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93 articles matched your search for the keywords:
Relative Agreement Model, Opinion Dynamics, Agent-Based Simulation

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

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

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

What is Ascape and Why Should You Care?

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

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

Simple is Beautiful? and Necessary

Guillaume Deffuant and Frederic Amblard
Journal of Artificial Societies and Social Simulation 6 (1) 6

Kyeywords: Extremist Attitudes; Opinion Dynamics; Sociophysics
Abstract: A contribution to the JASSS forum, in reaction to the paper in FASZ about our model of extremism

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

Luis R. Izquierdo, Nicholas M. Gotts and J. Gareth 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.

Deception and Convergence of Opinions

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

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

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

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

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

Mass Media and Polarisation Processes in the Bounded Confidence Model of Opinion Dynamics

Gary Mckeown and Noel Sheehy
Journal of Artificial Societies and Social Simulation 9 (1) 11

Kyeywords: Opinion Dynamics, Mass Media, Polarisation, Extremists, Consensus
Abstract: This paper presents a social simulation in which we add an additional layer of mass media communication to the social network \'bounded confidence\' model of Deffuant et al (2000). A population of agents on a lattice with continuous opinions and bounded confidence adjust their opinions on the basis of binary social network interactions between neighbours or communication with a fixed opinion. There are two mechanisms for interaction. \'Social interaction\' occurs between neighbours on a lattice and \'mass communication,\' adjusts opinions based on an agent interacting with a fixed opinion. Two new variables are added, polarisation: the degree to which two mass media opinions differ, and broadcast ratio: the number of social interactions for each mass media communication. Four dynamical regimes are observed, fragmented, double extreme convergence, a state of persistent opinion exchange leading to single extreme convergence and a disordered state. Double extreme convergence is found where agents are less willing to change opinion and mass media communications are common or where there is moderate willingness to change opinion and a high frequency of mass media communications. Single extreme convergence is found where there is moderate willingness to change opinion and a lower frequency of mass media communication. A period of persistent opinion exchange precedes single extreme convergence, it is characterized by the formation of two opposing groups of opinion separated by a gradient of opinion exchange. With even very low frequencies of mass media communications this results in a move to central opinions followed by a global drift to one extreme as one of the opposing groups of opinion dominates. A similar pattern of findings is observed for Neumann and Moore neighbourhoods.

The Viability of Cooperation Based on Interpersonal Commitment

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

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

A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast

Laurent Salzarulo
Journal of Artificial Societies and Social Simulation 9 (1) 13

Kyeywords: Opinion Dynamics, Self-Categorization Theory, Consensus, Polarization, Extremism
Abstract: We propose a new continuous opinion dynamics model inspired by social psychology. It is based on a central assumption of self-categorization theory called principle of meta-contrast. We study the behaviour of the model for several network interactions and show that, in particular, consensus, polarization or extremism are possible outcomes, even without explicit introduction of extremist agents. The model is compared to other existing opinion dynamics models.

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

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

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

An Agent-Based Spatially Explicit Epidemiological Model in MASON

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

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

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

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

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

Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology

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.

Finite Neighborhood Binary Games: a Structural Study

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

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

How Realistic Should Knowledge Diffusion Models Be?

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

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

Opinion Dynamics: the Effect of the Number of Peers Met at Once

Diemo Urbig, Jan Lorenz and Heiko Herzberg
Journal of Artificial Societies and Social Simulation 11 (2) 4

Kyeywords: Opinion Dynamics, Communication Regime
Abstract: The opinion dynamics model introduced by Deffuant and Weisbuch as well as the one by Hegselmann and Krause are rather similar. In both models individuals are assumed to have opinions about an issue, they meet and discuss, and they may adapt their opinions towards the other agents` opinions or may ignore each other if their positions are too different. Both models differ with respect to the number of peers they meet at once. Furthermore the model by Deffuant and Weisbuch has a convergence parameter that controls how fast agents adapt their opinions. By defining the reversed parameter as self-support we can extend the applicability of this parameter to scenarios with more than one interaction partner. We investigate the effects of changing the number of peers met at once, which is done for different population sizes, and the effects of changing the self-support. For describing the dynamics we look at different statistics, i.e. number of cluster, number of major clusters, and Gini coefficient.

Horizontal and Vertical Multiple Implementations in a Model of Industrial Districts

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

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

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

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

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

Simulating Evolutionary Games: A Python-Based Introduction

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

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

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

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

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

Replication in the Deception and Convergence of Opinions Problem

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

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

Can Extremism Guarantee Pluralism?

Floriana Gargiulo and Alberto Mazzoni
Journal of Artificial Societies and Social Simulation 11 (4) 9

Kyeywords: Extremists, Segregation, Opinion Dynamics
Abstract: Many models have been proposed to explain the opinion formation in a group of individuals; most of these models study the opinion propagation as the interaction between nodes/agents in a social network. Opinion formation is a very complex process and a realistic model should also take into account the important feedbacks that the opinions of the agents have on the structure of the social networks and on the characteristics of the opinion dynamics. In this paper we will show that associating to different agents different kind of interconnections and different interacting behaviour can lead to interesting scenarios, like the co-existence of several opinion clusters, namely pluralism. In our model agents have opinions uniformly and continuously distributed between two extremes. The social network is formed through a social aggregation mechanism including the segregation process of the extremists that results in many real communities. We show how this process affects opinion dynamics in the whole society. In the opinion evolution we consider the different predisposition of single individuals to interact and to to modify each other's opinions; we associate to each individual a different tolerance threshold, depending on its own opinion: extremists are less willing to interact with individuals with strongly different opinions and to change significantly their ideas. A general result is obtained: when there is no interaction restriction, the opinion always converges to uniformity, but the same is happening whenever a strong segregation process of the extremists occurs. Only when extremists are forming clusters but these clusters keep interacting with the rest of the society, the survival of a wide opinion range is guaranteed.

