Opinion Dynamics in Social Networks With Hostile Camps: Consensus vs. Polarization

Most of the distributed protocols for multi-agent consensus assume that the agents are mutually cooperative and “trustful,” and so the couplings among the agents bring the values of their states closer. Opinion dynamics in social groups, however, require beyond these conventional models due to ubiquitous competition and distrust between some pairs of agents, which are usually characterized by repulsive couplings and may lead to clustering of the opinions. A simple yet insightful model of opinion dynamics with both attractive and repulsive couplings was proposed recently by C. Altafini, who examined first-order consensus algorithms over static signed graphs. This protocol establishes modulus consensus, where the opinions become the same in modulus but may differ in signs. In this paper, we extend the modulus consensus model to the case where the network topology is an arbitrary time-varying signed graph and prove reaching modulus consensus under mild sufficient conditions of uniform connectivity of the graph. For cut-balanced graphs, not only sufficient, but also necessary conditions for modulus consensus are given.

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

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

[3]  Robert D. Kleinberg,et al.  Continuous-time model of structural balance , 2010, Proceedings of the National Academy of Sciences.

[4]  Guangming Xie,et al.  Forming Circle Formations of Anonymous Mobile Agents With Order Preservation , 2013, IEEE Transactions on Automatic Control.

[5]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[6]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[7]  A. Matveev,et al.  Estimation and Control over Communication Networks , 2008 .

[8]  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..

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

[10]  Alexey S. Matveev,et al.  Stability of continuous-time consensus algorithms for switching networks with bidirectional interaction , 2013, 2013 European Control Conference (ECC).

[11]  Pavel Yu. Chebotarev,et al.  The Forest Consensus Theorem , 2013, IEEE Transactions on Automatic Control.

[12]  Karl Henrik Johansson,et al.  The Role of Persistent Graphs in the Agreement Seeking of Social Networks , 2011, IEEE Journal on Selected Areas in Communications.

[13]  Claudio Altafini,et al.  Consensus Problems on Networks With Antagonistic Interactions , 2013, IEEE Transactions on Automatic Control.

[14]  M. Macy,et al.  Small Worlds and Cultural Polarization , 2011 .

[15]  Hal L. Smith Systems of ordinary differential equations which generate an order preserving flow. A survey of results , 1988 .

[16]  A. A. Lumsdaine Communication and persuasion , 1954 .

[17]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[18]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.

[19]  Rodolphe Sepulchre,et al.  Synchronization in networks of identical linear systems , 2009, Autom..

[20]  Yongcan Cao,et al.  Distributed Coordination of Multi-agent Networks , 2011 .

[21]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[22]  Ming Cao,et al.  Clustering in diffusively coupled networks , 2011, Autom..

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

[24]  P. Chebotarev,et al.  On of the Spectra of Nonsymmetric Laplacian Matrices , 2004, math/0508176.

[25]  S. Fiske,et al.  Social Psychology , 2019, Definitions.

[26]  Ming Cao,et al.  Opinion dynamics using Altafini's model with a time-varying directed graph , 2014, 2014 IEEE International Symposium on Intelligent Control (ISIC).

[27]  Hongwei Zhang,et al.  Bipartite Consensus of Linear Multi-Agent Systems Over Signed Digraphs: An Output Feedback Control Approach , 2014 .

[28]  F. Clarke Generalized gradients and applications , 1975 .

[29]  John N. Tsitsiklis,et al.  Convergence of Type-Symmetric and Cut-Balanced Consensus Seeking Systems , 2011, IEEE Transactions on Automatic Control.

[30]  Karl Henrik Johansson,et al.  Structural Balance and Opinion Separation in Trust–Mistrust Social Networks , 2016, IEEE Transactions on Control of Network Systems.

[31]  Maria Elena Valcher,et al.  On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions , 2014, Syst. Control. Lett..

[32]  Manfredi Maggiore,et al.  State Agreement for Continuous-Time Coupled Nonlinear Systems , 2007, SIAM J. Control. Optim..

[33]  Jiangping Hu,et al.  Adaptive bipartite consensus on coopetition networks , 2015 .

[34]  W. Zheng,et al.  Emergent collective behaviors on coopetition networks , 2014 .

[35]  David Lee,et al.  Biased assimilation, homophily, and the dynamics of polarization , 2012, Proceedings of the National Academy of Sciences.

[36]  Karl Henrik Johansson,et al.  Robust Consensus for Continuous-Time Multiagent Dynamics , 2013, SIAM J. Control. Optim..

[37]  Manfredi Maggiore,et al.  Necessary and sufficient graphical conditions for formation control of unicycles , 2005, IEEE Transactions on Automatic Control.

[38]  Frank Allgöwer,et al.  Consensus in Multi-Agent Systems With Coupling Delays and Switching Topology , 2011, IEEE Transactions on Automatic Control.

[39]  A. V. Savkin,et al.  Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance , 2014, Robotica.

[40]  Hai-Tao Zhang,et al.  Swarming behaviors in multi-agent systems with nonlinear dynamics. , 2013, Chaos.

[41]  Julien M. Hendrickx,et al.  A lifting approach to models of opinion dynamics with antagonisms , 2014, 53rd IEEE Conference on Decision and Control.

[42]  S Redner,et al.  Dynamics of social balance on networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Ziyang Meng,et al.  Modulus Consensus over Networks with Antagonistic Interactions and Switching Topologies , 2014, 1402.2766.

[44]  J. Dillard,et al.  On the Nature of Reactance and its Role in Persuasive Health Communication , 2005 .

[45]  L. Moreau,et al.  Stability of continuous-time distributed consensus algorithms , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[46]  Anna Semakova,et al.  Decentralized multi-agent tracking of unknown environmental level sets by a team of nonholonomic robots , 2014, 2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).

[47]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[48]  Alexey S. Matveev,et al.  Consensus and polarization in Altafini's model with bidirectional time-varying network topologies , 2014, 53rd IEEE Conference on Decision and Control.

[49]  E. J. McShane,et al.  On Filippov’s implicit functions lemma , 1967 .

[50]  Randal W. Beard,et al.  Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications , 2007, Communications and Control Engineering.

[51]  Claudio Altafini,et al.  Predictable Dynamics of Opinion Forming for Networks With Antagonistic Interactions , 2015, IEEE Transactions on Automatic Control.

[52]  Claudio Altafini,et al.  Dynamics of Opinion Forming in Structurally Balanced Social Networks , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).