经鸿之粟 | 桂姗助理教授合作论文在经济学国际一类期刊正式发表

摘要

We study dynamic mechanism design in non-clairvoyant environments, where sellers cannot forecast future demand and buyers hold private information. Using two laboratory experiments, we evaluate two non-clairvoyant mechanisms: the dynamic maximin- optimal non-clairvoyant mechanism (DM, Mirrokni et al. (2020)) and the repeated static optimal mechanism (RS, Myerson (1981)). Experiment 1 compares DM and RS between demand-distribution scenarios and shows that non-clairvoyant mechanisms perform as intended: DM either outperforms or underperforms RS depending on the scenario, consistent with theory. Building on these results, Experiment 2 examines the optimal clairvoyant mechanism (OC) and a different implementation of DM. In surprising contrast to theory, OC using full information performs no better than the non-clairvoyant mechanisms, while DM performs equally well across implementation forms. Our results highlight the practical importance of non-clairvoyant mechanisms as implementable approaches to dynamic auction design in uncertain markets.

桂姗，上海财经大学经济学院助理教授，美国乔治梅森大学经济学博士。主要研究方向为行为经济学与实验经济学，相关研究成果发表于 Games and Economic Behavior、American Journal of Economics and Sociology等期刊；主持国家自然科学基金青年项目。