A Rejection Mechanism in 2d Bounded Confidence Provides More Conformity

Huet, Sylvie; Deffuant, Guillaume; Jager, Wander

doi:10.1142/s0219525908001799

Cited by 67 publications

(86 citation statements)

References 27 publications

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“…However, af-filiation differs from both opinion and culture in several pertinent ways: opinion dynamics models typically consider a single issue, about which individuals may express one of two or more opinions (the model described in [19] is a notable exception, looking at interactions between two issues). In reality, individuals belong to multiple groups simultaneously.…”

Section: Non-exclusive Group Model: Assumptionsmentioning

confidence: 99%

Competition and the Dynamics of Group Affiliation

Geard

Bullock

2010

Advs. Complex Syst.

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How can we understand the interaction between the social network topology of a population and the patterns of group affiliation in that population? Each aspect influences the other: social networks provide the conduits via which groups recruit new members, and groups provide the context in which new social ties are formed. Given that the resources of individuals are finite, groups can be considered to compete with one another for the time and energy of their members. Such competition is likely to have an impact on the way in which social structure and group affiliation co-evolve. While many social simulation models exhibit group formation as a part of their behaviour (e.g., opinion clusters or converged cultures), models that explicitly focus on group affiliation are less common. We describe and explore the behaviour of a model in which, distinct from most current models, individual nodes can belong to multiple groups simultaneously. By varying the capacity of individuals to belong to groups, and the costs associated with group membership, we explore the effect of different levels of competition on population structure and group dynamics.

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Section: Non-exclusive Group Model: Assumptionsmentioning

confidence: 99%

Competition and the Dynamics of Group Affiliation

Geard

Bullock

2010

Advs. Complex Syst.

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“…This model is based on the concept of bounded confidence: two individuals having their opinion less far than a threshold are going to have closer opinion after the interaction. Some different implementations of this model taking into account a rejection process [20] or a different type of tolerance threshold connected to the opinion [12] have been proposed in the last years. In our model we incorporate the Deffuant model to describe the opinion dynamics contact process.…”

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confidence: 99%

Opinion dynamics in a group-based society

Gargiulo¹,

Huet²

2010

EPL

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Many models have been proposed to analyze the evolution of opinion structure due to the interaction of individuals in their social environment. Such models analyze the spreading of ideas both in completely interacting backgrounds and on social networks, where each person has a finite set of interlocutors. In this paper we analyze the reciprocal feedback between the opinions of the individuals and the structure of the interpersonal relationships at the level of community structures. For this purpose we define a group-based random network and we study how this structure co-evolves with opinion dynamics processes. We observe that the adaptive network structure affects the opinion dynamics process helping the consensus formation. The results also show interesting behaviors in regards to the size distribution of the groups and their correlation with opinion structure.

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“…Our model is very different from that of Huet et al [16] since theirs is based on the repeated interaction of randomly picked pairs of agents whose responses are based on their opinion differences on two issues. Also, the most important difference between our model in the above form and the J-A Model model [15] as well as its extension by Crawford et al [27] is that we use different convergence and divergence parameters µ and λ; while they set both to the same value.…”

Section: The Modelmentioning

confidence: 95%

“…Note that both the models of Jager and Amblard [15] and Crawford et al [27] involved agents with opinions on one issue only. In contrast, the model of Huet et al [16] studied the response of agents based on their opinions on two issues by Monte Carlo simulation up to 5000 agents.…”

Section: Introductionmentioning

confidence: 99%

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Social judgment theory based model on opinion formation, polarization and evolution

Chau

Wong

Chow

et al. 2014

Physica A: Statistical Mechanics and its Applications

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The dynamical origin of opinion polarization in the real world is an interesting topic physical scientists may help to understand. To properly model the dynamics, the theory must be fully compatible with findings by social psychologists on microscopic opinion change. Here we introduce a generic model of opinion formation with homogeneous agents based on the well-known social judgment theory in social psychology by extending a similar model proposed by Jager and Amblard. The agents' opinions will eventually cluster around extreme and/or moderate opinions forming three phases in a two-dimensional parameter space that describes the microscopic opinion response of the agents. The dynamics of this model can be qualitatively understood by mean-field analysis. More importantly, first-order phase transition in opinion distribution is observed by evolving the system under a slow change in the system parameters, showing that punctuated equilibria in public opinion can occur even in a fully connected social network.

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A Rejection Mechanism in 2d Bounded Confidence Provides More Conformity

Cited by 67 publications

References 27 publications

Competition and the Dynamics of Group Affiliation

Competition and the Dynamics of Group Affiliation

Opinion dynamics in a group-based society

Social judgment theory based model on opinion formation, polarization and evolution

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