- Vijay Krishna, PennState, Pennsylvania, USA.
Title: Communication and Cooperation (joint with Yu Awaya)
We study the role of communication in sustaining cooperation in repeated interactions. Specifically, we identify conditions under which, without communication the prospects for cooperation are limited because of poor monitoring---all equilibrium are bounded away from the efficient frontier. We show, however, that with cheap-talk communication, nearly efficient cooperation can be sustained as an equilibrium. In this sense communication is necessary for cooperation.
Vijay Krishna is a Professor of Economics at Penn State University. He has worked in a variety of areas of economic and game theory: auctions and mechanism design, bargaining, communication, learning, repeated games and voting.He is the author of Auction Theory, a standard reference on the subject.
- Parag Pathak, Massachusetts Institute of Technology, Cambridge, USA.
Title: Reserve Design in Affirmative Action: Boston, Chicago, and India’s Creamy Layer
Affirmative action is one of the most contentious issues in social policy, nowhere more so than in the context of admissions to selective educational institutions. In this talk, I will discuss several papers motivated by algorithmic details related to the implementation of reserves in affirmative action systems in Boston, Chicago, and India. Boston’s neighborhood reservation unintentionally disadvantaged neighborhood residents and ultimately led to its demise in 2013. Chicago’s new race-neutral system provides an additional boost to applicants from the most disadvantaged neighborhoods beyond the reservation due to algorithmic processing. I will also discuss how these issues are relevant for India's “Creamy Layer” debate on the equitable distribution of reserves.
Parag A. Pathak is the Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics at MIT, founding co-director of the NBER Working Group on Market Design, and founder of MIT's School Effectiveness and Inequality Initiative (SEII), a laboratory focused on education, human capital, and the income distribution. Pathak has helped to design the Boston, Chicago, Denver, Newark, New Orleans, New York, and Washington DC school choice systems. His work on market design and education was garnered numerous recognitions including a Presidential Early Career Award for Scientists and Engineers and the 2016 Social Choice and Welfare prize. He has also authored leading studies on charter schools, high school reform, selective education, and school vouchers. Pathak is a Fellow of the Econometric Society, and has served on the editorial boards of Econometrica, American Economic Review, and the Journal of Political Economy.
- Ariel Procaccia, Carnegie Mellon University, Pittsburg, USA.
- Tim Roughgarden, Stanford University, Stanford, USA.
Ariel Procaccia is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He usually works on problems at the interface of computer science and economics. His distinctions include the IJCAI Computers and Thought Award (2015), the Sloan Research Fellowship (2015), the NSF Faculty Early Career Development Award (2014), and the IFAAMAS Victor Lesser Distinguished Dissertation Award (2009); as well as half a dozen paper awards, including Best Paper (2016) and Best Student Paper (2014) at the ACM Conference on Economics and Computation (EC). He is co-editor of the Handbook of Computational Social Choice (Cambridge University Press, 2016).
Title: Data-Driven Optimal Auction Theory
The traditional economic approach to revenue-maximizing auction design posits a known prior distribution over what bidders are willing to pay, and then solves for the auction that maximizes the seller's expected revenue with respect to this distribution.But where does this distribution come from? One obvious answer is from data, in the form of previous bids by comparable bidders in auctions for comparable items. We survey recent work that develops theory to help reason about questions such as: (i) how much data is necessary and sufficient to identify a near-optimal auction? (ii) what is the optimal way to use data? (iii) are there fundamental trade-offs between auction complexity and auction optimality?
(Includes joint work with Richard Cole, Zhiyi Huang, Yishay Mansour, Jamie Morgenstern, and Josh Wang)