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

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

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

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

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

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

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

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

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

Flocking Behaviour: Agent-Based Simulation and Hierarchical Leadership

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

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

Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments

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

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

Obligation Norm Identification in Agent Societies

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

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

Opinion Formation by Informed Agents

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

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

The Current State of Normative Agent-Based Systems

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

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

An Agent-Based Model of Sustainable Corporate Social Responsibility Activities

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

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

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

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

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

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

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

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

Reexamining the Relative Agreement Model of Opinion Dynamics

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

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

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

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

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

The Leviathan Model: Absolute Dominance, Generalised Distrust, Small Worlds and Other Patterns Emerging from Combining Vanity with Opinion Propagation

Guillaume Deffuant, Timoteo Carletti and Sylvie Huet
Journal of Artificial Societies and Social Simulation 16 (1) 5

Kyeywords: Opinion Dynamics, Vanity, Leviathan
Abstract: We propose an opinion dynamics model that combines processes of vanity and opinion propagation. The interactions take place between randomly chosen pairs. During an interaction, the agents propagate their opinions about themselves and about other people they know. Moreover, each individual is subject to vanity: if her interlocutor seems to value her highly, then she increases her opinion about this interlocutor. On the contrary she tends to decrease her opinion about those who seem to undervalue her. The combination of these dynamics with the hypothesis that the opinion propagation is more efficient when coming from highly valued individuals, leads to different patterns when varying the parameters. For instance, for some parameters the positive opinion links between individuals generate a small world network. In one of the patterns, absolute dominance of one agent alternates with a state of generalised distrust, where all agents have a very low opinion of all the others (including themselves). We provide some explanations of the mechanisms behind these emergent behaviors and finally propose a discussion about their interest.

Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics

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.

Segregated Cooperation

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

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

Simple Heuristics as Equilibrium Strategies in Mutual Sequential Mate Search

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

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

Opinion Formation in the Digital Divide

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

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

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

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

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

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

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

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

Coevolution of Opinions and Directed Adaptive Networks in a Social Group

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.

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

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

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

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

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

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

Intergroup Conflict Escalation Leads to More Extremism

Meysam Alizadeh, Alin Coman, Michael Lewis and Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 17 (4) 4

Kyeywords: Intergroup Conflict, Opinion Dynamics, Differentiation, Bounded Confidence, Extremism
Abstract: Empirical findings in the intergroup conflict literature show that individuals’ beliefs that mark differentiation from out-groups become radicalized as intergroup tensions escalate. They also show that this differentiation is proportional to tension escalation. In this paper, we are interested to develop an agent-based model which captures these findings in order to explore the effect of perceived intergroup conflict escalation on the average number of emergent extremists and opinion clusters in the population. The proposed model builds on the 2-dimensional bounded confidence model proposed by Huet et al (2008). The results show that the average number of extremists has a negative correlation with intolerance threshold and positive correlation with the amount of opinion movement when two agents are to reject each other’s belief. In other words, the more tensions exist between groups, the more individuals getting extremists. We also found that intergroup conflict escalation leads to lower opinion diversity in the population compared with normal situations.

The Critical Few: Anticonformists at the Crossroads of Minority Opinion Survival and Collapse

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.

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

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

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

Modeling Education and Advertising with Opinion Dynamics

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.

From Anti-Conformism to Extremism

Gérard Weisbuch
Journal of Artificial Societies and Social Simulation 18 (3) 1

Kyeywords: Extremism, Opinion Dynamics, Bounded Confidence, Clustering, Anti-Conformism
Abstract: We here present a model of the dynamics of extremism based on opinion dynamics in order to understand the circumstances which favour its emergence and development in large fractions of the general public. Our model is based on the bounded confidence hypothesis and on the evolution of initially anti-conformist agents to extreme positions. Numerical analyses demonstrate that a few extremists are able to drag a large fraction of conformists agents to their position provided that they express theirs views more often than the conformists. The most influential parameter controlling the outcome of the dynamics is the uncertainty of the conformist agents; the higher their uncertainty, the higher is the influence of anti-conformists. Systematic scans of the parameter space show the existence of two regime transitions, one following the conformists uncertainty parameter and the other one following the anti-conformism strength.

Optimal Opinion Control: The Campaign Problem

Rainer Hegselmann, Stefan König, Sascha Kurz, Christoph Niemann and Jörg Rambau
Journal of Artificial Societies and Social Simulation 18 (3) 18

Kyeywords: Opinion Dynamics, Optimal Opinion Control, Bounded Confidence, Mixed Integer Linear Programming, Heuristics
Abstract: Opinion dynamics is nowadays a very common field of research. In this article we formulate and then study a novel, namely strategic perspective on such dynamics: There are the usual 'normal' agents that update their opinions, for instance according the well-known bounded con dence mechanism. But, additionally, there is at least one strategic agent. That agent uses opinions as freely selectable strategies to get control on the dynamics: The strategic agent of our benchmark problem tries, during a campaign of a certain length, to influence the ongoing dynamics among normal agents with strategically placed opinions (one per period) in such a way, that, by the end of the campaign, as much as possible normals end up with opinions in a certain interval of the opinion space. Structurally, such a problem is an optimal control problem. That type of problem is ubiquitous. Resorting to advanced and partly non-standard methods for computing optimal controls, we solve some instances of the campaign problem. But even for a very small number of normal agents, just one strategic agent, and a ten-period campaign length, the problem turns out to be extremely dicult. Explicitly we discuss moral and political concerns that immediately arise, if someone starts to analyze the possibilities of an optimal opinion control.

Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes

Meysam Alizadeh and Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 18 (3) 8

Kyeywords: Opinion Dynamics, Activation Regime, Extremism, Opinion Clusters
Abstract: Empirical evidence shows significant heterogeneity in the timing of individuals' activities. Moreover, computational analysis of agent-based models has shown the importance of the activation regime. In this paper, we apply four different asynchronous updating schemes, including random, uniform, and two state-driven Poisson updating schemes on an agent-based opinion dynamics model. We compare the effect of these activation regimes by measuring the appropriate opinion clustering statistics and the number of emergent extremists. Results exhibit both qualitative and quantitative difference among activation regimes, including some counterintuitive cases. In particular, we find that exposing radical/moderate agents to more encounters decreases/increases the average number of extremists compared to other types of activation regimes. Results also show that no specific updating scheme can always outperform the others in reaching consensus.

