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Dynamic Allocation and PricingA Mechanism Design Approach$
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Alex Gershkov and Benny Moldovanu

Print publication date: 2015

Print ISBN-13: 9780262028400

Published to MIT Press Scholarship Online: May 2016

DOI: 10.7551/mitpress/9780262028400.001.0001

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Learning and Dynamic Efficiency

Learning and Dynamic Efficiency

Chapter:
(p.105) 5 Learning and Dynamic Efficiency
Source:
Dynamic Allocation and Pricing
Author(s):

Alex Gershkov

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028400.003.0005

In this chapter the authors derive conditions on the primitives of 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. The authors’ 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 search policies that are characterized by a reservation price property. Finally, the authors characterize the constrained efficient policy ("second-best") if the efficient policy is not implementable.

Keywords:   Bayesian and non-Bayesian processes, Dynamic allocation environment

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