dynamic process, whose deepest step occurs when norms are complied with thoughtlessly. In order to implement a theoretical model of internalization and check its effectiveness in sustaining social norms and promoting cooperation, a simulated web-service distributed market has been designed, where both services and agents' tasks are dynamically assigned. Internalizers are compared with agents whose behaviour is driven only by self-interested motivations. Simulation findings show that in dynamic unpredictable scenarios, internalizers prove more adaptive and achieve higher level of cooperation than agents whose decision-making is based only on utility calculation.
Giulio Cimini and Angel Sanchez
Journal of Artificial Societies and Social Simulation 18 (2) 22
Kyeywords: Evolutionary Game Theory, Prisoner's Dilemma, Network Reciprocity
Abstract: Cooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far—as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner’s dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors’ payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players’ strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors’ payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people’s behavior.
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.
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.
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.
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.
Thomas Moore, Patrick Finley, Nancy Brodsky, Theresa Brown, Benjamin Apelberg, Bridget Ambrose and Robert Glass
Journal of Artificial Societies and Social Simulation 18 (2) 7
Kyeywords: Opinion Dynamics, Social Networks, Media, Advertising
Abstract: We present a modified Deffuant-Weisbuch opinion dynamics model that integrates the influence of media campaigns on opinion. Media campaigns promote messages intended to inform and influence the opinions of the targeted audiences through factual and emotional appeals. Media campaigns take many forms: brand-specific advertisements, promotions, and sponsorships, political, religious, or social messages, and public health and educational communications. We illustrate model-based analysis of campaigns using tobacco advertising and public health education as examples. In this example, “opinion” is not just an individual’s attitude towards smoking, but the integration of a wide range of factors that influence the likelihood that an individual will decide to smoke, such as knowledge, perceived risk, perceived utility and affective evaluations of smoking. This model captures the ability of a media campaign to cause a shift in network-level average opinion, and the inability of a media message to do so if it promotes too extreme a viewpoint for a given target audience. Multiple runs displayed strong heterogeneity in response to media campaigns as the difference between network average initial opinion and broadcasted media opinion increased, with some networks responding ideally and others being largely unaffected. In addition, we show that networks that display community structure can be made more susceptible to be influenced by a media campaign by a complementary campaign focused on increasing tolerance to other opinions in targeted nodes with high betweenness centrality. Similarly, networks can be “inoculated” against advertising campaigns by a media campaign that decreases tolerance.
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.
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.
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.
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.
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.
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.
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.
Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 18 (3) 2
Kyeywords: Tags, Thresholds, Altruism, Evolution, Cooperation, Social Harmonics
Abstract: Several parameters combine to govern the nature of agent interactions in evolutionary social simulations. Previous work has suggested that these parameters may have complex interplay that is obscured when they are not analyzed separately. Here we focus specifically on how three population-level parameters, which govern agent interactions, affect levels of altruism in a population. Specifically we vary how frequently agents interact in a generation, how far along a network they may interact, and the size of the population. We show that the frequency with which an agent interacts with its neighbors during a generation has a strong effect on levels of evolved altruism – provided that those pairings are stochastic. When agents interact equally with all of their neighbors, regardless of how often, minimal levels of altruism evolve. We further report a curious harmonic signature in the level of altruism resulting from the interplay of the benefit-cost ratio of an altruistic act and the number of agent interactions per generation. While the level of altruism is generally an increasing function of the number of pairings per generation, at each instance where pairings equals a multiple of the benefit-cost ratio a sharp discontinuity occurs, precipitating a drop onto a lower-value function. We explore the nature of these discontinuities by examining the temporal dynamics and spatial configuration of agents. Finally, we show that rules for the evolution of cooperation that are based on network density may be inadvertently missing effects that are due to the frequency of interactions and whether those interactions are symmetrical among neighbors.
Ben Fitzpatrick, Jason Martinez, Elizabeth Polidan and Ekaterini Angelis
Journal of Artificial Societies and Social Simulation 18 (3) 4
Kyeywords: Group Formation, Peer Influence, Identity Control Theory, Social Norms, College Drinking
Abstract: College drinking is a problem with severe academic, health, and safety consequences. The underlying social processes that lead to increased drinking activity are not well understood. Social Norms Theory is an approach to analysis and intervention based on the notion that students’ misperceptions about the drinking culture on campus lead to increases in alcohol use. In this paper we develop an agent-based simulation model, implemented in MATLAB, to examine college drinking. Students’ drinking behaviors are governed by their identity (and how others perceive it) as well as peer influences, as they interact in small groups over the course of a drinking event. Our simulation results provide some insight into the potential effectiveness of interventions such as social norms marketing campaigns.
Shu-Heng Chen, Bin-Tzong Chie and Tong Zhang
Journal of Artificial Societies and Social Simulation 18 (3) 5
Kyeywords: Trust Game, Network Game, Multiplier, Clique, Stochastic Choice, Myopic Trust, Relative Reciprocity
Abstract: By hybridizing two kinds of games frequently used in experimental economics, namely, trust games and network games, this paper develops a model of the network-based trust game. Through agent-based simulation of the model, we can demonstrate the positive effects of trust on growth. Even though the underlying technology still provides the fundamental channel for growth, there is an indirect effect on growth through network formation. It is in this network formation process that trust plays a role. The trust considered in this paper is a kind of myopic trust which, through the stochastic choice model, can affect agents' decisions regarding networking, portfolios, and kickbacks, which in turn affects network formation, wealth creation, and distribution.
