New! Apply now for a postdoctoral research position in Algorithmic Economics at MSR-NYC.
Market design, the engineering arm of economics, benefits from an understanding of computation: complexity, algorithms, engineering practice, and data. Conversely, computer science in a networked world benefits from a solid foundation in economics: incentives and game theory.
Increasingly, online service design teams require dual expertise in social science and computer science, adding competence in economics, sociology, and psychology to more traditionally recognized requirements like algorithms, interfaces, systems, machine learning, and optimization. Our researchers combine expertise in computer science and economics to bridge the gap between modeling human behavior and engineering web-scale systems.
Scientists with hybrid expertise are crucial as social systems of all types move to electronic platforms, as people increasingly rely on programmatic trading aids, as market designers rely more on equilibrium simulations, and as optimization and machine learning algorithms become part of the inner loop of social and economic mechanisms.
Application areas include auctions, crowdsourcing, gaming, information aggregation, machine learning in markets, market interfaces, market makers, monetization, online advertising, optimization, polling, prediction engines, preference elicitation, scoring rules, and social media.
- Miroslav Dudik, Sebastien Lahaie, and David Pennock, A Tractable Combinatorial Market Maker Using Constraint Generation, in ACM Conference on Electronic Commerce, 2012
- Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan, Efficient Market Making via Convex Optimization, and a Connection to Online Learning, in ACM Transactions on Economics and Computation (To appear), 2012
- Alina Beygelzimer, John Langford, and David Pennock, Learning performance of prediction markets with Kelly bettors, International Conference on Autonomous Agents and Multiagent Systems, 2012
- Yiling Chen, Mike Ruberry, and Jennifer Wortman Vaughan, Designing Informative Securities, in 28th Conference on Uncertainty in Artificial Intelligence (UAI), 2012
- Quang Duong and Sebastien Lahaie, Discrete Choice Models of Bidder Behavior in Sponsored Search, in Workshop on Internet and Network Economics (WINE), 2011
- David Pennock and Lirong Xia, Price updating in combinatorial prediction markets with Bayesian networks, Conference on Uncertainty in Artificial Intelligence, 2011
- Sebastien Lahaie and Preston McAfee, Efficient Ranking in Sponsored Search, in Workshop on Internet and Network Economics (WINE), 2011
- Sebastien Lahaie, A Kernel-Based Iterative Combinatorial Auction, in National Conference on Artificial Intelligence (AAAI), 2011
- Lirong Xia and David Pennock, An efficient Monte-Carlo algorithm for pricing combinatorial prediction markets for tournaments, International Joint Conference on Artificial Intelligence, 2011
- Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan, An Optimization-Based Framework for Automated Market-Making, in Twelfth ACM Conference on Electronic Commerce (EC), 2011
- Sharad Goel, Jake Hofman, Sebastien Lahaie, David Pennock, and Duncan Watts, Predicting Consumer Behavior with Web Search, in Proceedings of the National Academy of Sciences (PNAS), 2010
- Yiling Chen and Jennifer Wortman Vaughan, A New Understanding of Prediction Markets Via No-Regret Learning, in Eleventh ACM Conference on Electronic Commerce (EC), 2010
- Abe Othman, Tuomas Sandholm, David Pennock, and Daniel Reeves, A practical liquidity-sensitive automated market maker, 2010
- Sebastien Lahaie, Kernel Methods for Revealed Preference Analysis, in European Conference on Artificial Intelligence (ECAI), 2010
- Sebastien Lahaie, Stability and Incentive Compatibility in a Kernel-Based Combinatorial Auction, in National Conference on Artificial Intelligence (AAAI), 2010
- Sharad Goel, Sebastien Lahaie, and Sergei Vassilvitskii, Contract Auctions for Sponsored Search, in Workshop on Internet and Network Economics (WINE), 2009
