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A general framework for rational learning in social networks

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  • Mueller-Frank, Manuel

    ()
    (Department of Economics, University of Oxford)

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    Abstract

    This paper provides a formal characterization of the process of rational learning in social networks. Agents receive initial private information and select an action out of a choice set under uncertainty in each of infinitely many periods, observing the history of choices of their neighbors. Choices are made based on a common behavioral rule. Conditions under which rational learning leads to global consensus, local indifference and local disagreement are characterized. In the general setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends, while not being indifferent among their choices. The effect of the network structure on the degree of information aggregation and speed of convergence is also considered and an answer to the question of optimal information aggregation in networks provided. The results highlight distinguishing features between properties of Bayesian and non-Bayesian learning in social networks.

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    Bibliographic Info

    Article provided by Econometric Society in its journal Theoretical Economics.

    Volume (Year): 8 (2013)
    Issue (Month): 1 (January)
    Pages:

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    Handle: RePEc:the:publsh:1015

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    Web page: http://econtheory.org

    Related research

    Keywords: Learning; social networks; common knowledge; consensus; speed of convergence; optimal information aggregation;

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    References

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    Cited by:
    1. Berno Buechel & Tim Hellmann & Stefan Kölßner, 2014. "Opinion Dynamics and Wisdom under Conformity," Working Papers 2014.51, Fondazione Eni Enrico Mattei.
    2. Vivi Alatas & Abhijit Banerjee & Arun G Chandrasekhar & Rema Hanna & BENJAMIN A Olken, 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," Working Papers id:5106, eSocialSciences.
    3. Marco Pelliccia, 2013. "Ambiguous Networks," Birkbeck Working Papers in Economics and Finance 1303, Birkbeck, Department of Economics, Mathematics & Statistics.

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