Social learning on networks with community structure

Almost all existing social learning models assume that there is only one type of agents in the society in order to avoid identification problem. In this work, we assume that there are various types of agents according to the communities they locate in. We design the rule of weight adjustment and testify that the updating rule with weight adjustment ensures learning on the whole social network. Furthermore, we show that how convergence speed is influenced by two updating-relevant parameters, and present instruction on how to attain the optimal social learning efficiency.

[1]  Ilan Lobel,et al.  Lower bounds on the rate of learning in social networks , 2009, 2009 American Control Conference.

[2]  Sanjeev Goyal,et al.  Learning from Neighbors , 1995 .

[3]  P. DeMarzo,et al.  Persuasion Bias, Social Influence, and Uni-Dimensional Opinions , 2001 .

[4]  P. DeMarzo,et al.  Persuasion Bias, Social Influence, and Uni-Dimensional Opinions , 2001 .

[5]  Drew Fudenberg,et al.  Word-of-mouth learning , 2004, Games Econ. Behav..

[6]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[7]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[8]  Alvaro Sandroni,et al.  Learning under social influence , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[9]  S. Goyal,et al.  Learning from neighbours , 1998 .

[10]  Ilan Lobel,et al.  BAYESIAN LEARNING IN SOCIAL NETWORKS , 2008 .

[11]  Asuman E. Ozdaglar,et al.  Convergence of rule-of-thumb learning rules in social networks , 2008, 2008 47th IEEE Conference on Decision and Control.

[12]  Douglas Gale,et al.  Bayesian learning in social networks , 2003, Games Econ. Behav..

[13]  Glenn Ellison,et al.  Rules of Thumb for Social Learning , 1993, Journal of Political Economy.

[14]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

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

[16]  S. Goyal,et al.  Conformism and diversity under social learning , 2001 .

[17]  Lones Smith,et al.  Pathological Outcomes of Observational Learning , 2000 .

[18]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Matthew O. Jackson,et al.  Naïve Learning in Social Networks and the Wisdom of Crowds , 2010 .

[20]  Glenn Ellison,et al.  Word-of-Mouth Communication and Social Learning , 1995 .

[21]  Munther A. Dahleh,et al.  Observational learning in an uncertain world , 2010, 49th IEEE Conference on Decision and Control (CDC).

[22]  Kamiar Rahnama Rad,et al.  Distributed parameter estimation in networks , 2010, 49th IEEE Conference on Decision and Control (CDC).