The rules, information structure, and types of economic behavior in online markets impact our ability to estimate and learn about participants' preferences and market outcomes. In turn, learning about preferences and behavioral biases allows us to improve the ways in which markets operate, reducing market frictions and otherwise improving market performance. This is the promise of empirical market design.
One research direction involves estimating preferences and outcomes in two-sided markets using structural models, as well as the design of experiments (the two-sidedness as well as the dynamic nature of these markets provide technical and practical challenges). Similar challenges arise in online markets, where machine learning is used by market participants, as well as being central to the algorithms that run these markets. At the same time, digital markets provide new opportunities for rapid iteration through large-scale, randomized experiments.
A second research direction considers markets with participants that follow behavioral models of decision making, perhaps with simple adaptive behaviors, though not much is known about inference in matching environments with externalities. Here too, it will be interesting to couple the ability to learn about preferences and about systematic deviations from optimal choices with questions about design, including whether through careful design we can enable better inference about participants and reach more desirable outcomes.
There is also emerging theoretical and experimental work on information design for markets and mechanisms—which participants should receive which information, and when—along with the design of the methods of information elicitation. Both information design and the methods of information elicitation are major tools in reducing marketplace frictions.
This workshop will emphasize research in these three directions. We anticipate participation by leading scholars from economics, computer science, and operations research, in the evolving areas of empirical market design, machine learning and inference, and information acquisition and information design aspects of mechanism design and matching market design.
As with the second workshop, we also plan for participation by experts from industry, to add to the discussions. The goal of this workshop is to learn about research frontiers and create discussion between the scholars in these areas, aimed at studying challenges, both in the present and in the future.
All events take place in the Calvin Lab auditorium.
The first and last days of the workshop (Monday, September 30 and Thursday, October 3) will have discussion sessions, but no formal talks. Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-market3 [at] lists [dot] simons [dot] berkeley [dot] edu (at this address).
Registration is required to attend this workshop. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the workshop. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.
Nicholas Arnosti (Columbia University), Itai Ashlagi (Stanford University), Francis Bloch (Paris School of Economics), Yves Breitmoser (Bielefeld University), Caterina Calsamiglia (University of Pompeu Fabra), Yan Chen (University of Michigan), John Dickerson (University of Maryland), Laura Doval (Caltech), Federico Echenique (California Institute of Technology), Dean Eckles (Massachusetts Institute of Technology (MIT)), Yuri Faenza (Columbia University), Chiara Farronato (Harvard Business School), Karthik Gajulapalli (UC Irvine), Vasilis Gkatzelis (Drexel University), Kira Goldner (University of Washington), Nika Haghtalab (Microsoft Research), Jason Hartline (Northwestern University), Yinghua He (Rice University), Ori Heffetz (Cornell), Charles Hodgson (Stanford University), Nicole Immorlica (Microsoft Research), Ravi Jagadeesan (Harvard University), Ramesh Johari (Stanford University), Yash Kanoria (Columbia University), SangMok Lee (Washington University in St. Louis), Shengwu Li (Stanford University), Annie Liang (University of Pennsylvania), Irene Lo (Stanford University), James Lui (UC Irvine), 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), Hamid Nazerzadeh (Uber & University of Southern California), Afshin Nikzad (UC Berkeley), Oren Reshef (UC Berkeley), Amin Saberi (Stanford University), Daniel Schoepflin (Drexel University), Sebastian Schweighofer-Kodritsch (Humboldt-Universität zu Berlin), Philipp Strack (UC Berkeley), Steven Tadelis (UC Berkeley), Garrett van Ryzin (Cornell Tech & Lyft), Vijay Vazirani (UC Irvine), Gideon Weiss (The University of Haifa), Adam Wierman (California Institute of Technology), Yi Xin (Caltech), Richard Xu (USC)