Optimal Search, Learning, and Implementation

31 Pages Posted: 29 May 2009  

Alex Gershkov

Hebrew University of Jerusalem

Benny Moldovanu

University of Bonn - Chair of Economic Theory II; Centre for Economic Policy Research (CEPR)

Date Written: May 15, 2009


We derive conditions on the learning environment - which encompasses both Bayesian and non-Bayesian processes - ensuring that an efficient allocation of resources is achievable in a dynamic allocation environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents' values. There are two main kind of conditions: 1) Higher observations should lead to more optimistic beliefs about the distribution of future values; 2) The allowed optimism associated with higher observations needs to be carefully bounded. Our analysis reveals and exploits close, formal relations between the problem of ensuring monotone - and hence implementable - allocation rules in our dynamic allocation problems with incomplete information and learning, and between the classical problem of finding optimal stopping policies for search that are characterized by a reservation price property.

Keywords: Sequential Assignment, Learning, Dynamic Mechanism Design

JEL Classification: C7, D7, D8

Suggested Citation

Gershkov, Alex and Moldovanu, Benny, Optimal Search, Learning, and Implementation (May 15, 2009). Available at SSRN: https://ssrn.com/abstract=1411295 or http://dx.doi.org/10.2139/ssrn.1411295

Alex Gershkov (Contact Author)

Hebrew University of Jerusalem ( email )

Mount Scopus
Jerusalem, IL 91905

HOME PAGE: http://pluto.huji.ac.il/~alexg/

Benny Moldovanu

University of Bonn - Chair of Economic Theory II ( email )

Lennestrasse 37
53113 Bonn
+49 228 736395 (Phone)
+49 228 737940 (Fax)

Centre for Economic Policy Research (CEPR)

77 Bastwick Street
London, EC1V 3PZ
United Kingdom

Paper statistics

Abstract Views