A computational framework for understanding the roles of simplicity and rational support in people's behavior explanations

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.