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We consider the problem of estimating preferences of human agents from dataof strategic systems where the agents repeatedly interact. Recently, it wasdemonstrated that a new estimation method called "quantal regret" produces moreaccurate estimates for human agents than the classic approach that assumes thatagents are rational and reach a Nash equilibrium; however, this method has notbeen compared to methods that take into account behavioral aspects of humanplay. In this paper we leverage equilibrium concepts from behavioral economicsfor this purpose and ask how well they perform compared to the quantal regretand Nash equilibrium methods. We develop four estimation methods based onestablished behavioral equilibrium models to infer the utilities of humanagents from observed data of normal-form games. The equilibrium models we studyare quantal-response equilibrium, action-sampling equilibrium, payoff-samplingequilibrium, and impulse-balance equilibrium. We show that in some of theseconcepts the inference is achieved analytically via closed formulas, while inthe others the inference is achieved only algorithmically. We use experimentaldata of 2x2 games to evaluate the estimation success of these behavioralequilibrium methods. The results show that the estimates they produce are moreaccurate than the estimates of the Nash equilibrium. The comparison with thequantal-regret method shows that the behavioral methods have better hit rates,but the quantal-regret method performs better in terms of the overall meansquared error, and we discuss the differences between the methods.

From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions