Most game-theoretic applications in economics assume players are rational, mutually rational, and purely self-interested. In experimental studies of games one or more of these assumptions often appear to be violated because people do not play Nash or more refined equilibria (learning sometimes reduces violations but not always). It is usually difficult to tell from strategy choices alone which assumptions a subject is violating. Since game- theoretic solution concepts can be interpreted as algorithms for choosing a strategy, we can test the algorithms directly using a computer system that records what information in a game (e.g., which payoffs) a subject is looking at, and for how long, along with their choices. The information processing data are revealed preferences for information, which can be used to infer an unobservable thinking process. We can also measure the effect of learning on information processing to see how subjects learn, and whether they learn optimal responses or general principles (e.g., solution concepts). The games and ideas that will be studied in this project include: sequencing bargaining, forward induction, Nash vs. subgame perfection, and "almost common knowledge" games.
