Year: 2014
Type: report
Abstract: 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.
Institutions: Stockholm University, National Taiwan University
Language: English
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: National Science Council
OpenAlex ID: W337113207