The seminar is organized by Ozan Candogan (Chicago Booth)
, Vahideh Manshadi (Yale)
, and Fanyin Zheng (Columbia).
The seminar will be bi-weekly on Fridays at 1-2 pm ET (10-11 am PT).
If this seminar interests you and you would like to be notified of upcoming speakers, you can join our email list.
October 02 - Alvin Roth
Title: Kidney Exchange: an Operations Perspective
Abstract: Many patients in need of a kidney transplant have a willing but incompatible (or poorly matched) living donor. Kidney exchange programs arrange exchanges among such patient-donor pairs, in cycles and chains of exchange, so each patient receives a compatible kidney. Kidney exchange has become a standard form of transplantation in the United States and a few other countries, in large part because of continued attention to the operational details that arose as obstacles were overcome and new obstacles became relevant. We review some of the key operational issues in the design of successful kidney exchange programs. Kidney exchange has yet to reach its full potential, and the paper further describes some open questions that we hope will continue to attract attention from researchers interested in the operational aspects of dynamic exchange.
October 16 - John Birge
(University of Chicago)
Title: Increasing Efficiency in Electricity Market Auctions
Abstract: Electricity markets in much of the world are organized with day-ahead and real-time components for energy and in some place regular auctions of financial transmission rights that depend on the energy prices. The form of these organizations leads to a degree of inherent efficiency and instability due to misalignments of incentives. This talk will describe potential mechanisms for alleviating these inefficiencies including the inclusion of some form of stochastic clearing, flexibility in the forms of bids, and penalties for inefficiencies caused by speculative bidders.
October 30 - Jon Kleinberg
Title: Fairness and Bias in Algorithmic Decision-Making
Abstract: As algorithms trained via machine learning are increasingly used as a component of screening decisions in areas such as hiring, lending, and education, discussion in the public sphere has turned to the question of what it means for algorithmic classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs between these conditions. We also explore how the complexity of a classification rule interacts with its fairness properties, showing how natural ways of approximating a classifier via a simpler rule can act in conflict with fairness goals. The talk will be based on joint work with Jens Ludwig, Sendhil Mullainathan, Manish Raghavan, and Cass Sunstein.
November 13 - Winners of the Michael H. Rothkopf Junior Researcher Paper Prize
December 04 - Asuman Ozdaglar
December 18 - Matthew Jackson