Using behavioral data to understanding market outcomes Using behavioral data to understanding market outcomes

Special Quarter: Data Science & Online Markets - April 15 to June 15


In recent years many aspects of social engagement has shifted to what can be broadly thought of as online markets. These markets; which include eBay, Uber, Airbnb, Tinder, StubHub, Wikipedia, Amazon’s Mechanical Turk, etc.; are run by technology companies and are built by teams of software engineers. The science of designing and optimizing these online markets, however, is underdeveloped. Designing a marketplace that works well is challenging because the behavior of participants in the market place depends on the design of the marketplace. For example, a seller in eBay chooses among various sale formats including “buy it now” or the traditional eBay auction, how much to charge for shipping and handling, and other information to give with the listing; a buyer chooses which items to bid on, how much to bid, and when to place a bid. These actions by the participants in the marketplace are done strategically and and the choice of action depends on the propensity of the action to lead the participant to desired outcomes. Importantly, when the rules of the marketplace are changed, the actions taken by the participants may change in response. This strategic effect makes using behavioral data to understanding market outcomes and adapting the market rules — with methods from data science and machine learning — non straightforward. The goal if this special quarter is to conduct a broad study of data science and online markets from the combined perspective of the fields of algorithms, machine learning, mechanism design, and econometrics.


Jacob Abernethy (Bill and Jeanne Bliss Visiting Assistant Professor) is an assistant professor in computer science at Georgia Tech. He started his faculty career in the Department of Electrical Engineering and Computer Science at the University of Michigan. In October 2011 he finished a PhD in the Division of Computer Science at the University of California at Berkeley, and then spent nearly two years as a Simons postdoctoral fellow at the CIS department at UPenn, working with Michael Kearns. Abernethy's primary interest is in machine learning, with a particular focus in sequential decision making, online learning, online algorithms and adversarial learning models. He did his Master's degree at TTI-C, and his Bachelor's Degree at MIT. Abernethy's PhD advisor was Prof. Peter Bartlett.

Constantinos Daskalakis (Bill and Jeanne Bliss Visiting Associate Professor) is an associate professor of computer science and electrical engineering at MIT. He holds a diploma in electrical and computer engineering from the National Technical University of Athens, and a Ph.D. in electrical engineering and computer sciences from UC-Berkeley. His research interests lie in theoretical computer science and its interface with economics, probability, learning and statistics. He has been honored with the 2007 Microsoft Graduate Research Fellowship, the 2008 ACM Doctoral Dissertation Award, the Game Theory and Computer Science Prize from the Game Theory Society, the 2010 Sloan Fellowship in Computer Science, the 2011 SIAM Outstanding Paper Prize, the 2011 Ruth and Joel Spira Award for Distinguished Teaching, the 2012 Microsoft Research Faculty Fellowship, and the 2015 Research and Development Award by the Vatican Giuseppe Sciacca Foundation. He is also a recipient of Best Paper awards at the ACM Conference on Economics and Computation in 2006 and in 2013.

Jason Hartline is an associate professor of computer science at Northwestern University. His research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation which is under preparation. Prof. Hartline received his Ph.D. in 2003 from the University of Washington under the supervision of Anna Karlin. He was a postdoctoral fellow at Carnegie Mellon University under the supervision of Avrim Blum; and subsequently a researcher at Microsoft Research in Silicon Valley. He joined Northwestern University in 2008.

Denis Nekipelov (McCormick Advisory Council Visiting Associate Professor) is an associate professor of economics and computer science at the University of Virginia, a member of the advisory board of the Data Science Institute, and a visiting researcher at Microsoft Research. Nekipelov's work is aimed at the analysis and theory of scalable and efficient techniques that can be used to estimate the models of strategic behavior in data-rich settings. His work also analyzes the limitations in empirical implementation of strategic behavior models. In particular, some models may be sufficiently rich to provide a good description of the data, but provide very non-robust predictions regarding the behavior of Economic agents making the policy analysis and implementation very hard if not impossible. Nekipelov received his B.Sc. and M.Sc. in Applied Physics and Mathematics at Moscow Institute of Physics and Technology and his Ph.D. in Economics from Duke University.

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