讲座题目：Non-clairvoyant Dynamic Mechanism Design: Experimental Evidence
主 讲 人：美国乔治梅森大学 Daniel Houser 教授
讲座时间：2022年6月25日（周六）晚上7：30
腾讯会议：835 303 451
主讲人简介:
Daniel Houser教授是美国乔治梅森大学经济系系主任，乔治·梅森大学经济科学跨学科研究中心主任。与实验经济学鼻祖、2002年经济学诺贝尔奖得主Vernon Smith合作紧密。主要研究领域有：实验经济学、行为经济学、神经经济学。担任Management Science、Experimental Economics、Journal of Economic Behavior and Organization、Journal of Neuroscience, Psychology and Economics、Frontiers in Neuroscience等学术期刊的主编、副主编或编委，以及Science, Nature, PNAS, National Science Foundation等二十多个杂志和基金项目的审稿人。获得过多项国家科学基金的资助。在 PNAS、American Economic Review、Econometrica、Journal of Finance、Leadership Quarterly、Experimental Economics等期刊上发表多篇论文。
讲座摘要:
Dynamic mechanisms are powerful approaches for optimizing the revenue and efficiency of repeated auctions. Implementing these approaches is made complicated, however, by a number of conditions that are difficult to satisfy in practice. These include that the auction designer must be clairvoyant, in the sense that they must have reliable forecasts of participants’ valuation distributions in all future periods. Recently, Mirrokni et al. (2020) introduced a non-clairvoyant dynamic mechanism and showed it is optimal within the class of dynamic mechanisms that do not rely on strong assumptions regarding knowledge about the future. Here we report data from an experiment designed to test the performance of their mechanism. Our results support the theory: the optimal non-clairvoyant dynamic mechanism outperforms the repeated optimal static mechanism when it is predicted to do so. Our results point to the practical importance of non-clairvoyant mechanisms as implementable approaches to dynamic auction design.
