Learning by Imitation in Games: Theory, Field, and Laboratory

Nov 2014 | 734

Authors: Erik Mohlin, Robert Ostling, Joseph Tao-yi Wang


We exploit a unique opportunity to study how a large population of players in the field learn to play a novel game with a complicated and non-intuitive mixed strategy equilibrium.  We argue that standard models of belief-based learning and reinforcement learning are unable to explain the data, but that a simple model of similarity-based global cumulative imitation can do so.  We corroborate our findings using laboratory data from a scaled-down version of the same game, as well as from three other games.  The theoretical properties of the proposed learning model are studied by means of stochastic approximation.

JEL Codes: C72, C73, L83

Keywords: Learning, imitation, behavioral game theory, evolutionary game theory, stochastic approximation, replicator dynamic, similarity-based reasoning, generalization, mixed equilibrium


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