Non-Bayesian optimal search and dynamic implementation

Alex Gershkov; Benny Moldovanu

doi:10.1016/j.econlet.2012.09.026

Non-Bayesian optimal search and dynamic implementation

Alex Gershkov^*, Benny Moldovanu

^*Corresponding author for this work

Department of Economics

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	121-125
Number of pages	5
Journal	Economics Letters
Volume	118
Issue number	1
DOIs	https://doi.org/10.1016/j.econlet.2012.09.026
State	Published - Jan 2013

Bibliographical note

Funding Information:
We wish to thank Philippe Jehiel for helpful remarks. We are grateful to the German Science Foundation , and to the ERC for financial support. Gershkov’s research was partly supported by the Google Inter-University Center for Electronic Markets and Auctions .

Keywords

Dynamic mechanism design
Learning
Optimal stopping

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10.1016/j.econlet.2012.09.026

Cite this

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KW - Optimal stopping

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Non-Bayesian optimal search and dynamic implementation

Abstract

Bibliographical note

Keywords

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