scholarly journals OPINION FORMATION MODELS BASED ON GAME THEORY

2007 ◽  
Vol 18 (09) ◽  
pp. 1377-1395 ◽  
Author(s):  
ALESSANDRO DI MARE ◽  
VITO LATORA
Keyword(s):  
Game Theory ◽  
Numerical Simulations ◽  
Opinion Dynamics ◽  
Strategic Game ◽  
Opinion Formation ◽  
Bounded Confidence ◽  
Formation Dynamics ◽  
Bounded Confidence Model

A way to simulate the basic interactions between two individuals with different opinions, in the context of strategic game theory, is proposed. Various games are considered, which produce different kinds of opinion formation dynamics. First, by assuming that all individuals (players) are equals, we obtain the bounded confidence model of continuous opinion dynamics proposed by Deffuant et al. In such a model a tolerance threshold is defined, such that individuals with difference in opinion larger than the threshold can not interact. Then, we consider that the individuals have different inclinations to change opinion and different abilities in convincing the others. In this way, we obtain the so-called "Stubborn individuals and Orators" (SO) model, a generalization of the Deffuant et al. model, in which the threshold tolerance is different for every couple of individuals. We explore, by numerical simulations, the dynamics of the SO model, and we propose further generalizations that can be implemented.

scholarly journals The Combination of Pairwise and Group Interactions Promotes Consensus in Opinion Dynamics

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoxuan Liu ◽  
Changwei Huang ◽  
Haihong Li ◽  
Qionglin Dai ◽  
Junzhong Yang
Keyword(s):  
Complex Systems ◽  
Numerical Simulations ◽  
Complete Graph ◽  
Group Interaction ◽  
Opinion Dynamics ◽  
Pairwise Interaction ◽  
Network Structures ◽  
Opinion Formation ◽  
Group Interactions ◽  
Bounded Confidence

In complex systems, agents often interact with others in two distinct types of interactions, pairwise interaction and group interaction. The Deffuant–Weisbuch model adopting pairwise interaction and the Hegselmann–Krause model adopting group interaction are the two most widely studied opinion dynamics. In this study, we propose a novel opinion dynamics by combining pairwise and group interactions for agents and study the effects of the combination on consensus in the population. In the model, we introduce a parameter α to control the weights of the two interactions in the dynamics. Through numerical simulations, we find that there exists an optimal α , which can lead to a highest probability of complete consensus and minimum critical bounded confidence for the formation of consensus. Furthermore, we show the effects of α on opinion formation by presenting the observations for opinion clusters. Moreover, we check the robustness of the results on different network structures and find the promotion of opinion consensus by α not limited to a complete graph.

BOUNDED CONFIDENCE MODEL IN COMPLEX NETWORKS

2013 ◽  
Vol 24 (08) ◽  
pp. 1350049 ◽  
Author(s):  
K. FELIJAKOWSKI ◽  
R. KOSINSKI
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Formation Process ◽  
Order Parameter ◽  
Hierarchical Network ◽  
Opinion Formation ◽  
Spreading Process ◽  
Bounded Confidence ◽  
Initial Source ◽  
Bounded Confidence Model

This paper presents a study of the bounded confidence model applied to the complex networks. Two different cases were examined: opinion formation process in the Barabási–Albert network and corruption spreading in a hierarchical network. For both cases, the value of the bounded confidence parameter ε was assumed as a constant, or its value was dependent on the degree of a node in the network. To measure the opinion formation and corruption spreading processes, we introduced the order parameter related to the number of interfaces in the system. As a results of numerical simulations, the influence of the values of ε on the final opinions in the population, as well as, the influence of an initial source of corruption in the company structure on the corruption spreading process, were obtained and discussed.

