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While game theory is widely used to model strategic interactions, a naturalquestion is where do the game representations come from? One answer is to learnthe representations from data. If one wants to learn both the payoffs and theplayers' strategies, a naive approach is to learn them both directly from thedata. This approach ignores the fact the players might be playing reasonablygood 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 satisfactionproblem, including constraints both for the fit of the payoffs and strategiesto the data and the fit of the strategies to the payoffs. We use quantalresponse equilibrium as our notion of rationality for quantifying the latterfit. Our results show that incorporating rationality constraints can improvelearning when the amount of data is limited.

Learning Game Representations from Data Using Rationality Constraints