55 articles matched your search for
Extremism, Opinion Dynamics, Bounded Confidence, Clustering, Anti-Conformism
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
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.
Journal of Artificial Societies and Social Simulation 8 (2) 4
Kyeywords: Agent-Based Modelling, Luhmann Economy, Fuzzy Clustering
Abstract: The core of this work is the definition of an agent-based model for a simple Luhmann economy based on publications of Niklas Luhmann. • Using an implementation on a default personal computer the behaviour of the model is studied when assumptions regarding initial conditions are made. Fuzzy-c-means clustering is used as visualisation aid. The impact of the observation horizon (a model parameter determining how far agents can see) is studied interactively. • Solution paths of the Luhmann economy originating from an initial endowment to equilibrium (when the economy settles down) are studied. • The impact of model parameters determining the unevenness regarding the initial distribution of wealth is studied by Monte Carlo simulation. Niklas Luhmann\'s hypothesis, that the economy starts from and produces further inequality in order to continue (see Luhmann 1988, p. 112) could be reproduced by computer simulation. The main characteristic of the approach is the consideration of the cohesive structure of communication (i.e. one communicative act - many understanding observers) also prominent in (Dunbar 1996, pp. 192-207). The model gives directions how to model further aspects of Niklas Luhmann\'s theory.
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.
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.
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.
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.
Rainer Hegselmann and Ulrich Krause
Journal of Artificial Societies and Social Simulation 9 (3) 10
Kyeywords: Opinion Dynamics, Consensus/dissent, Bounded Confidence, Truth, Social Epistemology
Abstract: The paper analyzes the chances for the truth to be found and broadly accepted under conditions of cognitive division of labour combined with a social exchange process. Cognitive division of labour means, that only some individuals are active truth seekers, possibly with different capacities. The social exchange process consists in an exchange of opinions between all individuals, whether truth seekers or not. We de- velop a model which is investigated by both, mathematical tools and computer simulations. As an analytical result the Funnel theorem states that under rather weak conditions on the social process a consensus on the truth will be reached if all individuals posses an arbitrarily small inclination for truth seeking. The Leading the pack theorem states that under certain conditions even a single truth seeker may lead all individuals to the truth. Systematic simulations analyze how close and how fast groups can get to the truth depending on the frequency of truth seekers, their capacities as truth seekers, the position of the truth (more to the extreme or more in the centre of an opinion space), and the willingness to take into account the opinions of others when exchanging and updating opinions. A tricky movie visualizes simulations results in a parameter space of higher dimensions.
Journal of Artificial Societies and Social Simulation 9 (3) 8
Kyeywords: Continuous Opinion, Extremism, Convergence Pattern
Abstract: We compare patterns of extremism propagation yielded by 4 continuous opinion models, when the main parameters vary, on different types of networks (total connection, random network, lattice). In two models the individuals take into account the uncertainty of their interlocutor, and they show similar patterns, with a higher probability of double extreme convergence than in the other couple of models (in which the interlocutor\'s uncertainty is not taken into account). The addition of noise does not change significantly the results, except that it favours the single extreme convergence in some models. The lattice topology of interactions provides results which are significantly different from the ones obtained with a random network of similar connection density. We identify 3 typical behaviours with a single initial extremist, which help to explain the different results. In particular, we observe that the single extreme convergence is favoured by small shortest paths between all pairs of nodes in the network.
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.
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.
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.
Patrick Groeber, Frank Schweitzer and Kerstin Press
Journal of Artificial Societies and Social Simulation 12 (2) 4
Kyeywords: Social Norms, Conventions, Bounded Confidence, Dynamic Networks
Abstract: A local culture denotes a set of rules on business behaviour among firms in a cluster. Similar to social norms or conventions, it is an emergent feature of interaction in an economic network. To model its emergence, we consider a distributed agent population, representing cluster firms. Further, we build on a continuous opinion dynamics model with bounded confidence (ε), which assumes that two agents only interact if differences in their behaviour are less than ε. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. Two aspects extend this framework: (i) The agent\'s in-group consisting of acquainted interaction partners is explicitly taken into account, leading to an effective agent behaviour as agents try to continue to interact with past partners and thus seek to stay sufficiently close to them. (ii) The in-group network structure changes over time, as agents form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, the model introduces a feedback mechanism of agent behaviour and in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low ε) the feedback mechanism helps find consensus more often, whereas for open-minded agents (high ε) this does not necessarily hold. Overall, the dynamics of agent interaction in clusters as modelled here, are conducive to consensus among all or a majority of agents.
