Topic: Optimal Search Auctions with Deadlines
Speaker: Associate Professor Li Zhiyun, Department of Economics, Durham University
Host: Professor Yu Jianyun from RIEM, SWUFE
Time: September 9, 2025 (Tuesday) 10:00-11:30
Location: Conference Room 1211, Gezhi Building, Liulin Campus, SWUFE
Organizer: RIEM
Speaker's Profile
Li Zhiyun is an Associate Professor in the Department of Economics at Durham University, a doctoral supervisor, and Deputy Director of the PhD program in Economics. He received his PhD in Economics from the University of Oxford in 2012 and has worked at international organizations and university research institutes. His research areas include applied microeconomic theory, industrial organization, and development economics. His research findings have been published in journals such as International Economic Review, Journal of Industrial Economics, Journal of Economic Behavior & Organization, Social Choice and Welfare, and Journal of Comparative Economics. He is a highly cited author in Chinese social science literature, with over 4,000 citations on CNKI (China National Knowledge Infrastructure).
Abstract
This study examines the optimal strategy for searching potential bidders before the deadline to allocate an item optimally and provides a complete solution to the optimal mechanism design problem. For long‑term bidders, the optimal search strategy features a constant stopping threshold and an adaptive sampling rule: the stopping threshold remains fixed while the sampling intensity increases over time but decreases with the seller’s outside option. For short‑term bidders, the optimal search strategy displays a stopping threshold that declines over time and an increasing sample size. We show that an optimal sales mechanism can be implemented by a two‑price auction with appropriately set reserve prices and sampling rules; implementing the mechanism for long‑term bidders requires reserve prices that vary with bidders’ entry times and the number of subsequent bidders induced by their bids. We also derive the efficient mechanism, highlight that efficiency loss in the optimal search mechanism may stem from its sampling rule, and provide related comparative results.
