JASSS logo


28 articles matched your search for the keywords:
Research Methodology, Cognition, Motivation, Judgement, Decision Making

Qualitative Modeling and Simulation of Socio-Economic Phenomena

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.

Through the Minds of the Agents

Cristiano Castelfranchi
Journal of Artificial Societies and Social Simulation 1 (1) 5

Kyeywords: Cooperation, Altruism, Rationality, Cognition
Abstract: This is a response to Michael Macy's contribution to the JASSS Forum, Social Order in Artificial Worlds.

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

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

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

Critical Incident Management: an Empirically Derived Computational Model

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

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

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

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

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

From Social Monitoring to Normative Influence

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

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

Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model

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.

Metamimetic Games: Modeling Metadynamics in Social Cognition

David Chavalarias
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.

Simple Heuristics in Complex Networks: Models of Social Influence

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.

Modelling Contextualized Reasoning in Complex Societies with "Endorsements"

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

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

A Model of Political Judgment: An Agent-Based Simulation of Candidate Evaluation

Sung-youn Kim
Journal of Artificial Societies and Social Simulation 14 (2) 3

Kyeywords: Candidate Evaluation, Election, Cognition and Affect, Political Judgment, ACT-r
Abstract: This paper advances Kim, Taber, and Lodge's work (2010). Specifically, it is shown here that the psychological model of political judgment proposed by Kim et al (2010) is consistent with a set of well-known empirical regularities repeatedly found in electoral and psychological researches, that the model in general implies motivated reasoning - discounting contradictory information to the prior while accepting consistent information more or less at its face value - under general conditions, and that (prior) evaluative affect towards candidates plays a fundamental role in this process. It is also discussed the implication of motivated reasoning in accounting for the responsiveness, persistence, and polarization of candidate evaluation often observed in elections.

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

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

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

An Agent-Based Model of Social Identity Dynamics

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.

The Evolutionary Dominance of Ethnocentric Cooperation

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.

The Production of Step-Level Public Goods in Structured Social Networks: An Agent-Based Simulation

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.

An Agent-Based Model of Public Participation in Sustainability Management

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.

How Do Agents Make Decisions? A Survey

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

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

Heads and Hearts: Three Methods for Explicating Judgment and Decision Processes

Warren Thorngate
Journal of Artificial Societies and Social Simulation 18 (1) 14

Kyeywords: Research Methodology, Cognition, Motivation, Judgement, Decision Making
Abstract: Agent-based models are more likely to generate accurate outputs if they incorporate valid representations of human agents than if they don't. The present article outlines three research methodologies commonly used for explicating the cognitive processes and motivational orientations of human judgment and decision making: policy capturing, information seeking, and social choice. Examples are given to demonstrate how each methodology might be employed to supplement more traditional qualitative methods such as interviews and content analyses. Suggestions for encoding results of the three methodologies in agent-based models are also given, as are caveats about methodological practicalities.

Impacts of Farmer Coordination Decisions on Food Supply Chain Structure

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

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

Calibrating with Multiple Criteria: A Demonstration of Dominance

Jennifer Badham, Chipp Jansen, Nigel Shardlow and Thomas French
Journal of Artificial Societies and Social Simulation 20 (2) 11

Kyeywords: Multi-Criteria Decision Making, Calibration, Pattern-Oriented Modelling, Dominance, Behaviour Modelling
Abstract: Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-based model is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. The model fit is assessed by the size and timing of maximum behaviour adoption, as well as the more usual criterion of minimising mean squared error between actual and estimated behaviour. The rigorous approach to calibration supported explicit trading off between these criteria, and ultimately demonstrated that there were significant flaws in the model structure.

Agent-Based Modelling Approach for Multidimensional Opinion Polarization in Collective Behaviour

Jin Li and Renbin Xiao
Journal of Artificial Societies and Social Simulation 20 (2) 4

Kyeywords: Social Computing, Collective Behaviour, Agent-Based Model, Multidimensional Opinion Polarization, Social Judgement Theory, Multi-Agent System
Abstract: Opinion polarization in a group is an important phenomenon in collective behaviour that has become increasingly frequent during periods of social transition. In general, an opinion includes several dimensions in reality. By combining social judgement theory with the multi-agent model, we propose a multidimensional opinion evolution model for studying the dynamics of opinion polarization. Compared with previous models, a major contribution is that the opinion of the agent is extended to multiple dimensions, and the BA network is used as a model of real social networks. The results demonstrate that polarization is influenced by the average degree of the network, and the polarization process is affected by the parameters of the assimilation effect and contrast effect. Moreover, the evolution processes in different dimensions of opinion show correlation under certain specific conditions, and the discontinuous equilibrium phenomenon is observed in multidimensional opinion evolution in subsequent experiments.

Enhancing the Realism of Simulation (EROS): On Implementing and Developing Psychological Theory in Social Simulation

Wander Jager
Journal of Artificial Societies and Social Simulation 20 (3) 14

Kyeywords: Psychology, Theory, Needs, Norms, Cognition, Attitudes
Abstract: Using psychological theory in agent formalisations is relevant to capture behavioural phenomena in simulation models (Enhance Realism Of Simulation - EROS). Whereas the potential contribution of psychological theory is important, also a number of challenges and problems in doing so are discussed. Next examples of implementations of psychological theory are being presented, ranging from simple implementations (KISS) of rather isolated theories to extended models that integrate different theoretical perspectives. The role of social simulation in developing dynamic psychological theory and integrated social psychological modelling is discussed. We conclude with some fundamental limitations and challenges concerning the modelling of human needs, cognition and behaviour.

