Inferring Strategies from Observed Actions: A Nonparametric Binary Tree Classification Approach

This paper introduces a nonparametric binary classification tree approach to inferring unobserved strategies from the observed actions of economic agents. The strategies are in the form of possibly nested if- then statements. We apply our approach to experimental data from the repeated ultimatum game, which was conducted in four different countries by Roth et al. (1991). We find that strategy inference is consistent with existing inference, provides new explanations for subject behavior, and provides new empirically--based hypotheses regarding ultimatum game strategies. We conclude that strategy inference is potentially useful as a complementary method of statistical inference in applied research.