Speaker:

Yiding Feng (The Hong Kong University of Science and Technology)

Time:

• 10:00-11:00 Beijing Time
• Nov 6, 2024 (Wednesday)

Venue:

518, Research Building 4

Abstract:

A large proportion of the Bayesian mechanism design literature is restricted to the family of regular distributions [Mye-81] or the family of monotone hazard rate (MHR) distributions [BMP-63], which overshadows this beautiful and well-developed theory. We (re-)introduce two generalizations, the family of quasi-regular distributions and the family of quasi-MHR distributions.
The significance of our new families is manifold. First, their defining conditions are immediate relaxations of the regularity/MHR conditions (i.e., monotonicity of the virtual value functions and/or the hazard rate functions), which reflect economic intuition. Second, they satisfy natural mathematical properties (about order statistics) that are violated by both original families. Third but foremost, numerous results [BK-96, HR-09, CD-15, DRY-15, HR-14, AHNPY-19, JLQTX-19, FLR-19, GHZ-19, JLTX-20, JLX-23, LM-24] established before for regular/MHR distributions now can be generalized, with or even without quantitative losses.
This talk is based on joint work with Yaonan Jin (Huawei TCS Lab).

Speaker Bio:

Yiding Feng is an assistant professor at HKUST IEDA. Previously, he worked as a principal researcher at the University of Chicago Booth School of Business, and postdoctoral researcher at Microsoft Research New England. He received his Ph.D. from the Department of Computer Science, at Northwestern University in 2021. His research focuses on operations research, economics & computation, and theoretical computer science. He was the recipient of the INFORMS Auctions and Market Design (AMD) Michael H. Rothkopf Junior Researcher Paper Prize (second place).