嘉    宾： Ryota Iijima，Professor，Princeton University

主持人： 闵炜程 助理教授 上海交通大学安泰经济与管理学院

时    间：2025年10月15日（周三）15:00-16:00

地    点:  上海交通大学徐汇校区安泰经济与管理学院B207

内容简介：

We consider moral hazard problems where a principal has access to rich monitoring data about an agent's action. Rather than focusing on optimal contracts (which are known to in general be complicated), we characterize the optimal rate at which the principal's payoffs can converge to the first-best payoff as the amount of data grows large. Our main result suggests a novel rationale for the widely observed binary wage schemes, by showing that such simple contracts achieve the optimal convergence rate. Notably, in order to attain the optimal convergence rate, the principal must set a lenient cutoff for when the agent receives a high vs.\ low wage. In contrast, we find that other common contracts where wages vary more finely with observed data (e.g., linear contracts) approximate the first-best at a highly suboptimal rate. Finally, we show that the optimal convergence rate depends only on a simple summary statistic of the monitoring technology. This yields a detail-free ranking over monitoring technologies that quantifies their value for incentive provision in data-rich settings and applies regardless of the agent's specific utility or cost functions.

演讲人简介：

Ryota Iijima is a Professor of Economics at Princeton University. His research is in microeconomic theory (in particular, decision theory and information economics). He received a Ph.D. from Harvard University in 2016. Prior to joining Princeton, he was a Cowles postdoctoral fellow, assistant professor, and associate professor at Yale University.