Xiaolin Hu and Nicholas Keller
Journal of Artificial Societies and Social Simulation 18 (3) 6
Kyeywords: Child Maltreatment, Child Maltreatment Prevention, Social Ecology
Abstract: This paper presents an agent-based model that simulates the dynamics of child maltreatment and child maltreatment prevention. The developed model follows the principles of complex systems science and explicitly models a community and its families with multi-level factors across the social ecology. Each agent includes behavioral/cognitive modeling to account for the behavioral/cognitive process of child maltreatment. Simulation of child maltreatment prevention is also supported to evaluate the impacts of different prevention/intervention strategies. We describe the model and show experiment results to evaluate and demonstrate the agent-based model.
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.
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.
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.
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.
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.
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.
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.
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.
L. Andrew Bollinger, Martti J. van Blijswijk, Gerard P.J. Dijkema and Igor Nikolic
Journal of Artificial Societies and Social Simulation 19 (1) 1
Kyeywords: Socio-Technical Systems, Electricity Systems, Modelling Tools, Social Simulation, Netlogo, Matpower
Abstract: The growing importance of links between the social and technical dimensions of the electricity infrastructure mean that many research problems cannot be effectively addressed without joint consideration of social and technical dynamics. This paper motivates the need for and introduces a tool to facilitate the development of linked social and technical models of electric power systems. The tool, called MatpowerConnect, enables the runtime linkage of Netlogo - an oft-used modelling platform in the social simulation domain - with Matpower - a common power flow simulation package in the power systems domain. MatpowerConnect opens up new modelling possibilities for social simulation researchers active in the study of electricity systems. It offers ease of use coupled with a high degree of realism with which electricity infrastructure functionality is captured. We describe the development and use of two demonstration models using MatpowerConnect. These models illustrate two types of problems and system scales that can be addressed. In the first model we explore the consequences of actors' adaptive strategies on the performance of a small-scale power system. In the second model we simulate the effects of different regulatory regimes on network investment in a supra-national electricity transmission system to explore the long-term consequences for network development and social welfare. In both cases, the extension enables capturing a critical functionality of electric power systems, while allowing model development efforts to focus on social simulation aspects. Resources for using the extension are provided in conjunction with this paper.
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.
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
Hannah Übler and Stephan Hartmann
Journal of Artificial Societies and Social Simulation 19 (1) 2
Kyeywords: ABM, Norms, Social Influence
Abstract: We present a study of the spreading of trends in artificial social influence networks using agent based models. We concentrate on basic properties of the agents which describe their individual attitudes towards a trend, as well as the influence which they exert in their social neighbourhood. Using a simple random network, we investigate the impact of network dynamicity, situations of opposing trends, and the disappearance of trends. A 'community' network is used to study the impact of group cohesiveness and connectors for the spreading of trends in social communities.
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.
Eric Pulick, Patrick Korth, Patrick Grim and Jiin Jung
Journal of Artificial Societies and Social Simulation 19 (2) 1
Kyeywords: Polarization, Media, Opinion, Social Networks, Town Meetings, Reinforcement
Abstract: We are increasingly exposed to polarized media sources, with clear evidence that individuals choose those sources closest to their existing views. We also have a tradition of open face-to-face group discussion in town meetings, for example. There are a range of current proposals to revive the role of group meetings in democratic decision-making. Here, we build a simulation that instantiates aspects of reinforcement theory in a model of competing social influences. What can we expect in the interaction of polarized media with group interaction along the lines of town meetings? Some surprises are evident from a computational model that includes both. Deliberative group discussion can be expected to produce opinion convergence. That convergence may not, however, be a cure for extreme views polarized at opposite ends of the opinion spectrum. In a large class of cases, we show that adding the influence of group meetings in an environment of self-selected media produces not a moderate central consensus but opinion convergence at one of the extremes defined by polarized media.
Tanzhe Tang and Hang Ye
Journal of Artificial Societies and Social Simulation 19 (2) 2
Kyeywords: Altruistic Punishment, Mating Preference, Sexual Attractiveness, Social Dilemma
Abstract: Current simulation practices in artificial societies typically ignore the contribution of sexuality as a driving force for the evolution of prosocial behaviours. As recent researches in biology and genetics argued, sexual attractiveness, via the method of sexual selection, can explain many aspects of the second-order social dilemma. The basic hypothesis is that altruism is a sexually attractive virtue. To introduce the hypothesis into the analysis of human altruism, we employ the concepts of altruistic punishment and the behaviour-based sexual attractiveness to develop a gender-based evolutionary model where mating preference acts as the compensation to the male punishers from females in the given public goods game. In the model, the force of sexual selection is expressed as the effect of mating preference on altruism. The computer simulation indicates that social cohesion can be achieved by the existence of sexuality in an artificial society where the co-evolution of mating preference, altruistic punishment and cooperation exist. We then extend the model in two ways: (1) we employ the variable size population assumption to test the invasion capacity of cooperators, and (2) individual variation in altruistic investment is introduced to replace the average population payoff function in the baseline model. The variable size population and individual variation in investment are found to have amplifying effects on the evolution of altruism from different perspectives. Finally, we discuss the definition of altruism in dynamic evolutionary games, as well as the gender differences in the formation of altruism in primitive tribes.
Journal of Artificial Societies and Social Simulation 19 (2) 5
Kyeywords: Well-Being, Conspicuous Consumption, Overvaluation, Tradeoffs, Satisfaction, Social Capital
Abstract: This paper presents a semi-quantitative mathematical model of the changes over time in the statistical distribution of well-being of individuals in a society. The model predicts that when individuals overvalue the more socially conspicuous aspects of well-being in their lifestyle choices, then the average well-being of the overall population may experience continuous decline. In addition to tradeoff cost and overvaluation, we identify statistical variation in individuals’ well-being and turnover within the population as key factors driving negative trends. We investigate the influence of the effects of heterogeneity in the population, as well as economic and/or technological progress.