Measuring the role of two competing groups of informed agents in opinion formation

SIMULATION ◽  
2018 ◽  
Vol 95 (8) ◽  
pp. 753-766
Author(s):  
Kamal S Selim ◽  
Ahmed E Okasha ◽  
Fatma R Farag
Keyword(s):  
Opinion Dynamics ◽  
The Other ◽  
Social Characteristics ◽  
Opinion Formation ◽  
Agent Based ◽  
Bounded Confidence ◽  
Significant Difference ◽  
The Impact ◽  
Bounded Confidence Model

For politicians, to promote intended messages to different groups of individuals, they could employ strategic individuals called “informed agents.” The aim of this article is to explore and measure the impact of two competing groups of informed agents on opinion dynamics within a society exposed to two extreme opinions. Thus, an agent-based model is developed as an extension to the bounded confidence model by assuming the existence of two groups of informed agents. The impact of these agents with respect to their social characteristics, such as, their size in the society, how tolerant they are, their self-weight and attitudes about others’ opinions is explored. Different assumptions about the initial opinion distributions and their effect are also investigated. Due to the difficulty of observing a real society, social simulation experiments are constructed based on artificial societies.The simulations conducted resulted in some interesting findings. With no dominating group of the two informed agents, the society will be ended up concentrated around a moderate position. On the other hand, with significant difference between the two group sizes, the larger group will polarize the population towards its opinion. However, this conclusion will not apply if the population is skewed towards the other opinion. In such case, the larger group will only succeed to turn some of the society to be more moderate. In a society skewed towards extreme opinion, dominant informed agents adopting the other extreme will not be able to shift the society towards their opinion. Finally, in radical societies informed agents could turn most of the society to be extremists.

THE EFFECT OF IN-GROUP FAVORITISM ON THE COLLECTIVE BEHAVIOR OF INDIVIDUALS' OPINIONS

2015 ◽  
Vol 18 (01n02) ◽  
pp. 1550002 ◽  
Author(s):  
MEYSAM ALIZADEH ◽  
CLAUDIO CIOFFI-REVILLA ◽  
ANDREW CROOKS
Keyword(s):  
Collective Behavior ◽  
Cluster Formation ◽  
Opinion Dynamics ◽  
Agent Based ◽  
Global Consensus ◽  
Group Cooperation ◽  
Bounded Confidence ◽  
Rejection Mechanism ◽  
Group Members ◽  
Bounded Confidence Model

Empirical findings from social psychology show that sometimes people show favoritism toward in-group members in order to reach a global consensus, even against individuals' own preferences (e.g., altruistically or deontically). Here we integrate ideas and findings on in-group favoritism, opinion dynamics, and radicalization using an agent-based model entitled cooperative bounded confidence (CBC). We investigate the interplay of homophily, rejection, and in-group cooperation drivers on the formation of opinion clusters and the emergence of extremist, radical opinions. Our model is the first to explicitly explore the effect of in-group favoritism on the macro-level, collective behavior of opinions. We compare our model against the two-dimentional bounded confidence model with rejection mechanism, proposed by Huet et al. [Adv. Complex Syst.13(3) (2010) 405–423], and find that the number of opinion clusters and extremists is reduced in our model. Moreover, results show that group influence can never dominate homophilous and rejecting encounters in the process of opinion cluster formation. We conclude by discussing implications of our model for research on collective behavior of opinions emerging from individuals' interaction.

scholarly journals Dynamics of linguistic opinion formation in bounded confidence model

2016 ◽  
Vol 32 ◽  
pp. 52-61 ◽  
Author(s):  
Yucheng Dong ◽  
Xia Chen ◽  
Haiming Liang ◽  
Cong-Cong Li
Keyword(s):  
Opinion Formation ◽  
Bounded Confidence ◽  
Bounded Confidence Model

scholarly journals THE BOUNDED CONFIDENCE MODEL OF OPINION DYNAMICS

2012 ◽  
Vol 22 (02) ◽  
pp. 1150007 ◽  
Author(s):  
JAVIER GÓMEZ-SERRANO ◽  
CARL GRAHAM ◽  
JEAN-YVES LE BOUDEC
Keyword(s):  
Empirical Distribution ◽  
Mean Field ◽  
Sufficient Conditions ◽  
Opinion Dynamics ◽  
Initial Condition ◽  
Mean Field Limit ◽  
Bounded Confidence ◽  
Deterministic Solution ◽  
A Finite Group ◽  
Bounded Confidence Model