Jennifer Badham and Rob Stocker
Journal of Artificial Societies and Social Simulation 13 (1) 11
Kyeywords: Social Networks, Network Generation, Clustering Coefficient, Assortativity
Abstract: Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.
Journal of Artificial Societies and Social Simulation 13 (3) 2
Kyeywords: Prisoner\'s Dilemma Game, Tags, Parochial Cooperation, Clustering, Small-World-Ness, NetLogo
Abstract: Researchers from many disciplines have been interested in the maintenance of cooperation in animal and human societies using the Prisoner\'s Dilemma game. Recent studies highlight the roles of cognitively simple agents in the evolution of cooperation who read tags to interact either discriminately or selectively with tolerably similar partners. In our study on a one-shot Prisoner\'s Dilemma game, artificial agents with tags and tolerance perceive dissimilarities to local neighbors to cooperate with in-group and otherwise defect. They imitate tags and learn tolerance from more successful neighbors. In terms of efficiency, society-wide cooperation can evolve even when the benefits of cooperation are relatively low. Meanwhile, tolerance however decreases as agents become homogenized. In terms of stability, parochial cooperators are gullible to the deviants defectors displaying tolerably similar tags. We find that as the benefits of cooperation increase and the dimensions of tag space become larger, emergent societies can be more tolerant towards heterogeneous others. We also identify the effects of clustering and small-world-ness on the dynamics of tag-based parochial cooperation in spite of its fundamental vulnerability to those deviants regardless of network topology. We discuss the issue of tag changeability in search for alternative societies in which tag-based parochial cooperation is not only efficient but also robust.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Thomas Moore, Patrick Finley, Nancy Brodsky, Theresa Brown, Benjamin Apelberg, Bridget Ambrose and Robert Glass
Journal of Artificial Societies and Social Simulation 18 (2) 7
Kyeywords: Opinion Dynamics, Social Networks, Media, Advertising
Abstract: We present a modified Deffuant-Weisbuch opinion dynamics model that integrates the influence of media campaigns on opinion. Media campaigns promote messages intended to inform and influence the opinions of the targeted audiences through factual and emotional appeals. Media campaigns take many forms: brand-specific advertisements, promotions, and sponsorships, political, religious, or social messages, and public health and educational communications. We illustrate model-based analysis of campaigns using tobacco advertising and public health education as examples. In this example, “opinion” is not just an individual’s attitude towards smoking, but the integration of a wide range of factors that influence the likelihood that an individual will decide to smoke, such as knowledge, perceived risk, perceived utility and affective evaluations of smoking. This model captures the ability of a media campaign to cause a shift in network-level average opinion, and the inability of a media message to do so if it promotes too extreme a viewpoint for a given target audience. Multiple runs displayed strong heterogeneity in response to media campaigns as the difference between network average initial opinion and broadcasted media opinion increased, with some networks responding ideally and others being largely unaffected. In addition, we show that networks that display community structure can be made more susceptible to be influenced by a media campaign by a complementary campaign focused on increasing tolerance to other opinions in targeted nodes with high betweenness centrality. Similarly, networks can be “inoculated” against advertising campaigns by a media campaign that decreases tolerance.
Journal of Artificial Societies and Social Simulation 18 (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.
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 condence 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.
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.
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.
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.
Patryk Siedlecki, Janusz Szwabiński and Tomasz Weron
Journal of Artificial Societies and Social Simulation 19 (4) 9
Kyeywords: Opinion Dynamics, Social Influence, Conformity, Anticonformity, Bi-Polarization, Agent-Based Modelling
Abstract: Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agent-based model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al, 2009b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a bi-polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction L of negative cross-links between cliques. In the regime of small values of L the system is able to reach the total positive consensus. If the values of L are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a sufficient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is required to arrive at a polarized steady state within our model.
Journal of Artificial Societies and Social Simulation 20 (1) 13
Kyeywords: Agent-Based Model, Opinion Dynamics, Social Networks, Conformity, Polarization, Extremism
Abstract: Agent-based models are versatile tools for studying how societal opinion change, including political polarization and cultural diffusion, emerges from individual behavior. This study expands agents' psychological realism using empirically-motivated rules governing interpersonal influence, commitment to previous beliefs, and conformity in social contexts. Computational experiments establish that these extensions produce three novel results: (a) sustained strong diversity of opinions within the population, (b) opinion subcultures, and (c) pluralistic ignorance. These phenomena arise from a combination of agents' intolerance, susceptibility and conformity, with extremist agents and social networks playing important roles. The distribution and dynamics of simulated opinions reproduce two empirical datasets on Americans' political opinions.