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.
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.
Flávia Pereira dos Santos, Diana Adamatti, Henrique Rodrigues, Glenda Dimuro, Esteban De Manuel Jerez and Graçaliz Dimuro
Journal of Artificial Societies and Social Simulation 19 (3) 12
Kyeywords: Urban Ecosystem, Social Organization Simulation, Simulation of Social Production and Management Processes, Regulatory Policy Simulation, Multiagent-Based Simulations, JaCaMo Framework
Abstract: The concept of social production and management of urban ecosystems may be understood as the generation of new physical or relational situations, by constructing, transforming or eliminating physical and/or relational objects or ensuring the fulfillment of their social and environmental functions. This includes the citizen participation in the process of urban planning and transformation, forming a network structured and supported by tools allowing the equal distribution of power in the decision making. The SJVG-MAS Project addresses, in an interdisciplinary approach, the development of computational tools based on Multiagent Systems (MAS) for the simulation of the social production and management processes that occur in urban ecosystems, in particular, the San Jerónimo Vegetable Garden project (Spain). In this paper, we present a MAS-based simulation tool developed in JaCaMo. We conceived a 5-dimensional BDI-like agent social system composed of the agents' population, the social organization, the environment, the interactional/communication and the regulatory structures.
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.
Christopher Poile and Frank Safayeni
Journal of Artificial Societies and Social Simulation 19 (3) 8
Kyeywords: Computational Modeling, Simulation, Theory-Building, Equifinality
Abstract: Computational modeling is a powerful method for building theory. However, to construct a computational model, researchers need to operationalize their cognitive or verbal theory into the specific terms demanded by the simulation’s language. This requires the researcher to make a series of reasonable assumptions to fill unanticipated “specificity gaps.” The problem is that many other reasonable assumptions could also have been made, and many of those resulting models would also match the conceptual theory. This is the problem of equifinality. We demonstrate the power and the dangers of computational modeling by building a simulation of a classic small group study. The results demonstrate that reasonable assumptions and equifinality are straightforward (but often overlooked) problems at the core of genuinely useful methodology. We offer recommendations and hope to open a dialog on other perspectives and solutions.
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.
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.
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.
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.
Kei-Leo Brousmiche, Jean-Daniel Kant, Nicolas Sabouret and François Prenot-Guinard
Journal of Artificial Societies and Social Simulation 19 (4) 2
Kyeywords: Social Simulation, Attitude Formation, Cognitive Modeling, Calibration Using Field Data
Abstract: Attitude is a key concept in social psychology. The paper presents a novel agent-based model to simulate attitude formation by combining a rational and an emotional components based on cognitive, psychological and social theories. Individuals of the artificial population perceive actions taken by actors such as government or brands, they form an attitude toward them and also communicate the events through a social network. The model outputs are first studied through a functional analysis in which some unique macroscopic behaviors have emerged such as the impact of social groups, the resistance of the population toward disinformation campaigns or the social pressure. We then applied our model on a real world scenario depicting the effort of French Forces in their stabilization operations in Kapisa (Afghanistan) between 2010 and 2012. We calibrated the model parameters based on this scenario and the results of opinion polls that were conducted in the area during the same period about the sentiment of the population toward the Forces. Our model was able to reproduce polls results with a global error under 3%. Based on these results, we show the different dynamics tendencies that emerged among the population by applying a non-supervised classification algorithm.
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.
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.
Corinna Elsenbroich and Jennifer Badham
Journal of Artificial Societies and Social Simulation 19 (4) 8
Kyeywords: Extortion Racketeering, Game Theory, Social Dynamics
Abstract: Systematic extortion involves a long term parasitic relationship between the criminal and the victim. Game theory analysis has provided insight into the choices of individual hypothetical criminal and victim pairs. In this paper we present an agent-based model so as to extend the analysis to the relationship between extorters and other potential victims. The model is developed in two stages, the first to be closest to game theory, the second one making the decision informed by the social environment of the victim. The agent-based model shows the importance of social aspects for the functioning of extortion rackets.
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.
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.
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.
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.
Jonas Hauke, Iris Lorscheid and Matthias Meyer
Journal of Artificial Societies and Social Simulation 20 (1) 5
Kyeywords: Social Simulation, Lines of Research, Multidisciplinary, Citation Analysis, Co-Citation Analysis
Abstract: The research field of social simulation comprises many topics and research directions. A previous study about the early years indicated that the community has evolved into a differentiated discipline. This paper investigates the recent development of social simulation as reflected in Journal of Artificial Societies and Social Simulation (JASSS) publications from 2008 to 2014. By using citation analysis, we identify the most influential publications and study the characteristics of citations. Additionally, we analyze the development of the field with respect to research topics and their structure in a co-citation analysis. The citation characteristics support the continuing highly multidisciplinary character of JASSS. Prominently cited are methodological papers and books, standards, and NetLogo as the main simulation tool. With respect to the focus of this research, we observe continuity in topics such as opinion dynamics and the evolution of cooperation. While some topics disappeared such as learning, new subjects emerged such as marriage formation models and tools and platforms. Overall, one can observe a maturing inter- and multidisciplinary scientific community in which both methodological issues and specific social science topics are discussed and standards have emerged.
