Exploiting Myopic Learning

Start: 10/21/2010 - 4:15pm
End  : 10/21/2010 - 5:15pm

Statistics/OR/Math Finance Seminar

Mohamed Mostagir, CalTech


We develop a framework in which a central authority or 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 framework takes a simple form of imitation dynamics; a class of learning dynamics that often leads the population to converge to a Nash equilibrium of the underlying game. To illustrate our approach, we 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 correspond to empirically-observed behavior.

Harvey Mudd College 3rd floor Sprague. Refreshments at 4pm.

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