Learning and Economic Decisions

这项研究包括三个项目，调查如何经济 智能体使用他们过去的经验来指导他们当前的决策。 第一个研究项目研究使用非常简单规则的代理人 在选择替代技术时的经验;这些规则 将包含这样的想法，即代理人试图从 邻居的经历。 该项目将调查 这种“社会学习”的长期影响，特别是 简单的规则何时能带来有效的长期结果。 的 第二个项目研究在游戏中学习。 这里的重点是 当重复的互动会导致博弈符合纳什均衡时， 平衡或其细化之一。 第三个项目将 建立一个公司所有者之间的最优合同模型， 它的经理在一个环境中，业主了解公司的 从目前的业绩看未来的前景。 该模型将 为广泛观察到的“收入”做法提供解释 平滑，“在这种情况下，经理采取行动，增加 当收入低时报告收入，并减少报告收入 当收入高的时候。 这项研究很重要，因为它解决了 并帮助提供一些最重要的问题的答案， 学习的经济意义。

This research consists of three projects investigating how economic agents use their past experience to guide their current decisions. The first research project studies agents who use very simple rules of thumb in choosing between alternative technologies; these rules will incorporate the idea that agents try to learn from the experiences of their neighbors. The project will investigate the long-run implications of such "social learning," and in particular on when simple rules can lead to efficient long-run outcomes. The second project studies learning in games. Here the focus is on when repeated interaction will lead play to correspond to a Nash equilibrium or to one of its refinements. The third project will develop a model of optimal contracts between a firm's owners and its manager in a setting where the owners learn about the firm's future prospects from its current performance. The model will provide an explanation for the widely-observed practice of "income smoothing," under which the manager takes actions that increase reported income when income is low, and decrease reported income when income is high. This research is important because it tackles and helps provide answers to a few of the most important issues in the economic implications of learning.