Opinion dynamics in networks with bounded confidence and influence

We study a discrete-time model of opinion dynamics in which each agent has its own confidence and influence radius. We investigate the influence of heterogeneity in confidence/influence distribution on the behavior of the network. We consider three cases: 1) each agent has the same confidence and influence radius; 2) each agent's confidence radius is inversely proportional to its influence radius; 3) each agent's confidence radius is not related to its influence radius. We find that heterogeneity does not always promote consensus, and there is an optimal heterogeneity so that the size of the largest consensus cluster reaches maximum.

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

[2]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[3]  P Mishra.  Семантические особенности английского глагола meet , 2012 .

[4]  Ιωαννησ Τσιτσικλησ,et al.  PROBLEMS IN DECENTRALIZED DECISION MAKING AND COMPUTATION , 1984 .

[5]  Antoine Girard,et al.  Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs , 2009, IEEE Transactions on Automatic Control.

[6]  John N. Tsitsiklis,et al.  Continuous-Time Average-Preserving Opinion Dynamics with Opinion-Dependent Communications , 2009, SIAM J. Control. Optim..

[7]  M. Newman.  Power laws, Pareto distributions and Zipf's law , 2005 .

[8]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[9]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[10]  Paolo Frasca,et al.  Continuous-time Discontinuous Equations in Bounded Confidence Opinion Dynamics , 2010, ArXiv.

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

[12]  Wen Yang,et al.  Consensus in a heterogeneous influence network. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Andrew T. Hartnett,et al.  This PDF file includes: Materials and Methods SOM Text Figs. S1 to S12 Table S1 Full Reference List , 2022 .

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

[15]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[16]  Brian D. O. Anderson,et al.  Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events , 2008, SIAM J. Control. Optim..

[17]  John N. Tsitsiklis,et al.  Convergence Speed in Distributed Consensus and Averaging , 2009, SIAM J. Control. Optim..

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