Modeling Organizational Cognition: The Case of Impact Factor

Davide Secchi and Stephen J. Cowley
Journal of Artificial Societies and Social Simulation 21 (1) 13

Kyeywords: Organizational Cognition, Distributed Cognition, E-Cognition, Impact Factor, Perceived Scientific Value, Social Organizing, Agent-Based Simulation Modeling
Abstract: This article offers an alternative perspective on organizational cognition based on e-cognition whereby appeal to systemic cognition replaces the traditional computational model of the mind that is still extremely popular in organizational research. It uses information processing, not to explore inner processes, but as the basis for pursuing organizational matters. To develop a theory of organizational cognition, the current work presents an agent-based simulation model based on the case of how individual perception of scientific value is affected by and affects organizational intelligence units' (e.g., research groups', departmental) framing of the notorious impact factor. Results show that organizational cognition cannot be described without an intermediate meso scale – called here social organizing – that both filters and enables the many kinds of socially enabled perception, action and behavior that are so characteristic of human cognition.

One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models

Hannah Muelder and Tatiana Filatova
Journal of Artificial Societies and Social Simulation 21 (4) 5

Kyeywords: Micro-Foundations, Households, Decision Making, Behaviour, Theory, Energy
Abstract: As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents' decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructs such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce, and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four types of differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents' decisions, in the representation of these potentially different factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using the example of an agent-based model, which is developed to study regional impacts of households' solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in the simulation outcomes, even when agents' decision rules are based on the same theory and data. The paper outlines a number of critical methodological implications for future developments in agent-based modelling.

Hard Work, Risk-Taking, and Diversity in a Model of Collective Problem Solving

Amin Boroomand and Paul E. Smaldino
Journal of Artificial Societies and Social Simulation 24 (4) 10

Kyeywords: Teams, NK Landscape, Risk, Collective Decision Making, Agent-Based Model
Abstract: We studied an agent-based model of collective problem solving in which teams of agents search on an NK landscape and share information about newly found solutions. We analyzed the effects of team members’ behavioral strategies, team size, and team diversity on overall performance. Depending on the landscape complexity and a team’s features a team may eventually find the best possible solution or become trapped at a local maximum. Hard-working agents can explore more solutions per unit time, while risk-taking agents inject randomness in the solutions they test. We found that when teams solve complex problems, both strategies (risk-taking and hard work) have positive impacts on the final score, and the positive effect of moderate risk-taking is substantial. However, risk-taking has a negative effect on how quickly a team achieves its final score. If time restrictions can be relaxed, a moderate level of risk can produce an improved score. If the highest priority is instead to achieve the best possible score in the shortest amount of time, the hard work strategy has the greatest impact. When problems are simpler, risk-taking behavior has a negative effect on performance, while hard work decreases the time required to solve the problem. We also find that larger teams generally solved problems more effectively, and that some of this positive effect is due to the increase in diversity. We show more generally that increasing the diversity of teams has a positive impact on the team’s final score, while more diverse teams also require less time to reach their final solution. This work contributes overall to the larger literature on collective problem solving in teams.

PastoralScape: An Environment-Driven Model of Vaccination Decision Making Within Pastoralist Groups in East Africa

Matthew Sottile, Richard Iles, Craig McConnel, Ofer Amram and Eric Lofgren
Journal of Artificial Societies and Social Simulation 24 (4) 11

Kyeywords: Agent-Based Model, Random Field Ising Model, Livestock Health, Rift Valley Fever, Contagious Bovine Pleuropneumonia, Economic Decision Making
Abstract: Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

The Dynamical Relation Between Individual Needs and Group Performance: A Simulation of the Self-Organising Task Allocation Process

Shaoni Wang, Kees Zoethout, Wander Jager and Yanzhong Dang
Journal of Artificial Societies and Social Simulation 24 (4) 9

Kyeywords: Individual Needs, Motivation, Group Performance, Self-Organisation, Task Allocation, Agent-Based Modelling
Abstract: Team performance can be considered a macro-level outcome that depends on three sets of micro-level factors: individual workers contributing to the task, team composition, and task characteristics. For a number of reasons, the complex dynamics between individuals in the task allocation process are difficult to systematically explore in traditional experimental settings: the motivational dynamics, the complex dynamics of task allocation processes, and the lack of experimental control over team composition imply an ABM-approach being more feasible. For this reason, we propose an updated version of the WORKMATE model that has been developed to explore the dynamics of team performance. In doing so, we added Deci and Ryan’s SDT theory, stating that people are motivated by three psychological needs, competence, autonomy, and belongingness. This paper is aimed at explaining the architecture of the model, and some first simulation runs as proof of concept. The experimental results show that: 1) an appropriate motivation threshold will help the team have the lowest performance time; 2) the time needed for the task allocation process is related to the importance of different motivations; 3) highly satisfied teams are more likely composed of members valuing autonomy.

Calibrating Agent-Based Models of Innovation Diffusion with Gradients

Florian Kotthoff and Thomas Hamacher
Journal of Artificial Societies and Social Simulation 25 (3) 4

Kyeywords: Agent-Based Modeling, Multi-Agent Simulation, Innovation Diffusion, Adoption Model, Decision Making, Calibration
Abstract: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R2 ≃ 0.7.