Non-Bayesian optimal search and dynamic implementation
Non-Bayesian optimal search and dynamic implementation
We show that a non-Bayesian learning procedure leads to very permissive implementation results concerning the efficient allocation of resources in a dynamic environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents’ values. This contrasts the rather restrictive results that have been obtained for Bayesian learning in the same environment, and highlights the role of the learning procedure in dynamic mechanism design problems.
121-125
Gershkov, Alex
214a0b5e-c742-486d-b910-c8ec702c943a
Moldovanu, Benny
f84fdd42-3143-4219-be24-fb26385b106d
Gershkov, Alex
214a0b5e-c742-486d-b910-c8ec702c943a
Moldovanu, Benny
f84fdd42-3143-4219-be24-fb26385b106d
Gershkov, Alex and Moldovanu, Benny
(2012)
Non-Bayesian optimal search and dynamic implementation.
Economics Letters, 118 (1), .
(doi:10.1016/j.econlet.2012.09.026).
Abstract
We show that a non-Bayesian learning procedure leads to very permissive implementation results concerning the efficient allocation of resources in a dynamic environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents’ values. This contrasts the rather restrictive results that have been obtained for Bayesian learning in the same environment, and highlights the role of the learning procedure in dynamic mechanism design problems.
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Accepted/In Press date: 21 September 2012
e-pub ahead of print date: 5 October 2012
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Local EPrints ID: 503682
URI: http://eprints.soton.ac.uk/id/eprint/503682
ISSN: 0165-1765
PURE UUID: 646c5eef-5704-4c66-8f26-da3a1109c66a
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Date deposited: 08 Aug 2025 16:44
Last modified: 09 Aug 2025 02:19
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Author:
Alex Gershkov
Author:
Benny Moldovanu
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