Theory Seminar: Kamesh Munagala

This week, to account for visit day, theory seminar will be on Thursday March 16 at noon (instead of Friday at 1PM). It will be held in the PCPSE (Perelman Center for Political Science and Economics), in the Dean’s forum on the second floor.

We are excited to have Kamesh Munagala from Duke speaking. Please find the abstract and bio below.

Abstract: We study the classical Bayesian auction setting with a twist: Between the revenue maximizing seller and the buyers lies an intermediary that is better informed about the buyer values. The intermediary now segments the market by selectively releasing information to the seller, who still controls the auction. This process is called signaling and allows the seller to price discriminate. Though one would expect signaling to always help the seller, in the setting with one buyer, Bergemann, Brooks, and Morris [AER 2015] showed a remarkable result: Signaling can maximally help the buyer without really helping the seller. Specifically, there exists a signaling scheme where the seller’s revenue does not increase but the item always sells, thereby maximizing the consumer (buyer) surplus. In this talk, we will explore whether such a result is possible in more general settings: First, when the type space of the buyer is “inter-dimensional” with a private budget or deadline in addition to a private value, and second, when there are multiple buyers in the auction. On the positive side, we show exact and approximation results via new signaling schemes, while on the negative side, we show impossibility results that capture the limits to which information intermediaries can help the buyers. Joint work with Reza Alijani, Sid Banerjee, Shao-Heng Ko, and Kangning Wang, and combines two papers that appeared in ACM EC 2022.

Bio: Kamesh Munagala is Professor of Computer Science at Duke University. He obtained his Ph.D. in Computer Science from Stanford University in 2003 and has been at Duke since 2004. He is broadly interested in theoretical computer science, specifically approximation and online algorithms, algorithmic game theory, and fairness. He is an ACM Distinguished Scientist (2019); a recipient of the NSF CAREER Award (2008) and the Alfred P. Sloan Research Fellowship (2009); and co-author on the best papers at the WINE 2018 and WWW 2009 conferences. He was a Visiting Research Professor at Twitter in 2012, served as the Director of Graduate Studies for the Duke CS department from 2012 to 2015, and currently serves as its Associate Chair.