Optimal search, learning and implementation

Alex Gershkov*, Benny Moldovanu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We characterize the incentive compatible, constrained efficient policy ("second-best") in a dynamic matching environment, where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents' values. We also derive conditions on the learning process ensuring that the complete-information, dynamically efficient allocation of resources ("first-best") is incentive compatible. Our analysis reveals and exploits close, formal relations between the problem of ensuring implementable allocation rules in our dynamic allocation problems with incomplete information and learning, and between the classical problem, posed by Rothschild (1974) [20], of finding optimal stopping policies for search that are characterized by a reservation price property.

Original languageAmerican English
Pages (from-to)881-909
Number of pages29
JournalJournal of Economic Theory
Volume147
Issue number3
DOIs
StatePublished - May 2012

Bibliographical note

Funding Information:
The comments of an associate editor and an anonymous referee greatly improved the quality of the exposition. We wish to thank Sergiu Hart, Philippe Jehiel, Alessandro Pavan, Xianwen Shi, Phil Reny and Asher Wolinsky for helpful remarks. Participants at the workshop “Information and Dynamic Mechanism Design” June 2009, Bonn, made very fruitful comments. We are grateful to the German Science Foundation and to the European Research Council for financial support.

Keywords

  • Dynamic mechanism design
  • Learning
  • Optimal stopping

Fingerprint

Dive into the research topics of 'Optimal search, learning and implementation'. Together they form a unique fingerprint.

Cite this