Julio B. Clempner
Journal of Artificial Societies and Social Simulation 20 (2) 12
Kyeywords: Machiavellianism, Stackelberg/Nash Game, Machine Ethics, Moral, Markov Chains, Behavioral Games
Abstract: This paper presents a new game theory approach for modeling manipulation behavior based on Machiavellianism (social conduct and intelligence theory). The Machiavellian game conceptualizes the Machiavellianism considering three concepts: views, tactics and immorality. For modeling the Machiavellian views and tactics we employ a Stackelberg/Nash game theory approach. For representing the concept of immorality, we consider that rational Machiavellian players employ a combination of the deontological and utilitarian moral rules, as well as, moral heuristics. We employ a reinforcement learning approach for the implementation of the immorality concept providing a computational mechanism, in which, its principle of error-driven adjustment of cost/reward predictions contributes to the players' acquisition of moral (immoral) behavior. The reinforcement learning algorithm is based on an actor-critic approach responsible for evaluating the new state of the system and it determines if the cost/rewards are better or worse than expected, supported by the Machiavellian game theory solution. The result of the model is the manipulation equilibrium point. We provide the details needed to implement the extraproximal method in an efficient and numerically stable way. Finally, we present a numerical example that validates the effectiveness of the manipulation model.
Peng Shao and Ping Hu
Journal of Artificial Societies and Social Simulation 20 (2) 2
Kyeywords: Advance Selling, Product Diffusion, Social Network, Complex Network
Abstract: This study analyzes the diffusion of two product types using an advance selling strategy from a social network perspective. We extended the susceptible-infected-removed (SIR) model by adding a buyer component (SIRB) to the model and conducted an in-depth analysis of transmission probability and purchase probability when using an advance selling strategy. Agent-based simulation indicates that cost reduction and promotional effort have positive effects on profits, while lead time negatively affects them. Statistical analyses indicate that lead time has a U-shaped relationship with profits for non-durable products, but an inverted U-shaped relationship with those for durable products. For both products types, promotional effort has an inverted U-shaped relationship with profits under the condition of low-quality products and an inverted U-shaped relationship in the case of high-quality products. The reasons underlying these results are discussed, followed by implications for firms adopting advance selling strategies.
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.
Thomas Feliciani, Andreas Flache and Jochem Tolsma
Journal of Artificial Societies and Social Simulation 20 (2) 6
Kyeywords: Opinion Dynamics, Polarization, Social Influence, Segregation
Abstract: Increasing ethnic diversity fosters scholarly interest in how the spatial segregation of groups affects opinion polarization in a society. Despite much empirical and theoretical research, there is little consensus in the literature on the causal link between the spatial segregation of two groups and the emergence of opinion polarization. We contribute to the debate by investigating theoretically the conditions under which the former fosters or hinders the latter. We focus on two processes of opinion polarization (negative influence and persuasive argument communication) that, according to previous modeling work, can be expected to make conflicting predictions about the relationship between segregation and opinion polarization. With a Schelling-type agent-based model of residential segregation, we generate initial environments with different levels of group segregation. Then we simulate the two processes of opinion dynamics. We show that the negative influence model predicts segregation to hinder the emergence of opinion polarization. On the other hand, the persuasive argument model predicts that segregation does not substantially foster polarization. Moreover, we explore how the spatial patterns of opinion distribution differ between the models: in particular, we investigate the likelihood that group membership and opinion align. We show that the alignment of group membership and opinions differs between the two opinion formation models, and that the scale at which we measure alignment plays a crucial role.
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.
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.
Ivan Puga-Gonzalez and Cedric Sueur
Journal of Artificial Societies and Social Simulation 20 (3) 10
Kyeywords: Individual-Based Model, Friendships, Social Networks, Grooming, Aggression, Macaque Societies
Abstract: The individual-based model GrooFiWorld proposes a parsimonious theory explaining the complex behavior of macaque societies. It suggests that the socio-spatial structure of the group underlies the emergence of complex behaviour. A spatial structure with dominants at the center and subordinates at the periphery emerges due to aggression. This structure influences the distribution of social interactions: individuals interact more with close-by partners and thus several behavioural patterns emerge. In GrooFiWorld, however, individuals have no preferential interactions; whereas in primates, individuals prefer interactions with ‘friends’. The distribution of interactions, then, may be influenced by ‘friendships’ rather than spatial structure. To study this, here, we omitted space from the model and investigated the effects of ‘friendships’ on the emergence of social behaviour and network structure. Results show that ‘friendships’ promote cooperation but fail to produce other patterns characteristic of macaques. This highlights the importance that spatial structure may have in structuring macaque societies.
Nicholas M. Gotts and Gary Polhill
Journal of Artificial Societies and Social Simulation 20 (3) 11
Kyeywords: Energy-Use, Goal-Framing, Social-Networks, Values
Abstract: The CEDSS-3.4 agent-based model of domestic energy demand at community level is described. CEDSS (Community Energy Demand Social Simulator) is focused on household decisions (the model’s agents are households) to buy energy-using appliances, heating systems, and insulation, over the period from 2000 to 2049. Its empirical basis is a survey of households in Aberdeen and Aberdeenshire, Scotland, carried out in 2010, combined with publicly available data on household finances and equipment, and energy prices. CEDSS-3.4 emphasises mechanisms concerning value-strength dynamics and goal selection which influence such decisions, drawing on goal-framing theory. Results of experiments with the model are presented; the most important parameters for determining energy demand turn out to be economic (rates of change of incomes and of fuel prices), and the presence or absence of external (extra-community) influences on value-strengths. However, the value-strength dynamics used led in most runs to a single set of values dominating the population by 2049 – but even with identical parameters, different sets of values could become dominant, and which did so made a very considerable difference to demand. This resulted in bimodal distributions of outcome measures across the runs using a given parameter-setting in many cases; initial experiments indicated that changing parameters determining how far households influence each others’ values could at least reduce this tendency. Issues in the analysis of complex models with aspects unconstrained by either data or theory are discussed in the final section.
