Title: 

Simple menus in robust screening

Abstract: 

This talk investigates the design and effectiveness of simple selling mechanisms when a seller has only partial information about a buyer’s valuation distribution, obtained through market research or price experimentation. While robust screening offers stronger guarantees compared to deterministic pricing, it often involves complex menus with infinitely many options, posing implementation challenges. Our research introduces simple mechanisms with finite menus that balance performance guarantees with practical implementation. Using a unified framework for various ambiguity sets—including support, mean, and quantile—we derive optimal mechanisms and performance ratios for different menu sizes. Our findings reveal that modest menu sizes can closely approximate the benefits of optimal infinite-menu mechanisms. Remarkably, even a two-option menu significantly outperforms deterministic pricing.

We extend our results to multi-item mechanism design, where optimal mechanisms are complicated even with full knowledge of buyers’ valuation distributions. To address this challenge, we propose “semi-separable mechanisms,” where each item's allocation and payment rules depend only on its valuation and joint distributional information, but not on the valuations of other items. We prove that semi-separable mechanisms achieve the optimal performance ratio among all incentive-compatible and individually rational mechanisms when only marginal support information is available. Additionally, our framework accommodates settings where sellers possess aggregate valuation information for product bundles, further enhancing its practical applicability.

Bio: 

Shixin Wang is an Assistant Professor in the Department of Decisions, Operations and Technology at The Chinese University of Hong Kong. Before joining CUHK, she earned her Ph.D. in Operations Management from NYU Stern School of Business and a bachelor’s degree in Industrial Engineering from Tsinghua University. Her research focuses on developing simple, robust pricing policies in revenue management and designing sparse, reliable networks for supply chain and service systems. Her work has been recognized as a finalist in the INFORMS JFIG Paper Competition and the INFORMS Service Science Best Cluster Paper Award. Her research has been supported by funding from Hong Kong Research Grants Council (RGC).