主题: Simple and Robust Quality Disclosure: The Power of Quantile Partition
时间: 2026年4月10日10:00-11:30
地点: 管理学院 A723
主讲人: 冯逸丁, 香港科技大学 助理教授
主持人: 宋昊天，浙江大学管理学院“百人计划研究员”
Bio:
Yiding Feng is an Assistant Professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology (HKUST). Prior to joining HKUST, he was a Principal Researcher at the University of Chicago Booth School of Business and a Postdoctoral Researcher at Microsoft Research New England. He received his Ph.D. in Computer Science from Northwestern University in 2021 and his B.S. from the ACM Honors Class at Shanghai Jiao Tong University in 2016. His research interests lie at the intersection of operations research, economics and computation, and theoretical computer science. His work has been published in leading journals such as Management Science and Operations Research, as well as top theoretical computer science and economics conferences including STOC, FOCS, SODA, EC, ITCS, and WINE. He was a recipient of the INFORMS Auctions and Market Design (AMD) Michael H. Rothkopf Junior Researcher Paper Prize and the APORS Young Researcher Best Paper Award.
Abstract:
Online platforms often use simple percentile-based badges to convey quality information stably across markets. Motivated by this, we study robust quality disclosure: a platform commits to a public signal mapping product quality, then a monopolist sets prices. Buyers have private types with quality-linear valuations. We evaluate policies by minimax competitive ratio—worst-case revenue relative to Bayesian optimum—over all prior distributions.
Our results justify quantile-partition disclosure. For K-quantile policies, we characterize the robust optimum via a fixed-point equation and backward recursion, and provide an explicit “max-over-bins” formula explaining why finer top-quantile resolution yields guarantees like 1+1/K. Finite-signal monotone partitions cannot beat factor-2. Technically, we reduce the problem via a functional characterization of feasible indirect revenues.
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