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Exploiting Myopic Learning

Mostagir, Mohamed (2010) Exploiting Myopic Learning. Social Science Working Paper, 1341. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20170725-172657983

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Abstract

I develop a framework in which a principal can exploit myopic social learning in a population of agents in order to implement social or selfish outcomes that would not be possible under the traditional fully-rational agent model. Learning in this framework takes a simple form of imitation, or replicator dynamics, a class of learning dynamics that often leads the population to converge to a Nash equilibrium of the underlying game. To illustrate the approach, I give a wide class of games for which the principal can always obtain strictly better outcomes than the corresponding Nash solution and show how such outcomes can be implemented. The framework is general enough to accommodate many scenarios, and powerful enough to generate predictions that agree with empirically-observed behavior.


Item Type:Report or Paper (Working Paper)
Related URLs:
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http://resolver.caltech.edu/CaltechAUTHORS:20110603-140403915Related ItemPublished Version
Additional Information:I thank John Ledyard, R. Preston McAfee, Thomas Palfrey, and Jean-Laurent Rosenthal for their comments and suggestions. I also enjoyed many useful discussions with Dustin Beckett and Théophane Weber.
Group:Social Science Working Papers
Subject Keywords:Social Learning, Repeated Games, Bounded Rationality
Classification Code:JEL: C72, C73, D03
Record Number:CaltechAUTHORS:20170725-172657983
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170725-172657983
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79367
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:07 Aug 2017 21:24
Last Modified:07 Aug 2017 21:24

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