Non-Bayesian optimal search and dynamic implementation

Alex Gershkov, Benny Moldovanu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)121-125
Number of pages5
JournalEconomics Letters
Volume118
Issue number1
DOIs
StatePublished - Jan 2013
Externally publishedYes

Keywords

  • Dynamic mechanism design
  • Learning
  • Optimal stopping

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