This award funds laboratory experiments about how people learn in strategic situations. Most economic and social settings are characterized by repeated interaction between agents. Learning models have long sought to model how agents learn in such repeated interactions. The learning process most commonly assumed is one that maps actions to payoffs and updates this mapping with each new set of evidence. While such behavioral learning is useful in a broad set of situations, humans also tend to display cognitive learning-- learning how to reason about a game and to anticipate other players' actions and other developments. One major difference between the behavioral and cognitive approaches is that in the former, learning between games is not possible unless the actions are labeled the same, whereas in the latter, learning between games is possible and can be characterized and understood with the appropriate framework. The rule learning framework of Stahl (1996) is ideal in this regard. The rule-learning framework posits individuals who apply rules and form mappings between rules and payoffs rather than actions and payoffs. Then reinforcement occurs over rules and individuals learn over time to abandon historically inferior rules in favor of historically successful rules. The PI team has demonstrated that Rule Learning can accommodate such behavioral dynamics, and they will now conduct experiments to test those predictions.
Understanding consumption, investment or managerial rules and predicting which will rules will survive and which will perish is an important task for economists and one which is requires an understanding of how learning transfers between situations. The present framework is an important step in this direction.
