A Survey on Nonstrategic Models of Opinion Dynamics

The paper presents a survey on selected models of opinion dynamics. Both discrete (more precisely, binary) opinion models as well as continuous opinion models are discussed. We focus on frameworks that assume non-Bayesian updating of opinions. In the survey, a special attention is paid to modeling nonconformity (in particular, anticonformity) behavior. For the case of opinions represented by a binary variable, we recall the threshold model, the voter and q-voter models, the majority rule model, and the aggregation framework. For the case of continuous opinions, we present the DeGroot model and some of its variations, time-varying models, and bounded confidence models.

Download Full-text

CONTINUOUS OPINION DYNAMICS UNDER BOUNDED CONFIDENCE: A SURVEY

International Journal of Modern Physics C ◽

10.1142/s0129183107011789 ◽

2007 ◽

Vol 18 (12) ◽

pp. 1819-1838 ◽

Cited By ~ 334

Author(s):

JAN LORENZ

Keyword(s):

Social Psychology ◽

Homogeneous Model ◽

Opinion Dynamics ◽

Complexity Science ◽

Bifurcation Diagrams ◽

Agent Based ◽

Open Questions ◽

Bounded Confidence ◽

Cluster Configuration ◽

Continuous Opinions

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.

Download Full-text

Noise-based synchronization of bounded confidence opinion dynamics in heterogeneous time-varying communication networks

Information Sciences ◽

10.1016/j.ins.2020.04.018 ◽

2020 ◽

Vol 528 ◽

pp. 219-230

Author(s):

Wei Su ◽

Xueqiao Wang ◽

Ge Chen ◽

Yongguang Yu ◽

Tarik Hadzibeganovic

Keyword(s):

Communication Networks ◽

Opinion Dynamics ◽

Time Varying ◽

Bounded Confidence

Download Full-text

Simultaneous inference for time-varying models

Journal of Econometrics ◽

10.1016/j.jeconom.2021.03.002 ◽

2021 ◽

Author(s):

Sayar Karmakar ◽

Stefan Richter ◽

Wei Biao Wu

Keyword(s):

Simultaneous Inference ◽

Time Varying ◽

Time Varying Models

Download Full-text

Practical consensus in bounded confidence opinion dynamics

Automatica ◽

10.1016/j.automatica.2021.109683 ◽

2021 ◽

Vol 129 ◽

pp. 109683

Author(s):

Francesco Vasca ◽

Carmela Bernardo ◽

Raffaele Iervolino

Keyword(s):

Opinion Dynamics ◽

Bounded Confidence

Download Full-text

Modal factorization of time-varying models for nonlinear circuits by the Riccati transform

ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196) ◽

10.1109/iscas.2001.921370 ◽

2002 ◽

Cited By ~ 5

Author(s):

P. van der Kloet ◽

F.L. Neerhoff

Keyword(s):

Nonlinear Circuits ◽

Time Varying ◽

Time Varying Models

Download Full-text

Stochastic online learning under unknown time-varying models

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR) ◽

10.1109/acssc.2012.6489178 ◽

2012 ◽

Cited By ~ 1

Author(s):

Pouya Tehrani ◽

Qing Zhao

Keyword(s):

Online Learning ◽

Time Varying ◽

Time Varying Models

Download Full-text

Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks

Environmental Modelling & Software ◽

10.1016/j.envsoft.2014.01.021 ◽

2014 ◽

Vol 55 ◽

pp. 107-119 ◽

Cited By ~ 23

Author(s):

Vincent Wolfs ◽

Patrick Willems

Keyword(s):

Neural Networks ◽

Time Varying ◽

Model Trees ◽

Time Varying Models

Download Full-text

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

Complexity ◽

10.1155/2021/4382836 ◽

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.

Download Full-text