Non-Bayesian optimal search and dynamic implementation
Alex Gershkov and
Benny Moldovanu ()
Economics Letters, 2013, vol. 118, issue 1, 121-125
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.
Keywords: Dynamic mechanism design; Optimal stopping; Learning (search for similar items in EconPapers)
JEL-codes: D4 D8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:118:y:2013:i:1:p:121-125
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