We develop a theoretical framework for analyzing how decision makers form theories and extract behavioral rules directly from data. We distinguish a stable data-generating process (DGP) linking a relevant set of variables to outcomes from dataset-specific correlations that can make rules based on irrelevant variables perform well in some datasets but not in others. Rule extraction reflects a cost--benefit trade-off between a rule's value and the cognitive cost of processing it. The framework predicts when agents extract the optimal behavioral rule that generalizes across datasets, how they substitute toward alternatives, when irrelevant variables lower its extraction, and how differences in cognitive costs generate systematic heterogeneity in rule extraction among agents learning from the same data. We test these predictions in our experiment and in the data of Kendall and Oprea (2024), and find strong support for them in both settings. We also show how rule-specific costs can be identified from observed choices and use the framework to determine which observable measures of complexity can be interpreted as entering the cost function.

We study -- empirically and theoretically -- how restitution helps restore cooperation after a breach or an exploratory defection. Restitution strategies propose a return to cooperation by cooperating against defection, and condition actions on the balance between cooperation given and received. We reanalyze experimental data from repeated Prisoner's Dilemma games and find empirical support for restitution strategies in general, and for a strategy we name Payback, in particular.  Besides explaining how subjects deal with conflicts, accounting for restitution strategies helps reconcile discrepancies between theory and experiments—such as the widespread use of non-equilibrium strategies like Tit-for-Tat and the limited predictive power of risk dominance under imperfect monitoring.

It has been amply shown that choice behavior is context-dependent. Evidence from the cognitive sciences suggests that this dependency may be driven by implicit associations. In this paper, we propose and study a choice-based model of contextual associations. We start by formalizing contexts by the set of concepts it contains. We then introduce associations by way of the implicit relationship between alternatives and concepts, which directly impacts the utility evaluation of alternatives. We study the empirical content of the model, establish conditions for identification, characterize its comparative statics, and propose several extensions. We provide an application to probabilistic voting, showing that our model rationalizes how political competition contributes to the polarization of parties’ values, as emphasized in the empirical literature.

In this paper I study the trade-off between efficiency and strategic risk in the context of a repeated Prisoner's Dilemma with imperfect public monitoring. Because unfavorable public signals and resulting punishments can occur on the equilibrium path, keeping the value of cooperation high requires players to adopt more lenient and forgiving strategies, which leaves them more exposed to opportunistic behavior. For a broad class of one-dimensional public signals, I show that under imperfect monitoring, strategy adoption responds to beliefs about the opponent's strategy, whereas under perfect monitoring, beliefs affect only the extensive margin—that is, the decision of whether to cooperate at all. Finally, I use experimental data from Aoyagi and Fréchette (2009) to document suggestive patterns consistent with subjects choosing strategies to maximize the cooperation value rather than controlling for strategic risk.