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

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

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

Bounded Confidence Model with Fixed Uncertainties and Extremists: The Opinions Can Keep Fluctuating Indefinitely

Jean-Denis Mathias, Sylvie Huet and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 19 (1) 6

Kyeywords: Bounded Confidence Model, Opinion Dynamics, Noise, Fluctuations, Extremists
Abstract: The bounded confidence model and its variants applied to moderate and extremist agents exhibit three types of attractors: central clusters, double extreme and single extreme clusters. These attractors are observed when the models include a dynamics on the uncertainties tending to decrease the moderate uncertainties when interacting with extremists. We show here that a new stationary state appears when the uncertainties are fixed, for large uncertainties of the moderates. In this stationary state, the opinions of moderate agents keep fluctuating without clustering, altogether forming a stable density which shape changes significantly when the parameters vary.

Learning with Communication Barriers Due to Overconfidence. What a "Model-To-Model Analysis" Can Add to the Understanding of a Problem

Juliette Rouchier and Emily Tanimura
Journal of Artificial Societies and Social Simulation 19 (2) 7

Kyeywords: Collective Learning, Agent-Based Simulation, M2M, Influence Model, Analytical Model, Over-Confidence
Abstract: In this paper, we describe a process of validation for an already published model, which relies on the M2M paradigm of work. The initial model showed that over-confident agents, which refuse to communicate with agents whose beliefs differ, disturb collective learning within a population. We produce an analytical model based on probabilistic analysis, that enables us to explain better the process at stake in our first model, and demonstrates that this process is indeed converging. To make sure that the convergence time is meaningful for our question (not just for an infinite number of agents living for an infinite time), we use the analytical model to produce very simple simulations and assess that the result holds in finite contexts.

Revising the Human Development Sequence Theory Using an Agent-Based Approach and Data

Viktoria Spaiser and David J. T. Sumpter
Journal of Artificial Societies and Social Simulation 19 (3) 1

Kyeywords: Agent-Based Simulation, Human Development Sequence Theory, Democratisation, Mathematical Modeling, Data Analysis, Inequality
Abstract: Agent-based models and computer simulations are promising tools for studying emergent macro-phenomena. We apply an agent-based approach in combination with data analysis to investigate the human development sequence (HDS) theory developed by Ronald Inglehart and Christian Welzel. Although the HDS theory is supported by correlational evidence, the sequence of economic growth, democracy and emancipation stated by the theory is not entirely consistent with data. We use an agent-based model to make quantitative predictions about several different micro-level mechanisms. Comparison to data allows us to identify important inconsistencies between HDS and the data, and propose revised agent-based models that modify the theory. Our results indicate the importance of elites and economic inequality in explaining the data available on democratisation.

Dynamics of Uncertain Opinion Formation: An Agent-Based Simulation

Haiming Liang, Yucheng Dong and Congcong Li
Journal of Artificial Societies and Social Simulation 19 (4) 1

Kyeywords: Opinion Formation, Uncertain Opinions, Uncertainty Tolerance, Communication Regime, Agent-Based Simulation
Abstract: Opinion formation describes the dynamics of opinions in a group of interaction agents and is a powerful tool for predicting the evolution and diffusion of the opinions. The existing opinion formation studies assume that the agents express their opinions by using the exact number, i.e., the exact opinions. However, when people express their opinions, sentiments, and support emotions regarding different issues, such as politics, products, and events, they often cannot provide the exact opinions but express uncertain opinions. Furthermore, due to the differences in culture backgrounds and characters of agents, people who encounter uncertain opinions often show different uncertainty tolerances. The goal of this study is to investigate the dynamics of uncertain opinion formation in the framework of bounded confidence. By taking different uncertain opinions and different uncertainty tolerances into account, we use an agent-based simulation to investigate the influences of uncertain opinions in opinion formation from two aspects: the ratios of the agents that express uncertain opinions and the widths of the uncertain opinions, and also provide the explanations of the observations obtained.

Exploring an Effective Incentive System on a Groupware

Fujio Toriumi, Hitoshi Yamamoto and Isamu Okada
Journal of Artificial Societies and Social Simulation 19 (4) 6

Kyeywords: Groupware, Agent-Based Simulation, Meta-Sanction Game, Public Good Games,
Abstract: Groupware is an effective form of media for knowledge sharing and active open communication. One remaining important issue is how to design groupware in which vast amounts of beneficial content are provided and active discussion is facilitated. The behavior of information in such a medium resembles public-goods games because users voluntarily post beneficial information that creates media values. Many studies on such games have shown the effects of rewards or punishments in promoting cooperative behavior. In this paper, we show what types of incentive systems of rewards and punishments promote and maintain effective information behaviors or cooperative regimes in actual groupware. Our agent-based simulation demonstrates that a meta-reward system in which rewarders can gain other benefits for their own reward actions will probably encourage cooperation. Counterintuitively, our simulation also demonstrates that a system that applies sanctioning functions does not necessarily promote cooperation. Interestingly, a first-order reward system without any second-order incentives impedes the formation of cooperative regimes, while this is not the case with first-order punishment systems without second-order incentives. These findings may elucidate how successful groupware operates.

Robust Clustering in Generalized Bounded Confidence Models

Takasumi Kurahashi-Nakamura, Michael Mäs and Jan Lorenz
Journal of Artificial Societies and Social Simulation 19 (4) 7

Kyeywords: Opinion Dynamics, Continuous Opinions, Noise, Diversity Puzzle, Facilitation, Probability of Acceptance
Abstract: Bounded confidence models add a critical theoretical ingredient to the explanation of opinion clustering, opinion polarisation, and the persistence of opinion diversity, assuming that individuals are only influenced by others who are sufficiently similar and neglect actors with too different views. However, despite its enormous recognition in the literature, the bounded confidence assumption has been criticized for being able to explain diversity only when implemented in a very strict and unrealistic way. The model is unable to explain patterns of opinion diversity when actors are sometimes influenced also by others who hold distant views, even when these deviations from the bounded-confidence assumption are rare and random. Here, we echo this criticism but we also show that the model's ability to explain opinion diversity can be regained when another assumption is relaxed. Building on modeling work from statistical mechanics, we include that actors' opinion changes do not only result from social influence. When other influences are modelled as random, uniformly distributed draws, then robust patterns of opinion clustering emerge also with the relaxed implementations of bounded confidence. The results holds under both communication regimes: the updating to the average of all acceptable opinions as in the model of Hegselmann and Krause (2002) and random pair-wise communication as in the model of Deffuant et al. (2000). We discuss implications for future modelling work and point to gaps in empirical research on influence.

