报告题目：Competitive Information Design for Pandora's Box
报告摘要：We study a natural competitive-information-design variant for the Pandora’s Box problem, where each box is associated with a strategic information sender who can design what information about the box’s prize value to be revealed to the agent when she inspects the box. This variant with strategic boxes is motivated by a wide range of real-world economic applications for Pandora’s box. The main contributions of this article are two-fold: (1) we study informational properties of Pandora’s Box by analyzing how a box’s partial information revelation affects the search agent’s optimal decisions; and (2) we fully characterize the pure symmetric equilibrium for the boxes’ competitive information revelation, which reveals various insights regarding information competition and the resultant agent utility at equilibrium.
Wei Tang is currently a Postdoctoral Research Scientist at the Data Science Institute, Columbia University. He obtained his Ph.D at Washington University in St.Louis, and Bachlor‘s degree from Tianjin University.
His research interests are in machine learning, algorithmic economics, and online behavioral experiments, with a focus on developing theoretically rigorous, empirically grounded frameworks to understand and design algorithmic systems that integrate humans in the design process.
He was selected as the Rising Star in Data Science by The University of Chicago in 2021 Fall, and his work has won a best paper hornable memtion in HCOMP.