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.
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.
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.
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.
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.
Xin Sun, Xishun Zhao and Livio Robaldo
Journal of Artificial Societies and Social Simulation 20 (3) 6
Kyeywords: Convention, Game Theory, Imitate-The-Best, Social Network
Abstract: In this paper we propose a model that supports the emergence of conventions via multiagent learning in social networks. In our model, individual agents repeatedly interact with their neighbours in a game called Ali Baba and the Thief. An agent learns its strategy to play the game using the learning rule imitate-the-best. We show that some conventions prescribing peaceful behaviours can emerge after repeated interactions among agents inhabited in some social networks. Our experiments suggest that there are critical points of convention emergence in Ali Baba and the Thief. When the quotient of the amount of robbery and the initial utility is smaller than the critical point, the probability of convention emergence is high. The probability drops dramatically as long as the quotient is larger than the critical point.
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.
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.
Andreas Flache, Michael Mäs, Thomas Feliciani, Edmund Chattoe-Brown, Guillaume Deffuant, Sylvie Huet and Jan Lorenz
Journal of Artificial Societies and Social Simulation 20 (4) 2
Kyeywords: Social Influence, Opinion Dynamics, Polarization, Calibration and Validation, Micro-Macro Link
Abstract: In 1997, Robert Axelrod wondered in a highly influential paper "If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?" Axelrod’s question highlighted an ongoing quest for formal theoretical answers joined by researchers from a wide range of disciplines. Numerous models have been developed to understand why and under what conditions diversity in beliefs, attitudes and behavior can co-exist with the fact that very often in interactions, social influence reduces differences between people. Reviewing three prominent approaches, we discuss the theoretical ingredients that researchers added to classic models of social influence as well as their implications. Then, we propose two main frontiers for future research. First, there is urgent need for more theoretical work comparing, relating and integrating alternative models. Second, the field suffers from a strong imbalance between a proliferation of theoretical studies and a dearth of empirical work. More empirical work is needed testing and underpinning micro-level assumptions about social influence as well as macro-level predictions. In conclusion, we discuss major roadblocks that need to be overcome to achieve progress on each frontier. We also propose that a new generation of empirically-based computational social influence models can make unique contributions for understanding key societal challenges, like the possible effects of social media on societal polarization.
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.
Pierpaolo Angelini, Giovanni Cerulli, Federico Cecconi, Maria-Augusta Miceli and Bianca Potì
Journal of Artificial Societies and Social Simulation 20 (4) 4
Kyeywords: R&D Policy, Networks, Complexity, Social Simulation
Abstract: This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support.
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.
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.
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.
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.
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.
Jonathan Köhler, Fjalar de Haan, Georg Holtz, Klaus Kubeczko, Enayat Moallemi, George Papachristos and Émile Chappin
Journal of Artificial Societies and Social Simulation 21 (1) 8
Kyeywords: Transitions Models, Qualitative System Change, Modelling Social Values and Norms, Behavioural Change
Abstract: Transition modelling is an emerging but growing niche within the broader field of sustainability transitions research. The objective of this paper is to explore the characteristics of this niche in relation to a range of existing modelling approaches and literatures with which it shares commonalities or from which it could draw. We distil a number of key aspects we think a transitions model should be able to address, from a broadly acknowledged, empirical list of transition characteristics. We review some of the main strands in modelling of socio-technological change with regards to their ability to address these characteristics. These are: Eco-innovation literatures (energy-economy models and Integrated Assessment Models), evolutionary economics, complex systems models, computational social science simulations using agent based models, system dynamics models and socio-ecological systems models. The modelling approaches reviewed can address many of the features that differentiate sustainability transitions from other socio-economic dynamics or innovations. The most problematic features are the representation of qualitatively different system states and of the normative aspects of change. The comparison provides transition researchers with a starting point for their choice of a modelling approach, whose characteristics should correspond to the characteristics of the research question they face. A promising line of research is to develop innovative models of co-evolution of behaviours and technologies towards sustainability, involving change in the structure of the societal and technical systems.
Samantha Dobbie, Kate Schreckenberg, James G Dyke, Marije Schaafsma and Stefano Balbi
Journal of Artificial Societies and Social Simulation 21 (1) 9
Kyeywords: Social-Ecological Systems, Livelihood Trajectories, Nutrition, Malawi, Food Security
Abstract: We present a methodological approach for constructing an agent-based model (ABM) to assess community food security and variation among livelihood trajectories, using rural Malawi as a case study. The approach integrates both quantitative and qualitative data to explore how interactions between households and the environment lead to the emergence of community food availability, access, utilisation and stability over time. Results suggest that livelihoods based upon either non-agricultural work or farming are most stable over time, but agricultural labourers, dependent upon the availability of casual work, demonstrate limited capacity to ‘step-up’ livelihood activities. The scenario results suggest that population growth and increased rainfall variability are linked to significant declines in food utilisation and stability by 2050. Taking a systems approach may help to enhance the sustainability of livelihoods, target efforts and promote community food security. We discuss transferability of the methodological approach to other case studies and scenarios.
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.
Mathieu Bourgais, Patrick Taillandier, Laurent Vercouter and Carole Adam
Journal of Artificial Societies and Social Simulation 21 (2) 5
Kyeywords: Emotion, Social Simulation, Survey
Abstract: Emotions play a key role in human behavior. Being able to integrate them in models is thus a major issue to improve the believability of agent-based social simulations. However, even though these last years have seen the emergence of many emotional models usable for simulations, many modelers still tend to use simple ad hoc emotional models. To support this view, this article proposes a survey of the different practices of modelers in terms of implementations of emotional models. We then present different emotional architectures that already exist and that can be used by modelers. The main goal is to understand the way emotions are used today in social simulations, in order for the community to unify its uses of emotional agents.
