Learning by imitation in games: theory, field, and laboratory

Working paper

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.

Authors: Mohlin, E; Ostling, R; Wang, J

• Publication status: Published
• Publisher: University of Oxford
• Series: Department of Economics Discussion Paper Series
• Publication date: 2014-11-28
• Paper number: 734
• Keywords: imitation; evolutionary game theory; generalization; stochastic approximation; learning; similarity-based reasoning; replicator dynamic; mixed equilibrium; behavioral game theory