Journal of Systems Science and Complexity

, Volume 27, Issue 3, pp 581–593 | Cite as

Social learning with time-varying weights

  • Qipeng LiuEmail author
  • Aili Fang
  • Lin Wang
  • Xiaofan Wang


This paper investigates a non-Bayesian social learning model, in which each individual updates her beliefs based on private signals as well as her neighbors’ beliefs. The private signal is involved in the updating process through Bayes’ rule, and the neighbors’ beliefs are embodied in through a weighted average form, where the weights are time-varying. The authors prove that agents eventually have correct forecasts for upcoming signals, and all the beliefs of agents reach a consensus. In addition, if there exists no state that is observationally equivalent to the true state from the point of view of all agents, the authors show that the consensus belief of the whole group eventually reflects the true state.


Consensus social learning social networks time-varying weights 


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of ChinaShanghaiChina

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