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Non-Bayesian optimal search and dynamic implementation

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  • Gershkov, Alex
  • Moldovanu, Benny

Abstract

We show that a non-Bayesian learning procedure leads to very permissive implementation results concerning the efficient allocation of resources in a dynamic environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents’ values. This contrasts the rather restrictive results that have been obtained for Bayesian learning in the same environment, and highlights the role of the learning procedure in dynamic mechanism design problems.

Suggested Citation

  • Gershkov, Alex & Moldovanu, Benny, 2013. "Non-Bayesian optimal search and dynamic implementation," Economics Letters, Elsevier, vol. 118(1), pages 121-125.
    • Handle: RePEc:eee:ecolet:v:118:y:2013:i:1:p:121-125
    DOI: 10.1016/j.econlet.2012.09.026
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    References listed on IDEAS

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    5. Bikhchandani, Sushil & Sharma, Sunil, 1996. "Optimal search with learning," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 333-359.
    6. Gershkov, Alex & Moldovanu, Benny, 2012. "Optimal search, learning and implementation," Journal of Economic Theory, Elsevier, vol. 147(3), pages 881-909.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dynamic mechanism design; Optimal stopping; Learning;
    All these keywords.

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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