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Patryk Siedlecki, Janusz Szwabiński and Tomasz Weron
Journal of Artificial Societies and Social Simulation 19 (4) 9

Kyeywords: Opinion Dynamics, Social Influence, Conformity, Anticonformity, Bi-Polarization, Agent-Based Modelling
Abstract: Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agent-based model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al, 2009b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a bi-polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction L of negative cross-links between cliques. In the regime of small values of L the system is able to reach the total positive consensus. If the values of L are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a sufficient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is required to arrive at a polarized steady state within our model.

A Psychologically-Motivated Model of Opinion Change with Applications to American Politics

Peter Duggins
Journal of Artificial Societies and Social Simulation 20 (1) 13

Kyeywords: Agent-Based Model, Opinion Dynamics, Social Networks, Conformity, Polarization, Extremism
Abstract: Agent-based models are versatile tools for studying how societal opinion change, including political polarization and cultural diffusion, emerges from individual behavior. This study expands agents' psychological realism using empirically-motivated rules governing interpersonal influence, commitment to previous beliefs, and conformity in social contexts. Computational experiments establish that these extensions produce three novel results: (a) sustained strong diversity of opinions within the population, (b) opinion subcultures, and (c) pluralistic ignorance. These phenomena arise from a combination of agents' intolerance, susceptibility and conformity, with extremist agents and social networks playing important roles. The distribution and dynamics of simulated opinions reproduce two empirical datasets on Americans' political opinions.

Augmenting Bottom-up Metamodels with Predicates

Ross Gore, Saikou Diallo, Christopher Lynch and Jose Padilla
Journal of Artificial Societies and Social Simulation 20 (1) 4

Kyeywords: Metamodel, Agent-Based Simulation, Statistical Modeling, Predicates, Validation
Abstract: Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: (1) top-down and (2) bottom-up. The top down approach enables users to decompose high-level mental models into behaviors and interactions of agents. In contrast, the bottom-up approach constructs a relatively small, simple model that approximates the structure and outcomes of a dataset gathered from the runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. For most users the construction of a bottom-up metamodel entails: (1) creating an experimental design, (2) running the simulation for all cases specified by the design, (3) collecting the inputs and output in a dataset and (4) applying first-order regression analysis to find a model that effectively estimates the output. Unfortunately, the sums of input variables employed by first-order regression analysis give the impression that one can compensate for one component of the system by improving some other component even if such substitution is inadequate or invalid. As a result the metamodel can be misleading. We address these deficiencies with an approach that: (1) automatically generates Boolean conditions that highlight when substitutions and tradeoffs among variables are valid and (2) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations.

An Agent Based Model for a Double Auction with Convex Incentives

Annalisa Fabretti and Stefano Herzel
Journal of Artificial Societies and Social Simulation 20 (1) 7

Kyeywords: Incentives, Agent-Based Simulations, Market Instability, Price Convergence, Order Book Analysis
Abstract: We studied the influence of convex incentives, e.g. option-like compensations, on the behavior of financial markets. Such incentives, usually offered to portfolio managers, have been often considered a potential source of market instability. We built an agent-based model of a double-auction market where some of the agents are endowed with convex contracts. We show that these contracts encourage traders to buy more aggressively, increasing total demand and market prices. Our analysis suggests that financial markets with many managers with convex contracts are more likely to be more unstable and less efficient.

How Social Unrest Started Innovations in a Food Supply Chain

Jan Buurma, Wil Hennen and Tim Verwaart
Journal of Artificial Societies and Social Simulation 20 (1) 8

Kyeywords: Sociotechnical Innovation, Opinion Dynamics, Content Analysis, Dramaturgical Analysis, Food Supply Chain
Abstract: Transitions leading to sociotechnical innovations in food supply chains have been described in dramaturgical analyses on the basis of newspaper articles and parliamentary records. The time scale of the transitions driven by aroused public opinion on issues such as animal welfare, is typically a decade. Actors are primary producers (farmers), other supply chain parties, authorities, NGOs voicing particular opinions, political parties, and consumers. In this article, their interactions and reactions to external events are modelled in an agent-based simulation based on opinion dynamics. The purposes of the simulation are (1) to validate that hypothetical relations derived from the dramaturgical analysis indeed lead to the emergence of the observed transitions, and (2) to study how the system could have developed under different behaviours or a different course of external events. Simulation results and a sensitivity analysis are discussed.

How, when and where can Spatial Segregation Induce Opinion Polarization? Two Competing Models

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.

The Explanation of Social Conventions by Melioration Learning

Johannes Zschache
Journal of Artificial Societies and Social Simulation 20 (3) 1

Kyeywords: Reinforcement Learning, Agent-Based Simulation, N-Way Coordination Game, Roth-Erev Model
Abstract: In line with previous research, the evolution of social conventions is explored by n-way coordination games. A convention is said to be established if decisions of all actors synchronise over time. In contrast to the earlier studies, an empirically well-grounded process of reinforcement learning is used as behavioural assumption. The model is called melioration learning. It is shown by agent-based simulations that melioration enables actors to establish a convention. Besides the payoffs of the coordination game, the network structure of interactions affects actors' ability to coordinate their choices and the speed of convergence. The results of melioration learning are compared to predictions of the Roth-Erev model.

