New York Computer Science and Economics Day 2011
Friday, September 16, 2011
NYCE 2011 is the fourth annual New York Computer Science and Economics Day. The goal of the meeting is to bring together researchers in the larger New York metropolitan area with interests in computer science, economics, marketing, and business, and with a common focus on understanding and developing the economics of Internet activity. Examples of topics of interest include theoretical, modeling, algorithmic, and empirical work on advertising and marketing based on search, user-generated content, or social networks, and other means of monetizing the Internet.
Location
NYCE 2011 will be held at
Kimmel Center
New York University
60 Washington Square South
New York, NY 10012
Directions to the Kimmel Center
Organizing Committee
Xi Chen
Columbia University
Richard Cole
New York University
Gagan Goel
Google Inc.
Sharad Goel
Yahoo Inc.
Mallesh Pai
University of Pennsylvania
Registration Pricing
| Advance | Onsite: 9/16/2011 | |
| General | $20 | $30 |
| Student / Postdoc / Fellow | $10 | $20 |
Agenda
* Presentation times are subject to change.
Friday, September 16, 2011 | |
9:00 AM | Welcome |
9:15 AM | Economics and Machine Learning |
10:15 AM | Payment Rules for Combinatorial Auctions via Structural Support Vector Machines |
11:00 AM | Break |
11:30 AM | Computing Game-Theoretic Solutions for Security |
12:15 PM | Lunch and Poster Session |
1:45 PM | Short Talks |
3:00 PM | Learning from Seller Experiments in Online Markets |
4:00 PM | Welfare and Revenue in Ad Auctions |
5:00 PM | Adjourn |
Speakers
Vincent Conitzer
Duke University
Vincent Conitzer is the Sally Dalton Robinson Professor of Computer Science and Professor of Economics at Duke University. His research focuses on computational aspects of microeconomic theory, in particular game theory, mechanism design, voting/social choice, and auctions. He recently received the IJCAI Computers and Thought Award, which is awarded to outstanding young scientists in artificial intelligence.
Jonathan Levin
Stanford University
Jonathan Levin is Professor and Chair of the Department of Economics at Stanford University. His research is in the field of industrial organization, particularly the design of market institutions. He recently won the American Economic Association's John Bates Clark Medal as the outstanding economist under the age of 40.
Preston McAfee
Yahoo! Research
Preston McAfee is vice president and research fellow at Yahoo! Research in Burbank, CA, where he leads a group focused on microeconomics research. From 2004 to 2007, McAfee was the J. Stanley Johnson Professor of Business, Economics, and Management at the California Institute of Technology. He wrote Introduction to Economic Analysis, a free, open-source text that spans both principles and intermediate microeconomics. McAfee is the author of over 70 articles published in scholarly economics journals, many on the topic of auctions and bidding, and coauthor of the book Incentives in Government Procurement.
McAfee served as one of four economists who edited the American Economic Review, the most prominent economics journal, for over nine years and serves as an associate editor of Theoretical Economics, a new open-access journal. He is a Fellow of the Econometric Society. McAfee taught business strategy at the University of Chicago's Graduate School of Business in 2000-2001 and is the author of Competitive Solutions: The Strategist's Toolkit, published by Princeton University Press in 2003.
David Parkes
Harvard University
Eva Tardos
Cornell University
Abstracts
Computing Game-Theoretic Solutions for Security
Vincent Conitzer, PhD, Duke University
Algorithms for computing game-theoretic solutions are now deployed in real-world security domains, notably air travel. These applications raise some hard questions. How do we deal with the equilibrium selection problem? How is the temporal and informational structure of the game best modeled? What assumptions can we reasonably make about the utility functions of the attacker and the defender? And, last but not least, can we make all these modeling decisions in a way that allows us to scale to realistic instances? I will present our ongoing work on answering these questions.
Coauthors: Dmytro Korzhyk, Joshua Letchford, Kamesh Munagala, Ronald Parr (Duke University); Manish Jain, Zhengyu Yin, Milind Tambe (University of Southern California); Christopher Kiekintveld (University of Texas El Paso); Ondrej Vanek, Michal Pechoucek (Czech Technical University); and Tuomas Sandholm (Carnegie Mellon University)
Payment Rules for Combinatorial Auctions via Structural Support Vector Machines
David Parkes, PhD, Harvard University
How can machine learning be used for the design of mechanisms? In this talk, I will outline a new connection in which structural support vector machines are used for the design of payment rules for combinatorial auctions. Given an algorithm that defines an allocation rule, the proposed methodology first constructs training data according to a prior on agent valuations, and then trains a classifier, where the discriminant function of the classifier provides a payment rule. For an appropriately constrained hypothesis space, an exact classifier generates a strategyproof mechanism, while minimizing regularized empirical error training error corresponds to minimizing a regularized upper bound on ex post regret. I present experimental results on single-minded and multi-minded distributions and also provide a discussion of ex post IR properties, along with other connections between kernel methods and combinatorial auctions.
Coauthors: Paul Duetting, Felix Fischer, Pichayut Jirapinyo, John Lai, and Benjamin Lubin
Learning from Seller Experiments in Online Markets
Jonathan Levin, PhD, Stanford University
The internet has dramatically reduced the cost of varying prices, displays, and information provided to consumers, facilitating both active and passive experimentation. We document the prevalence of targeted pricing and auction design variation on eBay and identify hundreds of thousands of experiments across a wide array of retail products. We show how this type of data can be used to address questions about consumer behavior and market outcomes. We argue that leveraging the experiments of others can be a powerful approach to economic measurement that takes advantage of the scale and heterogeneity of online markets.
Welfare and Revenue in Ad Auctions
Éva Tardos, PhD, Cornell University
The Generalized Second Price Auction for selling advertisement space has been the main mechanism used to monetize Internet services. These auctions give rise to a number of interesting challenges not traditionally considered by auction theory, e.g., search advertising gives rise of an enormous number of auctions running simultaneously, limiting the applicability of traditional auction theory. In this talk we will talk about various models for this auction and consider the social welfare and revenue of the Nash equilibria of this game in various models, including the full information setting, a information setting, and in the Bayesian setting.