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.
Segismundo S. Izquierdo and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 21 (3) 2
Kyeywords: Social Simulation, Decision Support Systems, Deductive Inference, Fuzzy Logic, Mamdani
Abstract: Fuzzy logic presents many potential applications for modelling and simulation. In particular, this paper analyses one of the most popular fuzzy logic techniques: Mamdani systems. Mamdani systems can look particularly appealing because they are designed to incorporate expert knowledge in the form of IF-THEN rules expressed in natural language. While this is an attractive feature for modelling and simulating social and other complex systems, its actual application presents important caveats. This paper studies the potential use of Mamdani systems to explore the logical consequences of a model based on IF-THEN rules via simulation. We show that in the best-case scenario a Mamdani system provides a function that complies with its generating set of IF-THEN rules, which is a different exercise from that of finding the relation or consequences implied by those rules. In general, the logical consequences of a set of rules cannot be captured by a single function. Furthermore, the consequences of an IF-THEN rule in a Mamdani system can be very different from the consequences of that same rule in a system governed by the most basic principles of logical deductive inference. Thus, care must be taken when applying this tool to study “the consequences” of a set of hypothesis. Previous analyses have typically focused on particular steps of the Mamdani process, while here we present a holistic assessment of this technique for (deductive) simulation purposes.
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.
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.
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.
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.
Ricardo Andrés Guzmán, Sammy Drobny and Carlos Rodríguez-Sickert
Journal of Artificial Societies and Social Simulation 21 (4) 10
Kyeywords: Social Stratification, Agricultural Intensification, Territorial War, Civil Wars, Malthusian Dynamics, Spatial Models
Abstract: We present a spatial agent-based model of the emergence and proliferation of premodern complex societies in an isolated region initially inhabited by simple societies. At the intrasocietal level, the model integrates scalar stress, social fission, sociocultural evolution, societal collapse, and Malthusian-Ricardian demographic dynamics. At the geographical level, the model includes warfare for territory and captives, territorial division due to social conflict, and territorial disintegration due to collapse. We found that a single variable---slow, continuous progress in intensive agriculture---drives the social and geographical dynamics. Consistent with the archaeological and historical record, the model produced three consecutive "eras": During the first era, simple societies dominate the region. They use extensive food production methods. Small complex societies of intensive agriculturists emerge intermittently in the core land, where intensification is feasible. Shortly after, they collapse or are annihilated by local simple societies. During the second era, some complex societies avert early collapse and annihilation. They expand by conquest. At all times, they coexist with simple societies. Some complex societies are destroyed in war; others collapse. From time to time, complex societies collapse en masse. During the third era, there are no more mass collapses. Complex societies slowly expand until they dominate the core land. Simple societies take refuge in the marginal land, where intensification is infeasible. Simple and complex societies coexist, separated by a moving frontier. In an ebb and flow, complex societies expand to the marginal land and withdraw to the core land. The results of the simulations are qualitatively consistent with prehistorical and historical case studies. The model replicates the progression from simple to more complex societies, and explains why that progression happened in fits and starts.
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.
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.
Hang Xiong, Diane Payne and Stephen Kinsella
Journal of Artificial Societies and Social Simulation 21 (4) 6
Kyeywords: Peer Effects, Social Networks, Diffusion of Innovation, High-Value Crop
Abstract: We separately identify two mechanisms underlying peer effects in farm households' adoption of a new crop. A farmer can follow his peers to adopt a new crop because he learns knowledge about the new crop from them (social learning) and because he wants to avoid the damage caused by the practice conflicting with theirs (externalities). Using an agent-based model, we simulate the two mechanisms on a multiplex network consisting of two types of social relationships. The simulation model is estimated using detailed data of social networks, adoption and relevant socio-economic characteristics from 10 villages in China. We find that social learning -- in this case, the sharing of experiential resources -- among family members and production externalities between contiguous land plots both significantly influence farmers' adoption. Furthermore, sharing of experiential resources plays a significant role in the entire diffusion process and dominates the early phase, whereas externalities only matter in the late phase. This study shows the roles peer effects play in shaping diffusion can occur through different mechanisms and can vary as the diffusion proceeds. The work also suggests that agent-based models can help disentangle the role of social interactions in promoting or hindering diffusion.
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.
Azhar Mohd Ibrahim, Ibrahim Venkat and Philippe De Wilde
Journal of Artificial Societies and Social Simulation 22 (1) 3
Kyeywords: Evacuation Model, Evolution of Crowd Behaviour, Crowd Disaster, Evolutionary Game Theory
Abstract: Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game-theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Tanzhe Tang and Caspar G. Chorus
Journal of Artificial Societies and Social Simulation 22 (3) 2
Kyeywords: Opinion Dynamics, Norm Formation, Voter Model, Behavioral Change
Abstract: Opinion dynamics models are based on the implicit assumption that people can observe the opinions of others directly, and update their own opinions based on the observation. This assumption significantly reduces the complexity of the process of learning opinions, but seems to be rather unrealistic. Instead, we argue that the opinion itself is unobservable, and that people attempt to infer the opinions of others by observing and interpreting their actions. Building on the notion of Bayesian learning, we introduce an action-opinion inference model (AOI model); this model describes and predicts opinion dynamics where actions are governed by underlying opinions, and each agent changes her opinion according to her inference of others’ opinions from their actions. We study different action-opinion relations in the framework of the AOI model, and show how opinion dynamics are determined by the relations between opinions and actions. We also show that the well-known voter model can be formulated as being a special case of the AOI model when adopting a bijective action-opinion relation. Furthermore, we show that a so-called inclusive opinion, which is congruent with more than one action (in contrast with an exclusive opinion which is only congruent with one action), plays a special role in the dynamic process of opinion spreading. Specifically, the system containing an inclusive opinion always ends up with a full consensus of an exclusive opinion that is incompatible with the inclusive opinion, or with a mixed state of other opinions, including the inclusive opinion itself. A mathematical solution is given for some simple action-opinion relations to help better understand and interpret the simulation results. Finally, the AOI model is compared with the constrained voter model and the language competition model; several avenues for further research are discussed at the end of the paper.
