报告主题: Screening with Limited Information

主讲人: Zhi Chen, Assistant Professor

报告简介: Consider a seller seeking a selling mechanism to maximize the worst-case revenue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. For a generic convex ambiguity set, we show via the minimax theorem that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem where the adversary first chooses a worst-case distribution and then the seller decides the best posted price mechanism. We then study a multidimensional mechanism design problem where a seller offers multiple products to a single buyer. The seller possesses only marginal distributional information about the buyer's random valuation of each product. The buyer is not a perfect optimizer and is satisficing whenever his incentive is epsilon away from the optimal---a notion called approximate incentive compatibility (IC). We show that the optimal mechanism first allocates epsilon among multiple products and then separately screens each product under approximate IC. To compute the optimal mechanism, we first propose a discrete approximation that, powered by our separation theorem, can be reformulated as a scalable finite-dimensional convex program; we then devise an oracle that efficiently extends solutions of the discretization to feasible mechanisms with attractive performance guarantees.

主讲人简介: Zhi Chen is an Assistant Professor in the CUHK Business School, the Chinese University of Hong Kong. His research interests include (1) developing models and designing algorithms for decision-making under uncertainty with different levels of data availability as well as applications in business, economics, finance, and operations; (2) how to compete or cooperate in joint activities such as resource allocation and risk management.