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Social learning with time-varying weights

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Abstract

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.

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Qipeng Liu, Aili Fang, Lin Wang & Xiaofan Wang

Authors
  1. Qipeng Liu
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  2. Aili Fang
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  3. Lin Wang
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  4. Xiaofan Wang
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Corresponding author

Correspondence to Qipeng Liu.

Additional information

This research is supported by the National Natural Science Foundation of China under Grant Nos. 61074125 and 61104137, the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant No. 61221003, and the National Key Basic Research Program (973 Program) of China under Grant No. 2010CB731403.

This paper was recommended for publication by Editor ZOU Guohua.

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Liu, Q., Fang, A., Wang, L. et al. Social learning with time-varying weights. J Syst Sci Complex 27, 581–593 (2014). https://doi.org/10.1007/s11424-014-1195-0

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  • DOI: https://doi.org/10.1007/s11424-014-1195-0

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