Clustering and asymptotic behavior in opinion formation

Abstract We investigate the long time behavior of models of opinion formation. We consider the case of compactly supported interactions between agents which are also non-symmetric, including for instance the so-called Krause model. Because of the finite range of interaction, convergence to a unique consensus is not expected in general. We are nevertheless able to prove the convergence to a final equilibrium state composed of possibly several local consensus. This result had so far only been conjectured through numerical evidence. Because of the non-symmetry in the model, the analysis is delicate and is performed in two steps: First using entropy estimates to prove the formation of stable clusters and then studying the evolution in each cluster. We study both discrete and continuous in time models and give rates of convergence when those are available.

[1]  Adrian Carro,et al.  The Role of Noise and Initial Conditions in the Asymptotic Solution of a Bounded Confidence, Continuous-Opinion Model , 2012, Journal of Statistical Physics.

[2]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[3]  Nicolas Lanchier,et al.  Stochastic Dynamics on Hypergraphs and the Spatial Majority Rule Model , 2013, 1301.0151.

[4]  P. Markowich,et al.  Boltzmann and Fokker–Planck equations modelling opinion formation in the presence of strong leaders , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  N. Lanchier The Axelrod model for the dissemination of culture revisited. , 2010, 1004.0365.

[6]  John N. Tsitsiklis,et al.  On the 2R conjecture for multi-agent systems , 2007, 2007 European Control Conference (ECC).

[7]  P. Krapivsky,et al.  Coarsening and persistence in the voter model. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  Jan Lorenz,et al.  Continuous Opinion Dynamics under Bounded Confidence: A Survey , 2007, 0707.1762.

[9]  J.N. Tsitsiklis,et al.  Convergence in Multiagent Coordination, Consensus, and Flocking , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[10]  S. Redner,et al.  Unity and discord in opinion dynamics , 2003 .

[11]  E. Ben-Naim,et al.  Bifurcations and patterns in compromise processes , 2002, cond-mat/0212313.

[12]  E. Ben-Naim Opinion dynamics: Rise and fall of political parties , 2004, cond-mat/0411427.

[13]  Claudio Canuto,et al.  An Eulerian Approach to the Analysis of Krause's Consensus Models , 2012, SIAM J. Control. Optim..

[14]  Eitan Tadmor,et al.  A New Model for Self-organized Dynamics and Its Flocking Behavior , 2011, 1102.5575.

[15]  Haoxiang Xia,et al.  Opinion Dynamics: A Multidisciplinary Review and Perspective on Future Research , 2011, Int. J. Knowl. Syst. Sci..

[16]  M. Degroot Reaching a Consensus , 1974 .

[17]  Guillaume Deffuant,et al.  Meet, discuss, and segregate! , 2002, Complex..

[18]  John N. Tsitsiklis,et al.  On Krause's Multi-Agent Consensus Model With State-Dependent Connectivity , 2008, IEEE Transactions on Automatic Control.

[19]  U. Krause A DISCRETE NONLINEAR AND NON–AUTONOMOUS MODEL OF CONSENSUS FORMATION , 2007 .

[20]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[21]  Sébastien Motsch,et al.  Heterophilious Dynamics Enhances Consensus , 2013, SIAM Rev..