RESUMEN
When explaining other people's behavior, people generally find some explanations more satisfying than others. We propose that people judge behavior explanations based on two computational principles: simplicity and rational support-the extent to which an explanation makes the behavior "make sense" under the assumption that the person is a rational agent. Furthermore, we present a computational framework based on decision networks that can formalize both of these principles. We tested this account in a series of experiments in which subjects rated or generated explanations for other people's behavior. In Experiments 1 and 2, the explanations varied in what the other person liked and disliked. In Experiment 3, the explanations varied in what the other person knew or believed. Results from Experiments 1 and 2 supported the idea that people rely on both simplicity and rational support. However, Experiment 3 suggested that subjects rely only on rational support when judging explanations of people's behavior that vary in what someone knew.