2012/03/15 by Xi Alice Gao, Avi Pfeffer, Gao, Xi Alice +1
Decision Sciences · Social Sciences · #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Decision-Making and Behavioral Economics #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #Game Theory and Applications
paper · pdf · doi:10.48550/arxiv.1203.3480
openalex publication_date 2012/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representations from data. If one wants to learn both the payoffs and the players' strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main contribution of this paper is to make this connection while learning. We formulate the learning problem as a weighted constraint satisfaction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporating rationality constraints can improve learning when the amount of data is limited.