The bounded confidence model of opinion dynamics, introduced by Deffuant et al., is a stochastic model for the evolution of continuous-valued opinions within a finite group of peers. We prove that, as time goes to infinity, the opinions evolve globally into a random set of clusters too far apart to interact, and thereafter all opinions in every cluster converge to their barycenter. We then prove a mean-field limit result, propagation of chaos: as the number of peers goes to infinity in adequately started systems and time is rescaled accordingly, the opinion processes converge to i.i.d. nonlinear Markov (or McKean–Vlasov) processes; the limit opinion processes evolve as if under the influence of opinions drawn from its own instantaneous law, which are the unique solution of a nonlinear integro-differential equation of Kac type. This implies that the (random) empirical distribution processes converge to this (deterministic) solution. We then prove that, as time goes to infinity, this solution converges to a law concentrated on isolated opinions too far apart to interact, and identify sufficient conditions for the limit not to depend on the initial condition, and to be concentrated at a single opinion. Finally, we prove that if the equation has an initial condition with a density, then its solution has a density at all times, develop a numerical scheme for the corresponding functional equation, and show numerically that bifurcations may occur.

scholarly journals Modeling the Evolution of Ideological Landscapes through Opinion Dynamics

2020 ◽  
Author(s):  
Jan Lorenz
Keyword(s):  
Classical Model ◽  
Opinion Dynamics ◽  
European Social Survey ◽  
Model Parameters ◽  
Stylized Facts ◽  
Social Survey ◽  
Political Landscape ◽  
Bounded Confidence ◽  
Bounded Confidence Model

This paper explores the possibilities to explain the stylized facts of empirically observed ideological landscapes through the bounded confidence model of opinion dynamics. Empirically, left-right self-placements are often not normally distributed but have multiple peaks (e.g. extreme-left-center-right-extreme). Some stylized facts are extracted from histograms from the European Social Survey. In the bounded confidence model, agents repeatedly adjust their ideological position in their ideological neighborhood. As an extension of the classical model, agents sometimes completely reassesses their opinion depending on their ideological openness and their propensity for reassessment, respectively. Simulations show that this leads to the emergence of clustered ideological landscapes similar to the ones observed empirically. However, not all stylized facts of real world ideological landscapes can be reproduced with the model. Changes in the model parameters show that the ideological landscapes are susceptible to interesting slow and abrupt changes.A long term goal is to integrate models of opinion dynamics into the classical spatial model of electoral competition as a dynamic element taking into account that voters themselves shape the political landscape by adjusting their positions and preferences through interaction.

scholarly journals A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

2022 ◽  
Vol 21 (1) ◽  
pp. 1-32
Author(s):  
Abigail Hickok ◽  
Yacoub Kureh ◽  
Heather Z. Brooks ◽  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Opinion Dynamics ◽  
Bounded Confidence ◽  
Bounded Confidence Model

scholarly journals An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

2021 ◽  
Author(s):  
Unchitta Kan ◽  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Social Networks ◽  
Early Time ◽  
Opinion Dynamics ◽  
Convergence Time ◽  
Confidence Bound ◽  
Neighboring Node ◽  
Popular Approach ◽  
Time Dynamics ◽  
Bounded Confidence ◽  
Bounded Confidence Model

Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC model, by studying opinion dynamics that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinion when they interact with a neighboring node and (2) break a connection with a neighbor based on an opinion tolerance threshold and then form a new connection to a node following the principle of homophily. This opinion tolerance threshold acts as a threshold to determine if the opinions of adjacent nodes are sufficiently different to be viewed as discordant. We find that our adaptive BC model requires a larger confidence bound than the standard DW model for the nodes of a network to achieve a consensus. Interestingly, our model includes regions with `pseudo-consensus' steady states, in which there exist two subclusters within an opinion-consensus group that deviate from each other by a small amount. We conduct extensive numerical simulations of our adaptive BC model and examine the importance of early-time dynamics and nodes with initial moderate opinions for achieving consensus. We also examine the effects of coevolution on the convergence time of the dynamics.

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