Social learning with time-varying weights
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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.
KeywordsConsensus social learning social networks time-varying weights
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- Kagel J H and Roth A E, Handbook of Experimental Economics, Princeton University Press, Princeton, 1995.Google Scholar
- Rabin M, Psychology and economics, Journal of Economics Literature, 1998, 36: 11–46.Google Scholar
- Golub B and Jackson M O, Naïve learning in social networks: Convergence, influence, and the wisdom of crowds, American Economic Journal: Microeconomics, 2010, 2(1): 112–149.Google Scholar