2020: The 16th Conference on Web and Internet Economics

December 7-11, 2020, Peking University, Beijing

 

Keynote Speakers

The scientific program of WINE 2020 will include four invited talks. The confirmed speakers are:

  • Eric Budish, University of Chicago

  • Yiling Chen, Harvard University

  • Jose Correa, Universidad de Chile

  • Constantinos Daskalakis, Massachusetts Institute of Technology 

See below for more information.

Eric Budish

Speaker: Eric Budish, University of Chicago Booth School of Business

Title: The Economic Limits of Bitcoin and the Blockchain

Abstract:The amount of computational power devoted to anonymous, decentralized blockchains such as Bitcoin’s must simultaneously satisfy zero-profit condition and incentive compatibility condition. The constraint is softer if both the mining technology used to run the blockchain is non-repurposable and any majority attack is a “sabotage”; however, reliance on non-repurposable technology for security and vulnerability to sabotage each raise their own concerns, and point to specific collapse scenarios. The model suggests that Bitcoin would be majority attacked if it became sufficiently economically important which suggests that there are intrinsic economic limits to how economically important it can become in the first place.

Short Bio:

Eric Budish is the Steven G. Rothmeier Professor of Economics at the University of Chicago, Booth School of Business, a Research Associate at the National Bureau of Economic Research, and Co-Director of the Initiative on Global Markets at Chicago Booth. Budish’s main area of research is market design, with specific topics studied including financial markets, matching markets, ticket markets, cryptocurrencies, and incentives for innovation. Budish’s research on high-frequency trading and the design of financial exchanges received the AQR Insight Award and the Leo Melamed Award, has been discussed in major policy addresses by the NY Attorney General and the SEC Chair, and has influenced exchange design proposals in both stock markets and futures markets. Budish’s dissertation research, which proposed a new market design for the matching problem of assigning students to schedules of courses, or workers to schedules of shifts, has been implemented at several leading professional schools. Budish’s research on patent design and cancer R&D received the Kauffman/iHEA Award for Health Care Entrepreneurship and Innovation Research and the Arrow Award for the best paper in Health Economics. Budish’s most recent research concerns the economic limitations of bitcoin and the blockchain. Budish received his PhD in Business Economics from Harvard University, his MPhil in Economics from Oxford (Nuffield College), and his BA in Economics and Philosophy from Amherst College. Prior to graduate school, Budish was an analyst at Goldman Sachs. Budish’s honors include the Marshall Scholarship, the Sloan Research Fellowship, and giving the 2017 AEA-AFA joint luncheon address.


Yiling Chen

Speaker: Yiling Chen, Harvard University

Title: Challenges of Incorporating Algorithms into Decision Making: Fairness, Welfare and Disparate Interactions

Abstract:Algorithms have entered the center of many decision-making processes, either by providing predictions or assessments to facilitate human decision making or, in some scenarios, suggesting decisions directly. In this talk, I will discuss the importance of examining algorithmic decision-making and algorithm-facilitated decision making in the broader context of intended applications and in the lens of human-algorithm interactions. I will first present a welfare-based analysis of fair classification algorithms to understand the welfare impact of fairness-constrained classification algorithms in the context of financial lending. Then, I’ll discuss a sequence of controlled human-subject experiments studying how the interactions between people and algorithms influence human decision making.

Short Bio:

Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard University. She received her Ph.D. in Information Sciences and Technology from the Pennsylvania State University. Prior to working at Harvard, she spent two years at Yahoo! Research in New York City. Her research focuses on topics in the intersection of computer science, economics and other social sciences. She was a recipient of NSF Career award and and The Penn State Alumni Association Early Career Award, and was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch" in 2011. Her work received best paper awards at ACM EC, AAMAS, ACM FAT* (now ACM FAccT) and ACM CSCW conferences. She has co-chaired the 2013 Conference on Web and Internet Economics (WINE’13), the 2016 ACM Conference on Economics and Computation (EC’16) and the 2018 AAAI Conference on Human Computation and Crowdsourcing (HCOMP’18) and has served as an associate editor for several journals.


Jose Correa

Speaker: Jose Correa, Universidad de Chile

Title: Optimal stopping from sampled data

Abstract:Basic optimal stopping problems such as the secretary problem or prophet inequalities provide an idealized ground to study online decision making under stochastic input. The basic idea is to design good stopping rules for a given problem whose reward is, in expectation, close to what a prophet, that can foresee the whole instance in advance, can get. The area has been very active in the last decade because of its connections to mechanism design and other fundamental problems. In this talk I will review some of these recent results and will discuss new variants of the problem in which the distributional information is replaced by the availability of samples taken from the input.

Short Bio:

José Correa is a full professor in the Department of Industrial Engineering at Universidad de Chile. Jose obtained a mathematical engineering degree from Universidad the Chile in 1999 and a PhD in Operations Research from MIT in 2004. His research, focusing in algorithmic game theory and mechanism design, has received awards including an ACM SIGecom best paper award, an INFORMS Transportation Science and Logistics best paper awards, a Tucker prize finalist, and research awards from Amazon and Google. José also serves and has served in the editorial board of some of the leading journals of his field: Mathematical Programming B, Mathematics of Operations Research (as Game Theory Area Editor), and Operations Research.


Constantinos Daskalakis

Speaker: Constantinos Daskalakis, Electrical Engineering and Computer Science,MIT

Title: Equilibrium Computation and the Foundations of Deep Learning

Abstract:Deep Learning has recently yielded important advances in single-agent learning challenges, much of that progress being fueled by the empirical success of gradient descent and its variants in computing local optima of non-convex optimization problems. In multi-agent learning applications, the role of single-objective optimization is played by equilibrium computation, yet our understanding of its complexity in settings that are relevant for Deep Learning remains sparse. In this talk we focus on min-max optimization of nonconvex-nonconcave objectives, which has found applications in GANs, and other adversarial learning problems. Here, not only are there no known gradient-descent based methods converging to even local and approximate min-max equilibria, but the computational complexity of identifying them remains poorly understood. We show that finding approximate local min-max equilibria of Lipschitz and smooth objectives requires a number of queries to the function and its gradient that is exponential in the relevant parameters, in sharp contrast to the polynomial number of queries required to find approximate local minima of non-convex objectives. Our oracle lower bound is a byproduct of a complexity-theoretic result showing that finding approximate local min-max equilibria is computationally equivalent to finding Brouwer fixed points, and Nash equilibria in non zero-sum games, and thus PPAD-complete. Minimal complexity theory knowledge will be assumed in the talk. Joint work with Stratis Skoulakis and Manolis Zampetakis

Short Bio:

Constantinos (aka "Costis") Daskalakis is a Professor of Electrical Engineering and Computer Science at MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, and a PhD in Electrical Engineering and Computer Science from UC Berkeley. He works on Computation Theory and its interface with Game Theory, Economics, Probability Theory, Machine Learning and Statistics. He has resolved long-standing open problems about the computational complexity of Nash equilibrium, and the mathematical structure and computational complexity of multi-item auctions. His current work focuses on high-dimensional statistics and learning from biased, dependent, or strategic data. He has been honored with the ACM Doctoral Dissertation Award, the Kalai Prize from the Game Theory Society, the Sloan Fellowship in Computer Science, the SIAM Outstanding Paper Prize, the Microsoft Research Faculty Fellowship, the Simons Investigator Award, the Rolf Nevanlinna Prize from the International Mathematical Union, the ACM Grace Murray Hopper Award, and the Bodossaki Foundation Distinguished Young Scientists Award.