Optimal Search, Learning, and Implementation
Hebrew University of Jerusalem
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
Date posted: May 29, 2009
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