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.
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.
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.
Andreas Flache, Michael Mäs, Thomas Feliciani, Edmund Chattoe-Brown, Guillaume Deffuant, Sylvie Huet and Jan Lorenz
Journal of Artificial Societies and Social Simulation 20 (4) 2
Kyeywords: Social Influence, Opinion Dynamics, Polarization, Calibration and Validation, Micro-Macro Link
Abstract: In 1997, Robert Axelrod wondered in a highly influential paper "If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?" Axelrod’s question highlighted an ongoing quest for formal theoretical answers joined by researchers from a wide range of disciplines. Numerous models have been developed to understand why and under what conditions diversity in beliefs, attitudes and behavior can co-exist with the fact that very often in interactions, social influence reduces differences between people. Reviewing three prominent approaches, we discuss the theoretical ingredients that researchers added to classic models of social influence as well as their implications. Then, we propose two main frontiers for future research. First, there is urgent need for more theoretical work comparing, relating and integrating alternative models. Second, the field suffers from a strong imbalance between a proliferation of theoretical studies and a dearth of empirical work. More empirical work is needed testing and underpinning micro-level assumptions about social influence as well as macro-level predictions. In conclusion, we discuss major roadblocks that need to be overcome to achieve progress on each frontier. We also propose that a new generation of empirically-based computational social influence models can make unique contributions for understanding key societal challenges, like the possible effects of social media on societal polarization.
Annalisa Stefanelli and Roman Seidl
Journal of Artificial Societies and Social Simulation 20 (4) 3
Kyeywords: Agent-Based Model, Arguments, Opinion Dynamics, Social Judgment
Abstract: The effect of social interactions on how opinions are developed and changed over time is crucial to public processes that involve citizens and their points of view. In this opinion dynamics exercise, we address the topic of nuclear waste repositories in Switzerland and suggest a more realistic investigation of public opinion using agent-based modeling in combination with empirical data and sociopsychological theory. Empirical data obtained from an online questionnaire (N = 841) is used for the initialization of the model, whose agents directly represent the participants. We use social judgment theory (SJT) to describe how opinions can be adapted during social interactions, including through mechanisms of contrast and assimilation. Furthermore, we focus on the definition of “opinion” itself, claiming that working with disaggregated opinions (i.e., arguments) can play a determining role if one aims to capture real-world mechanisms of opinion dynamics. Simulation results show different patterns for the three different argument categories used for this specific topic (i.e., risk, benefit, and process), suggesting a mutual influence between an individual’s initial knowledge and evaluations and an individual’s social dynamics and opinion changes. The importance of content-related and empirical information, as well as the theory and mechanisms used in the social simulation, are discussed.
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.
Paolo Zeppini and Koen Frenken
Journal of Artificial Societies and Social Simulation 21 (3) 1
Kyeywords: Clustering, Diffusion, Efficiency, Phase Transition, Small-World Network, Welfare
Abstract: Understanding diffusion processes is key to market strategies as well as innovation and sustainability policies. In promoting new products and technologies, firms and governments need to understand the conditions favouring successful spread of these products. We propose a generic diffusion model based on percolation theory. Our reference is a new product diffusion in a social network through word-of-mouth. Given that consumers differ in their reservation prices, a critical price exists that defines a phase transition from a no-diffusion to a diffusion regime. As consumer surplus is maximised just below a product’s critical price, one can systematically compare the economic efficiency of network structures by investigating their critical price. Networks with low clustering were the most efficient, because clustering leads to redundant information flows hampering effective product diffusion. We further showed that the more equal a society, the more efficient the diffusion process.
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.
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.
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.
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.
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.
Masanori Hirano, Kiyoshi Izumi, Hiroyasu Matsushima and Hiroki Sakaji
Journal of Artificial Societies and Social Simulation 23 (3) 6
Kyeywords: Artificial Market, Multi-Agent Simulation, Data-Mining, High-Frequency Trade, Market-Making, Clustering
Abstract: Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-trader market-making (HFT-MM) strategy from both the real financial market and our simulation. Regarding the former, we extracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the difference between these traders' orders and the market price. In our simulation, we built an artificial market model with a continuous double auction system, stylized trader agents, and HFT-MM trader agents based on prior research. As an experiment, we compared the distribution of the order placements of HFT-MM traders in the real and simulated financial data. We found that the order placement distribution near the market or best price in both the real data and the simulations were similar. However, the orders far from the market or best price differed significantly when the real data exhibited a wider range of orders. This indicates that in order to build more realistic simulation of financial markets, integrating fine-grained data is essential.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.