Matching and Objectives
Organizers:
Matching theory has been applied widely: it is used as a normative guide in the emerging field of market design, and as a positive tool offering predictions that can be tested in sociological and economic investigations.
Over the past two decades, the literature has evolved in several directions. One direction is the study of decentralized markets, ones in which participants interact with little market guidance. Recent research explores the impact of dynamics, the interaction of demographics with matching outcomes, as well as the impact of incomplete information.
Another direction is the design and exploration of centralized matching clearinghouses. Economists and computer scientists have played an important role in the practical organization and design of many centralized markets, including centralized systems for the allocation of organs, procedures for student assignment to schools, and modifications of the protocol matching newly-minted doctors to hospitals.
Finally, the emergence of online matching platforms has opened new spaces for innovation, and new approaches to market design. We now have online matching markets for ride sharing, dating, vacation homes, used goods, real estate, various jobs, and many other applications. Economists and computer scientists have already played an important role in the design of online advertising and auctions. There is now an opportunity to both learn from and influence the design of these new matching platforms.
The analysis of matching markets often utilizes a combination of theory, empirics, and lab experiments. This workshop aims to bring together scholars specializing in these varied methodologies to discuss frontier topics in the study and design of matching markets. The workshop will be multi-disciplinary, engaging leaders from different fields—economics, computer science, and operations research.
The first day of the workshop (Wednesday, September 4) will have discussion sessions but no formal talks.
Nicholas Arnosti (Columbia University), Itai Ashlagi (Stanford University), Sid Banerjee (Cornell University), Caterina Calsamiglia (University of Pompeu Fabra), Yeon-Koo Che (Columbia University), Gabrielle Demange (Paris School of Economics), Laura Doval (Caltech), Federico Echenique (California Institute of Technology), Yuri Faenza (Columbia University), Daniel Freund (MIT), Karthik Gajulapalli (UC Irvine), Vasileios Gkatzelis (Drexel University), Kira Goldner (University of Washington), Chamsi Hssaine (Cornell University), Zhiyi Huang (The University of Hong Kong), Nicole Immorlica (Microsoft Research), Ravi Jagadeesan (Harvard University), Kamal Jain (Faira.com), Ramesh Johari (Stanford University), Yash Kanoria (Columbia University), Bettina Klaus (University of Lausanne), SangMok Lee (Washington University in St. Louis), Jacob Leshno (University of Chicago), Irene Lo (Stanford University), James Lui (UC Irvine), Gregory Macnamara (Stanford University), Tung Mai (UC Irvine), Vahideh Manshadi (Yale University), Nimrod Megiddo (IBM Almaden Research Center), Aranyak Mehta (Google), Divyarthi Mohan (Princeton University), Seffi NAOR (Technion – Israel Institute of Technology), Afshin Nikzad (UC Berkeley), Oren Reshef (UC Berkeley), Al Roth (Stanford University), Daniela Saban (Stanford University), Amin Saberi (Stanford University), Daniel Schoepflin (Drexel University), Balu Sivan (Google Research), Philipp Strack (UC Berkeley), Steven Tadelis (UC Berkeley), Alexander Teytelboym (Oxford University), Vijay Vazirani (UC Irvine), David Wajc (Carnegie Mellon University), Gideon Weiss (The University of Haifa), Adam Wierman (California Institute of Technology), Yi Xin (Caltech), Richard Xu (USC)
