An Experimental Study of Rationality and Learning in Noncooperative Games

经济学中的大多数博弈论应用都假设参与者是理性的、相互理性的、纯粹自利的。在游戏的实验研究中，一个或多个假设经常被违反，因为人们不玩纳什均衡或更精细的均衡（学习有时会减少违反，但并非总是如此）。通常，仅从策略选择就很难判断受试者违反了哪些假设。由于博弈论解决方案的概念可以解释为选择策略的算法，我们可以直接使用计算机系统来测试算法，该系统可以记录游戏中的信息（例如，哪些收益），以及他们的选择持续了多长时间。信息加工数据揭示了对信息的偏好，可以用来推断一个不可观察的思维过程。我们还可以测量学习对信息处理的影响，看看受试者是如何学习的，以及他们是否学习了最佳反应或一般原则（例如，解决方案概念）。本项目将研究的博弈和思想包括：排序议价、正向归纳、纳什vs.子博弈完美和“几乎常识”博弈。

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.