4/19/18 | 4:15pm | E51-345
Reception to follow.
Abstract: Auctions are widely used in practice. While also extensively studied in the literature, most of the developments rely on significant informational assumptions for the seller. In this work, we study the design of optimal prior-independent selling mechanisms. In particular, the seller faces buyers whose values are drawn from an unknown distribution, and only knows that the distribution belongs to a certain class. Our results are along two dimensions. We first characterize the structure of optimal mechanisms. Leveraging such structure, we then establish tight lower and upper bounds on the performance against various classes of distributions, leading to a crisp characterization of optimal performance. (joint work with A. Allouah)
Bio: Omar Besbes is an Associate Professor in the Decision, Risk, & Operations division at the Graduate School of Business, Columbia University. His primary research interests are in the area of data-driven decision-making with a focus on applications in e-commerce, pricing, and revenue management, online advertising, operations management, and service systems. His research has been recognized by the 2012 INFORMS Revenue Management and Pricing Section prize, the 2013 M&SOM best paper award, and the 2017 M&SOM young scholar award. He serves on the editorial boards of Management Science and Operations Research.
He has taught over the years core MBA courses in Operations Management and Business Analytics, an MBA elective on advanced Business Analytics, as well as various Ph.D. seminars on stochastic models, revenue management and data-driven decision-making. He is a recipient of the Dean's award for teaching excellence in the core at Columbia Business School.
Omar is a graduate of Ecole Polytechnique (France) and received an M.Sc. from Stanford University in 2000 and a Ph.D. from Columbia University in 2008. Before joining Columbia, he was on the faculty at the Wharton School, University of Pennsylvania.