Effects of the Interaction Between Ideological Affinity and Psychological Reaction of Agents on the Opinion Dynamics in a Relative Agreement Model

Norma L. Abrica-Jacinto, Evguenii Kurmyshev and Héctor A. Juárez
Journal of Artificial Societies and Social Simulation 20 (3) 3

Kyeywords: Opinion Dynamics, Ideological Affinity, Artificial Society, Relative Agreement, Agent-Based Model
Abstract: Ideology is one of the defining elements of opinion dynamics. In this paper, we report the effects of the nonlinear interaction of ideological affinity with the psychological reaction of agents in the frame of a multiparametric mathematical model of opinion dynamics. Computer simulations of artificial networked societies composed of agents of two psychological types were used for studying opinion formation; the simulations showed a phenomenon of preferential self-organization into groups of ideological affinity at the first stages of opinion evolution. The separation into ideologically akin opinion groups (ideological affinity) was more notable in societies composed mostly of concord agents; a larger opinion polarization was associated with the increase of agents’ initial average opinion uncertainty. We also observed a sensibility of opinion dynamics to the initial conditions of opinion and uncertainty, indicating potential instabilities. A measure of convergence was introduced to facilitate the analysis of transitions between the opinion states of networked societies and to detect social instability events. We found that the average of opinion uncertainty distribution reaches a steady state with values lower than the initial average value, sometimes nearing zero, which points at socially apathetic agents. Our analyses showed that the model can be utilized for further investigation on opinion dynamics and can be extended to other social phenomena.

Growing Unpopular Norms

Christoph Merdes
Journal of Artificial Societies and Social Simulation 20 (3) 5

Kyeywords: Social Norms, Agent-Based Simulation, Social Influence, Pluralistic Ignorance
Abstract: Unpopular norms are a pervasive and puzzling phenomenon of the social world. They are norms that are established and maintained against the interest of their subjects, but without external coercion. Pluralistic ignorance has been suggested as a potential explanation of unpopular norms. What is currently lacking is a formal model of this process that can be meaningfully compared with empirically known properties of pluralistic ignorance. An agent-based model of a growing social network can reproduce the most significant qualitative features, viz a deviation of the perceived norm from the preference distribution and the dynamical lag of the former behind the latter. In addition, the model is extended with a central influence representing for example central media or a powerful political elite.

Models of Social Influence: Towards the Next Frontiers

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.

Opinion Communication on Contested Topics: How Empirics and Arguments can Improve Social Simulation

Annalisa Stefanelli and Roman Seidl
Journal of Artificial Societies and Social Simulation 20 (4) 3

Kyeywords: Agent-Based Model, Arguments, Opinion Dynamics, Social Judgment
Abstract: The effect of social interactions on how opinions are developed and changed over time is crucial to public processes that involve citizens and their points of view. In this opinion dynamics exercise, we address the topic of nuclear waste repositories in Switzerland and suggest a more realistic investigation of public opinion using agent-based modeling in combination with empirical data and sociopsychological theory. Empirical data obtained from an online questionnaire (N = 841) is used for the initialization of the model, whose agents directly represent the participants. We use social judgment theory (SJT) to describe how opinions can be adapted during social interactions, including through mechanisms of contrast and assimilation. Furthermore, we focus on the definition of “opinion” itself, claiming that working with disaggregated opinions (i.e., arguments) can play a determining role if one aims to capture real-world mechanisms of opinion dynamics. Simulation results show different patterns for the three different argument categories used for this specific topic (i.e., risk, benefit, and process), suggesting a mutual influence between an individual’s initial knowledge and evaluations and an individual’s social dynamics and opinion changes. The importance of content-related and empirical information, as well as the theory and mechanisms used in the social simulation, are discussed.

Asynchronous Opinion Dynamics with Online and Offline Interactions in Bounded Confidence Model

Zhaogang Ding, Yucheng Dong, Haiming Liang and Francisco Chiclana
Journal of Artificial Societies and Social Simulation 20 (4) 6

Kyeywords: Opinion Dynamics, Asynchronism, Bounded Confidence, Agent-Based Simulation
Abstract: Nowadays, about half of the world population can receive information and exchange opinions in online environments (e.g. the Internet), while the other half do so offline (e.g. face to face). The speed at which information is received and opinions are exchanged in online environment is much faster than offline. To model this phenomenon, in this paper we consider online and offline as two subsystems in opinion dynamics and assume asynchronization when agents in these two subsystems update their opinions. We unfold that asynchronization has a strong impact on the steady-state time of the opinion dynamics, the opinion clusters and the interactions between online and offline subsystems. Furthermore, these effects are often enhanced the larger the size of the online subsystem is.

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.

The Role of Heterogeneity and the Dynamics of Voluntary Contributions to Public Goods: An Experimental and Agent-Based Simulation Analysis

Engi Amin, Mohamed Abouelela and Amal Soliman
Journal of Artificial Societies and Social Simulation 21 (1) 3

Kyeywords: Agent-Based Simulation, Cooperation, Public Goods Game, Laboratory Experiment, Social Preferences
Abstract: This paper examines the role of heterogeneous agents in the study of voluntary contributions to public goods. A human-subject experiment was conducted to classify agent types and determine their effects on contribution levels. Data from the experiment was used to build and calibrate an agent-based simulation model. The simulations display how different compositions of agent preference types affect the contribution levels. Findings indicate that the heterogeneity of cooperative preferences is an important determinant of a population’s contribution pattern.

Emergence of Task Formation in Organizations: Balancing Units' Competence and Capacity

Friederike Wall
Journal of Artificial Societies and Social Simulation 21 (2) 6

Kyeywords: Agent-Based Simulation, Complexity, Coordination, Emergence, Reinforcement Learning, Task Formation
Abstract: This paper studies the emergence of task formation under conditions of limited knowledge about the complexity of the problem to be solved by an organization. Task formation is a key issue in organizational theory and the emergence of task formation is of particular interest when the complexity of the overall problem to be solved is not known in advance, since, for example, an organization is newly founded or has gone through an external shock. The paper employs an agent-based simulation based on the framework of NK fitness landscapes and controls for different levels of task complexity and for different coordination modes. In the simulations, artificial organizations are observed while searching for higher levels of organizational performance by two intertwined adaptive processes: short-termed search for superior solutions to the organizations' task and, in mid term, learning-based adaptation of task formation. The results suggest that the emerging task formations vary with the complexity of the underlying problem and, thereby, the balance between units' scope of competence and the organizational capacity for problem-solving is affected. For decomposable problems, task formations emerge which reflect the nature of the underlying problem; for non-decomposable structures, task formations with a broader scope of units' competence emerge. Furthermore, results indicate that, particularly for non-decomposable problems, the coordination mode employed in an organization subtly interferes with the emergence of task formation.