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.
Gianfranco Giulioni, Edmondo Di Giuseppe, Piero Toscano, Francesco Miglietta and Massimiliano Pasqui
Journal of Artificial Societies and Social Simulation 22 (3) 4
Kyeywords: Wheat International Trade, Wheat Price-Quantity Modeling, Food Security, Wheat Price Volatility, Export Ban
Abstract: In this paper, we build a computational model for the analysis of international wheat spot price formation, its dynamics and the dynamics of quantities traded internationally. The model has been calibrated using FAOSTAT data to evaluate its in-sample predictive power. The model is able to generate wheat prices in twelve international markets and traded wheat quantities in twenty-four world regions. The time span considered is from 1992 to 2013. In our study, particular attention was paid to the impact of the Russian Federation's 2010 grain export ban on wheat price and quantities traded internationally. Among other results, we found that the average weighted world wheat price in 2013 would have been 3.55% lower than the observed one if the Russian Federation had not imposed the export ban in 2010.
Xinglong Qu, Zhigang Cao, Xiaoguang Yang and The Anh Han
Journal of Artificial Societies and Social Simulation 22 (3) 5
Kyeywords: Group Cohesion, Public Goods Game, Cooperation Emergence, Conditional Dissociation, Positive Assortment
Abstract: Leaving is usually an option for individuals if they cannot tolerate their defective partners. In a two-player game, when a player chooses to leave, both she and her opponent become single players. However, in a multi-player game, the same decision may have different consequences depending on whether group cohesion exists. Players who choose not to leave would still be united together rather than be separated into singletons if there is cohesion among them. Considering this difference, we study two leaving mechanisms in public goods games. In the first mechanism, every player would be single once any of the group members leaves. In the second, we assume group cohesion exists that members who don't leave form a union. In our model, each player adopts a trigger strategy characterized by a threshold: she leaves if the number of defectors in her group exceeds the threshold. We find that under both mechanisms, when the expected lifespan of individuals is long enough, cooperators with zero tolerance toward defection succeed in the evolution. Moreover, when cohesion exists in groups, cooperation is better promoted because the cooperators have a higher chance to play together. That is, group cohesion facilitates positive assortment and therefore promotes cooperation.
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.
Márta Radó and Károly Takács
Journal of Artificial Societies and Social Simulation 22 (4) 11
Kyeywords: Deskmates, Academic Performance, Intervention, Social Networks, Prejudice, Acting White
Abstract: Traditional desegregation policies have improved but not fully solved the problems associated with the reproduction of inequalities and interracial prejudice in schools. This is partly because social networks are inherently segregated within integrated schools and the benefits of contact have not fully materialized. Therefore, new kinds of policies are needed to further improve the situation. This paper investigates the consequences and efficiency of seating arrangements on academic outcomes and prejudice using an agent-based model that reflects real-life asymmetries. We model interpersonal dynamics and study behavior in the classroom in the hypothetical case of a single teacher who defines students’ seating arrangements. The model incorporates the mechanisms of peer influence on study behavior, on attitude formation, and homophilous selection in order to depict the interrelated dynamics of networks, behavior, and attitudes. We compare various seating arrangement scenarios and observe how GPA distribution and level of prejudice changes over time. Results highlight the advantages and disadvantages of seating strategies. In general, more heterogeneous deskmate pairs lead to a lower level of inequality and prejudice in the classroom, but this strategy does not favor talent management. Further, we evaluate outcomes compared to the absence of external intervention whereby students choose their own deskmates based on homophilous selection. Our model takes into account the fact that homophilous selection may be distorted due to the ‘Acting White’ phenomenon and pre-existing prejudice. Accounting for these factors implies slower convergence between advantaged and disadvantaged students.
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.
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.
Andrea Scalco, Jennie I. Macdiarmid, Tony Craig, Stephen Whybrow and Graham. W. Horgan
Journal of Artificial Societies and Social Simulation 22 (4) 8
Kyeywords: Consumer Behaviour, Food Choice, Meat Consumption, Population Health, Social Influence
Abstract: The current rate of production and consumption of meat poses a problem both to peoples’ health and to the environment. This work aims to develop a simulation of peoples’ meat consumption in Britain using agent-based modelling. The agents represent individual consumers. The key variables that characterise agents include sex, age, monthly income, perception of the living cost, and concerns about the impact of meat on the environment, health, and animal welfare. A process of peer influence is modelled with respect to the agents’ concerns. Influence spreads across two eating networks (i.e. co-workers and household members) depending on the time of day, day of the week, and agents’ employment status. Data from a representative sample of British consumers is used to empirically ground the model. Different experiments are run simulating interventions of the application of social marketing campaigns and a rise in price of meat. The main outcome is the mean weekly consumption of meat per consumer. A secondary outcome is the likelihood of eating meat. Analyses are run on the overall artificial population and by subgroups. The model succeeded in reproducing observed consumption patterns. Different sizes of effect on consumption emerged depending on the application of a social marketing strategy or a price increase. A price increase had a greater effect than environmental and animal welfare campaigns, while a health campaign had a larger impact on consumers’ behaviour than the other campaigns. An environmental campaign targeted at consumers concerned about the environment produced a boomerang effect increasing the consumption in the population rather than reducing it. The results of the simulation experiments are mainly consistent with the literature on food consumption providing support for future models of public strategies to reduce meat consumption.
