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Exploiting Myopic Learning

Mostagir, Mohamed (2010) Exploiting Myopic Learning. In: Internet and Network Economics. Lecture Notes in Computer Science . No.6484. Springer , Berlin, pp. 306-318. ISBN 978-3-642-17571-8. https://resolver.caltech.edu/CaltechAUTHORS:20110603-140403915

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Abstract

We show how 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 our model 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. We show that, for a large class of games, the principal can always obtain strictly better outcomes than the corresponding Nash solution and explicitly specify how such outcomes can be implemented. The methods applied are general enough to accommodate many scenarios, and powerful enough to generate predictions that allude to some empirically-observed behavior.


Item Type:Book Section
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http://resolver.caltech.edu/CaltechAUTHORS:20170725-172657983Related ItemSSWP 1341
Additional Information:© 2010 Springer-Verlag Berlin Heidelberg.
Series Name:Lecture Notes in Computer Science
Issue or Number:6484
Record Number:CaltechAUTHORS:20110603-140403915
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20110603-140403915
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:23902
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 Jun 2011 20:41
Last Modified:03 Oct 2019 02:51

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