Impact of Basel III Countercyclical Measures on Financial Stability: An Agent-Based Model

Barbara Llacay and Gilbert Peffer
Journal of Artificial Societies and Social Simulation 22 (1) 6

Kyeywords: Agent-Based Simulation, Financial Markets, Financial Stability, Value-At-Risk, Countercyclical Regulation, Basel III
Abstract: The financial system is inherently procyclical, as it amplifies the course of economic cycles, and precisely one of the factors that has been suggested to exacerbate this procyclicality is the Basel regulation on capital requirements. After the recent credit crisis, international regulators have turned their eyes to countercyclical regulation as a solution to avoid similar episodes in the future. Countercyclical regulation aims at preventing excessive risk taking during booms to reduce the impact of losses suffered during recessions, for example increasing the capital requirements during the good times to improve the resilience of financial institutions at the downturn. The Basel Committee has already moved forward towards the adoption of countercyclical measures on a global scale: the Basel III Accord, published in December 2010, revises considerably the capital requirement rules to reduce their procyclicality. These new countercyclical measures will not be completely implemented until 2019, so their impact cannot be evaluated yet, and it is a crucial question whether they will be effective in reducing procyclicality and the appearance of crisis episodes such as the one experienced in 2007-08. For this reason, we present in this article an agent-based model aimed at analysing the effect of two countercyclical mechanisms introduced in Basel III: the countercyclical buffer and the stressed VaR. In particular, we focus on the impact of these mechanisms on the procyclicality induced by market risk requirements and, more specifically, by value-at-risk models, as it is a issue of crucial importance that has received scant attention in the modeling literature. The simulation results suggest that the adoption of both of these countercyclical measures improves market stability and reduces the emergence of crisis episodes.

Participatory Modeling and Simulation with the GAMA Platform

Patrick Taillandier, Arnaud Grignard, Nicolas Marilleau, Damien Philippon, Quang-Nghi Huynh, Benoit Gaudou and Alexis Drogoul
Journal of Artificial Societies and Social Simulation 22 (2) 3

Kyeywords: Agent-Based Simulation, Participatory Modeling, Participatory Simulation, Serious Game
Abstract: In recent years, agent-based simulation has become an important tool to study complex systems. However, the models produced are rarely used for decision-making support because stakeholders are often not involved in the modeling and simulation processes. Indeed, if several tools dedicated to participatory modeling and simulation exist, they are limited to the design of simple - KISS - models, which limit their potential impact. In this article, we present the participatory tools integrated within the GAMA modeling and simulation platform. These tools, which take advantage of the GAMA platform concerning the definition of rich - KIDS - models, allows to build models graphically and develop distributed serious games in a simple way. Several application examples illustrate their use and potential.

Endogenous Changes in Public Opinion Dynamics

Francisco J. León-Medina
Journal of Artificial Societies and Social Simulation 22 (2) 4

Kyeywords: Opinion Dynamics, Mechanism Explanation, Agent-Based Modeling, Homophily, Social Influence, Social Network
Abstract: Opinion dynamics models usually center on explaining how macro-level regularities in public opinion (uniformity, polarization or clusterization) emerge as the effect of local interactions of a population with an initial random distribution of opinions. However, with only a few exceptions, the understanding of patterns of public opinion change has generally been dismissed in this literature. To address this theoretical gap in our understanding of opinion dynamics, we built a multi-agent simulation model that could help to identify some mechanisms underlying changes in public opinion. Our goal was to build a model whose behavior could show different types of endogenously (not induced by the researcher) triggered transitions (rapid or slow, radical or soft). The paper formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions. According to our findings, only scale-free networks show fluctuations in public opinion. Public opinion changes in this model appear as a diffusion process of individual opinion shifts that is triggered by an opinion change of a highly connected agent. The frequency, rapidity and radicalness of the diffusion, and hence of public opinion fluctuations, positively depends on how influential external information is in individual opinions and negatively depends on how homophilic social interactions are.

Learning Opinions by Observing Actions: Simulation of Opinion Dynamics Using an Action-Opinion Inference Model

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.

Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models

Christopher Weimer, J.O. Miller, Raymond Hill and Douglas Hodson
Journal of Artificial Societies and Social Simulation 22 (4) 5

Kyeywords: Opinion Dynamics, Agent-Based Modeling, Scheduling, Asynchronous, Synchronous
Abstract: Opinion dynamics models are an important field of study within the agent-based modeling community. Agent scheduling elements within existing opinion dynamics models vary but are largely unjustified and only minimally explained. Furthermore, previous research on the impact of scheduling is scarce, partially due to a lack of a common taxonomy with which to discuss and compare schedules. The Synchrony, Actor type, Scale (SAS) taxonomy is presented, which aims to provide a common lexicon for agent scheduling in opinion dynamics models. This is demonstrated using a generalized repeated averaging model (GRAM) and a generalized bounded confidence model (GBCM). Significant differences in model outcomes with varied schedules are given, along with the results of intentional model biasing using only schedule variation. We call on opinion dynamics modelers to make explicit their choice of schedule and to justify that choice based on realistic social phenomena.

Tension Between Stability and Representativeness in a Democratic Setting

Victorien Barbet, Juliette Rouchier, Noé Guiraud and Vincent Laperrière
Journal of Artificial Societies and Social Simulation 23 (2) 5

Kyeywords: Agent-Based Model, Communication, Opinion Dynamics, Democracy, Non-Profit Organization, Short Food Chain
Abstract: We present a model showing the evolution of an organization of agents who discuss democratically about good practices. This model feeds on a field study we did for about twelve years in France where we followed NPOs, called AMAP, and observed their construction through time at the regional and national level. Most of the hypothesis we make are here either based on the literature on opinion diffusion or on the results of our field study. By defining dynamics where agents influence each other, make collective decision at the group level, and decide to stay in or leave their respective groups, we analyse the effect of different forms of vertical communication that is meant to spread good practices within the organization. Our main indicators of the good functioning of the democratic dynamics are stability and representativeness. We show that if communication about norms is well designed, it has a positive impact on both stability and representativeness. Interestingly the effect of communication increases with the number of dimensions discussed in the groups. Communication about norms is thus a valuable tool to use in groups that wish to improve their democratic practices without jeopardizing stability.

