【SDS Colloquium Series】 Price Discovery in Waiting Lists（Pengyu Qian, Assistant Professor, Krannert School of Management, Purdue University）
Topic: Price Discovery in Waiting Lists
Speaker: Pengyu Qian, Assistant Professor, Krannert School of Management, Purdue University
Host: Prof. Yilun CHEN, Assistant Professor, School of Data Science, CUHK-Shenzhen
Date: 21 September (Wednesday), 2022
Time: 11:00 to 12:00, Beijing Time
Format: Hybrid
Venue: 103 Meeting Room, Daoyuan Building
Zoom Link: https://cuhk-edu-cn.zoom.us/j/5304767369?pwd=aFErUGFSSDlLNWJld0VNNmpTL0k0UT09
Zoom Meeting ID: 5304767369
Password: 852648
Language: English
Recording:
Abstract：
Waiting lists offer agents a choice between types of items with associated waiting times. These waiting times function as prices and are endogenously determined by a tâtonnement-like price discovery process: an item's price increases when an agent selects it, and decreases when an item arrives and is assigned. We show that this simple price discovery process generates waiting times that fluctuate around market-clearing prices, and that the loss from price fluctuations is bounded by the size of price adjustments. The technical approach and intuition for the results relies on a connection between price adjustments in the waiting list and the stochastic gradient descent optimization algorithm. We further show that this simple price discovery process is asymptotically optimal if the size of price adjustments optimally balances between the adaptivity and the rigidity of the price discovery process. (Joint work with Itai Ashlagi, Jacob Leshno, and Amin Saberi)
Biography：
Pengyu Qian is an assistant professor at the Krannert School of Management, Purdue University. He received his Ph.D. from Columbia Business School in 2021, and his B.S. in Mathematics from Peking University in 2015. His research studies networked marketplaces with an emphasis on online decision-making, using tools from applied probability and modern optimization. He is interested in foundational theoretical models motivated by problems in revenue management and pricing, and matching markets. His research emphasizes algorithms and mechanisms that not only have good theoretical guarantees, but also are simple, robust, and hence practical for real-world systems.
Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4192003