Elizabeth A. Stiles, Colin D. Swearingen, Linda Seiter and Brendan Foreman
Journal of Artificial Societies and Social Simulation 23 (1) 1
Kyeywords: Social Network Analysis, Threshold Model, Invisible Primary, Campaigns, Elections, Presidency
Abstract: The invisible primary is an important time in United States Presidential primary politics as candidates gain momentum for their campaigns before they compete formally in the first state caucus (Iowa) and primaries (e.g. New Hampshire). However, this critical period has not been possible to observe, hence the name. By simulating networks of primary followers, we can explicate hypotheses for how messages travel through networks to affect voter preferences. To do so, we use a threshold model to drive our simulated network analysis testing spread of public support for candidates in invisible primaries. We assign voter thresholds for candidates and vary number of voters, attachment to candidates and decay. We also vary social graph structure and model. Results of the algorithm show effects of size of lead, an unwavering base of support, and information loss.
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.
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.
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.
Myong-Hun Chang and Joseph Harrington
Journal of Artificial Societies and Social Simulation 23 (2) 1
Kyeywords: Stigma, Diffusion, Conformity, Compassion, Social Network
Abstract: The dynamics of social stigma are explored in the context of diffusion models. Our focus is on exploring the dynamic process through which the behavior of individuals and the interpersonal relationships among them influence the macro-social attitude towards the stigma. We find that a norm of tolerance is best promoted when the population comprises both those whose conduct is driven by compassion for the stigmatized and those whose focus is on conforming with others in their social networks. A second finding is that less insular social networks encourage de-stigmatization when most people are compassionate, but it is instead more insularity that promotes tolerance when society is dominated by conformity.
J Jumadi, Nick Malleson, Steve Carver and Duncan Quincey
Journal of Artificial Societies and Social Simulation 23 (2) 2
Kyeywords: ABM, Volcanic Crisis, Risk Estimation, Spatio-Temporal Modeling, MCE, Merapi
Abstract: Managing disasters caused by natural events, especially volcanic crises, requires a range of approaches, including risk modelling and analysis. Risk modelling is commonly conducted at the community/regional scale using GIS. However, people and objects move in response to a crisis, so static approaches cannot capture the dynamics of the risk properly, as they do not accommodate objects’ movements within time and space. The emergence of Agent-Based Modelling makes it possible to model the risk at an individual level as it evolves over space and time. We propose a new approach of Spatio-Temporal Dynamics Model of Risk (STDMR) by integrating multi-criteria evaluation (MCE) within a georeferenced agent-based model, using Mt. Merapi, Indonesia, as a case study. The model makes it possible to simulate the spatio-temporal dynamics of those at risk during a volcanic crisis. Importantly, individual vulnerability is heterogeneous and depends on the characteristics of the individuals concerned. The risk for the individuals is dynamic and changes along with the hazard and their location. The model is able to highlight a small number of high-risk spatio-temporal positions where, due to the behaviour of individuals who are evacuating the volcano and the dynamics of the hazard itself, the overall risk in those times and places is extremely high. These outcomes are extremely relevant for the stakeholders, and the work of coupling an ABM, MCE, and dynamic volcanic hazard is both novel and contextually relevant.
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.
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.
Szymon Talaga and Andrzej Nowak
Journal of Artificial Societies and Social Simulation 23 (2) 6
Kyeywords: Social Networks, Homophily, Social Distance Attachment, Configuration Model
Abstract: Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.
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.
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.
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.
Rosemary Pepys, Robert Bowles and Noémie Bouhana
Journal of Artificial Societies and Social Simulation 23 (3) 12
Kyeywords: Radicalisation, Social-Ecological Modelling, State-Transition Modelling, Model Development, Stylized Facts
Abstract: This paper presents a simulation model describing the radicalisation process. The radicalisation process is a complex human socio-environmental process which has been of much academic interest for the past two decades. Despite this it is still poorly understood and is an extremely difficult area for social scientists to research. It is a subject which suffers from a lack of available data, making the construction of an effective simulation model particularly challenging. In order to construct the simulation in this paper we rely on a theoretical framework which was originally developed as a means of synthesising the academic literature on radicalisation. This theoretical framework has three levels: individual vulnerability to radicalisation, exposure to radicalising moral contexts, and the emergence of radicalising settings. We adapt this framework into a simulation model by first re-constructing it as an individual-level state-transition model. Next, appropriate data is sought to parameterise the model. A parallel is drawn between the process of radicalisation and the process by which people develop the propensity to participate in more general acts of criminality; this analogy enables considerably more data to be used in parameterisation. The model is then calibrated by considering the logical differences between crime and terrorism which might lead to differences in the radicalisation and criminality development processes. The model is validated against stylised facts, demonstrating that despite being highly theoretical the simulation is capable of producing a realistic output. Possible uses of the model to evaluate the effectiveness of counter-radicalisation measures are also considered.
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.
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.
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.
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.
Sebastian Fajardo, Gert Jan Hofstede, Martijn de Vries, Mark Kramer and Andrés Bernal
Journal of Artificial Societies and Social Simulation 23 (4) 11
Kyeywords: Human Colonization, Gregarious Behavior, Social Differentiation, Settlement Patterns, Caribbean, Archaeology
Abstract: Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of Old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.