A Weighted Balance Model of Opinion Hyperpolarization

Simon Schweighofer, Frank Schweitzer and David Garcia
Journal of Artificial Societies and Social Simulation 23 (3) 5

Kyeywords: Polarization, Balance Theory, Opinion Dynamics, Agent-Based Modeling
Abstract: Polarization is threatening the stability of democratic societies. Until now, polarization research has focused on opinion extremeness, overlooking the correlation between different policy issues. In this paper, we explain the emergence of hyperpolarization, i.e., the combination of extremeness and correlation between issues, by developing a new theory of opinion formation called "Weighted Balance Theory (WBT)". WBT extends Heider's cognitive balance theory to encompass multiple weighted attitudes. We validated WBT on empirical data from the 2016 National Election Survey. Furthermore, we developed an opinion dynamics model based on WBT, which, for the first time, is able to generate hyperpolarization and to explain the link between affective and opinion polarization. Finally, our theory encompasses other phenomena of opinion dynamics, including mono-polarization and backfire effects.

Modeling Cultural Dissemination and Divergence Between Rural and Urban Regions

Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen and Adam Landsberg
Journal of Artificial Societies and Social Simulation 23 (4) 3

Kyeywords: Cultural Evolution, Cultural Transmission, Opinion Dynamics, Agent-Based Modeling, Cultural Dissemination
Abstract: The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as "cultural dissemination," is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.

WorkSim: An Agent-Based Model of Labor Markets

Jean-Daniel Kant, Gérard Ballot and Olivier Goudet
Journal of Artificial Societies and Social Simulation 23 (4) 4

Kyeywords: Agent-Based Simulation, Dual Labor Markets, Anticipations, Bounded Rationality, Policy Evaluation
Abstract: In this paper, we provide an overview of the WorkSim model, an agent-based framework designed to study labor markets. The first objective of this model was to reproduce, within rigorous stock-flow accounting, the gross flows of individuals between important work-states: i.e., employment (distinguishing fixed term contracts and open-ended contracts), unemployment and inactivity. French legal institutions of the labor market are modelled in some detail and constrain the decisions of the agents on job flows and worker flows. Firms and individuals are heterogeneous and all decisions are taken on the basis of bounded rationality, yet employers as well as workers form imperfect anticipations. One important theoretical novelty of the model is that we consider multi-job firms and shocks on the individual demand of the firms. Employers consider anticipated shocks when they decide on the types of contract. Once the model was calibrated, the secondary objective was to characterize the nature of the labor market under study, and notably the differentiated roles of the two types of contracts and their impact on unemployment. This is achieved, first by examining the patterns of flows and stocks of labor and secondly by sensitivity experiments, modifying certain exogenous parameters and variables such as total demand. We then used the model as a tool for experimenting labor market policies, including changes in the labor law in France.

An Argument Communication Model of Polarization and Ideological Alignment

Sven Banisch and Eckehard Olbrich
Journal of Artificial Societies and Social Simulation 24 (1) 1

Kyeywords: Argument Communication Theory, Opinion Dynamics, Polarisation, Ideological Alignment, Belief Systems, Cognitive-Evaluative Maps
Abstract: This multi-level model of opinion formation considers that attitudes on different issues are usually not independent. In the model, agents exchange beliefs regarding a series of facts. A cognitive structure of evaluative associations links different (partially overlapping) sets of facts on different political issues and determines agents’ attitudinal positions in a way borrowed from expectancy value theory. If agents preferentially interact with other agents who hold similar attitudes on one or several issues, this leads to biased argument pools and increasing polarization in the sense that groups of agents selectively believe in distinct subsets of facts. Besides the emergence of a bi-modal distribution of opinions on single issues as most previous opinion polarization models address, our model also accounts for the alignment of attitudes across several issues along ideological dimensions.

Opinion Dynamics and Collective Risk Perception: An Agent-Based Model of Institutional and Media Communication About Disasters

Francesca Giardini and Daniele Vilone
Journal of Artificial Societies and Social Simulation 24 (1) 4

Kyeywords: Risk Perceptions, Opinion Dynamics, Social Influence, Agent-Based Model
Abstract: The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals' attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seemed to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.

Introducing the Argumentation Framework Within Agent-Based Models to Better Simulate Agents' Cognition in Opinion Dynamics: Application to Vegetarian Diet Diffusion

Patrick Taillandier, Nicolas Salliou and Rallou Thomopoulos
Journal of Artificial Societies and Social Simulation 24 (2) 6

Kyeywords: Opinion Dynamics, Agent-Based Simulation, Argumentation Framework, Vegetarian Diets
Abstract: This paper introduces a generic agent-based model simulating the exchange and the diffusion of pro and con arguments. It is applied to the case of the diffusion of vegetarian diets in the context of a potential emergence of a second nutrition transition. To this day, agent-based simulation has been extensively used to study opinion dynamics. However, the vast majority of existing models have been limited to extremely abstract and simplified representations of the diffusion process. These simplifications impairs the realism of the simulations and disables the understanding of the reasons for the shift of an actor's opinion. The generic model presented here explicitly represents exchanges of arguments between actors in the context of an opinion dynamic model. In particular, the inner attitude towards an opinion of each agent is formalized as an argumentation graph and each agent can share arguments with other agents. Simulation experiments show that introducing attacks between arguments and a limitation of the number of arguments mobilized by agents has a strong impact on the evolution of the agents' opinion. We also highlight that when a new argument is introduced into the system, the quantity and the profile of the agents receiving the new argument will impact the evolution of the overall opinion. Finally, the application of this model to vegetarian diet adoption seems consistent with historical food behaviour dynamics observed during crises.

