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Social Learning and the Vanishing Value of Private Information

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Abstract

This paper considers a sequential social learning game with a general utility function, state and action space. We establish that the value of private information converges to zero almost surely in every Perfect Bayesian equilibrium of any sequential social learning game. We use this result to show that totally unbounded signals are necessary and sufficient for asymptotic learning to hold in every sequential social learning game. Finally, we assume that the utility function of each agent is a private random draw and establish robustness of our results.

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  • Mueller-Frank, Manuel & Arieliy, Itai, 2015. "Social Learning and the Vanishing Value of Private Information," IESE Research Papers D/1119, IESE Business School.
    • Handle: RePEc:ebg:iesewp:d-1119
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    File URL: http://www.iese.edu/research/pdfs/WP-1119-E.pdf
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    1. Philippe Aghion & Patrick Bolton & Christopher Harris & Bruno Jullien, 1991. "Optimal Learning by Experimentation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(4), pages 621-654.
    2. Itai Arieli & Manuel Mueller-Frank, 2019. "Multidimensional Social Learning," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 913-940.
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    9. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    10. Manuel Mueller-Frank & Mallesh M. Pai, 2016. "Social Learning with Costly Search," American Economic Journal: Microeconomics, American Economic Association, vol. 8(1), pages 83-109, February.
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    Cited by:

    1. Arieli, Itai, 2017. "Payoff externalities and social learning," Games and Economic Behavior, Elsevier, vol. 104(C), pages 392-410.
    2. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.

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