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Optimal Search, Learning, and Implementation

Alex Gershkov

Hebrew University of Jerusalem

Benny Moldovanu

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

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.

Number of Pages in PDF File: 31

Keywords: Sequential Assignment, Learning, Dynamic Mechanism Design

JEL Classification: C7, D7, D8

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Date posted: May 29, 2009  

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

Contact Information

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
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