Dynamics of Public Opinion: Diverse Media and Audiences’ Choices

Zhongtian Chen and Hanlin Lan
Journal of Artificial Societies and Social Simulation 24 (2) 8

Kyeywords: Opinion Dynamics, Social Media, Polarization, Agent-Based Modeling, Opinion Guidance
Abstract: Studies on the fundamental role of diverse media in the evolution of public opinion can protect us from the spreading of brainwashing, extremism, and terrorism. Many fear the information cocoon may result in polarization of the public opinion. Hence, in this work, we investigate how audiences' choices among diverse media might influence public opinion. Specifically, we aim to figure out how peoples' horizons (i.e., range of available media) and quantity, as well as the distribution of media, may shape the space of public opinion. We propose a novel model of opinion dynamics that considers different influences and horizons for every individual, and we carry out simulations using a real-world social network. Numerical simulations show that diversity in media can provide more choices to the people, although individuals only choose media within the bounds of their horizons, extreme opinions are more diluted, and no opinion polarizations emerge. Furthermore, we find that the distribution of media's opinions can effectively influence the space for public opinion, but when the number of media grows to a certain level, its effect will reach a limitation. Finally, we show that the effect of campaigns for consciousness or education can be improved by constructing the opinion of media, which can provide a basis for the policy maker in the new media age.

A Noisy Opinion Formation Model with Two Opposing Mass Media

Hirofumi Takesue
Journal of Artificial Societies and Social Simulation 24 (4) 3

Kyeywords: Opinion Dynamics, Continuous Opinions, Noise, Mass Media
Abstract: Processes of individual attitude formation and their macroscopic consequences have become an intriguing research topic, and agent-based models of opinion formation have been proposed to understand this phenomenon. This study conducted an agent-based simulation and examined the role of mass media in a noisy opinion formation process, where opinion heterogeneity is preserved by a weak intensity of assimilation and errors accompanying opinion modifications. In a computational model, agents conformed to their neighbours' opinions in social networks. In addition, each agent tended to be influenced by one of a two external agents with fixed opinions, that is, mass media that take opposite positions on an opinion spectrum. The simulation results demonstrated that a small probability of interactions with mass media reduces opinion heterogeneity even with extreme mass media position values. However, a large frequency of interactions with mass media increases opinion heterogeneity. Accordingly, intermediate assimilation strength achieves the least heterogeneous opinion distribution. The influence of mass media dampens the effects of network topology. Our simulation implies that mass media can play qualitatively different roles depending on their positions and intensity of influence.

The Role of Mistrust in the Modelling of Opinion Adoption

Johnathan Adams, Gentry White and Robyn Araujo
Journal of Artificial Societies and Social Simulation 24 (4) 4

Kyeywords: Opinion Dynamics, Mistrust, Bayesian Update, Polarisation, Modelling
Abstract: Societies tend to partition into factions based on shared beliefs, leading to sectarian conflict in society. This paper investigates mistrust as a cause for this partitioning by extending an established opinion dynamics model with Bayesian updating that specifies mistrust as the underlying mechanism for disagreement and, ultimately, polarisation. We demonstrate that mistrust is at the foundation of polarisation. Detailed analysis and the results of rigorous simulation studies provide new insight into the potential role of mistrust in polarisation. We show that consensus results when mistrust levels are low, but introducing extreme agents makes consensus significantly harder to reach and highly fragmented and dispersed. These results also suggest a method to verify the model using real-world experimental or observational data empirically.

Cultural Dissemination: An Agent-Based Model with Social Influence

Ngan Nguyen, Hongfei Chen, Benjamin Jin, Walker Quinn, Conrad Tyler and Adam Landsberg
Journal of Artificial Societies and Social Simulation 24 (4) 5

Kyeywords: Cultural Dissemination, Agent-Based Modeling, Cultural Evolution, Opinion Dynamics, Cultural Transmission, Bounded Confidence Models
Abstract: We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod's original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.

Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics

Gregor Betz
Journal of Artificial Societies and Social Simulation 25 (1) 2

Kyeywords: Opinion Dynamics, Argumentation, Natural Language Processing, Language Model
Abstract: This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves — rather than with their mathematical representations (as in symbolic models). This paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Mäs & Flache 2013; Singer et al. 2019): First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from symbolic models. However, once ADAs start to actively generate new contributions, the evolution of a conversation is dominated by properties of the agents as authors. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research.

Arguments as Drivers of Issue Polarisation in Debates Among Artificial Agents

Felix Kopecky
Journal of Artificial Societies and Social Simulation 25 (1) 4

Kyeywords: Polarization, Opinion Dynamics, Argumentation Strategies, Arguments, Belief Systems
Abstract: Can arguments and their properties influence the development of issue polarisation in debates among artificial agents? This paper presents an agent-based model of debates with logical constraints based on the theory of dialectical structures. Simulations on this model reveal that the exchange of arguments can drive polarisation even without social influence, and that the usage of different argumentation strategies can influence the obtained levels of polarisation.

The Role of Reinforcement Learning in the Emergence of Conventions: Simulation Experiments with the Repeated Volunteer’s Dilemma

Hendrik Nunner, Wojtek Przepiorka and Chris Janssen
Journal of Artificial Societies and Social Simulation 25 (1) 7

Kyeywords: Conventions, Repeated Games, Volunteer’s Dilemma, Agent-Based Simulation, Reinforcement Learning, Cognitive Modeling
Abstract: We use reinforcement learning models to investigate the role of cognitive mechanisms in the emergence of conventions in the repeated volunteer’s dilemma (VOD). The VOD is a multi-person, binary choice collective goods game in which the contribution of only one individual is necessary and sufficient to produce a benefit for the entire group. Behavioral experiments show that in the symmetric VOD, where all group members have the same costs of volunteering, a turn-taking convention emerges, whereas in the asymmetric VOD, where one “strong” group member has lower costs of volunteering, a solitary-volunteering convention emerges with the strong member volunteering most of the time. We compare three different classes of reinforcement learning models in their ability to replicate these empirical findings. Our results confirm that reinforcement learning models can provide a parsimonious account of how humans tacitly agree on one course of action when encountering each other repeatedly in the same interaction situation. We find that considering contextual clues (i.e., reward structures) for strategy design (i.e., sequences of actions) and strategy selection (i.e., favoring equal distribution of costs) facilitate coordination when optima are less salient. Furthermore, our models produce better fits with the empirical data when agents act myopically (favoring current over expected future rewards) and the rewards for adhering to conventions are not delayed.