David Pennock, Ph.D.David Pennock is a Principal Research Scientist at Yahoo! Research in New York City, where he leads a group focused on algorithmic economics. He has over sixty academic publications relating to computational issues in electronic commerce and the web, including papers in PNAS, Science, AI Magazine, IEEE Computer, Theoretical Computer Science, Algorithmica, Electronic Commerce Research, Electronic Markets, AAAI, EC, WWW, KDD, UAI, SIGIR, ICML, NIPS, INFOCOM, SAINT, ACM SIGCSE, and VLDB. He has authored two patents and ten patent applications. In 2005, he was named to MIT Technology Review's list of 35 top technology innovators under age 35. Prior to his current position at Yahoo!, Pennock worked as a research scientist at NEC Laboratories America, a research intern at Microsoft Research, and in 2001 served as an adjunct professor at Pennsylvania State University. His work has been featured in Discover Magazine, New Scientist, CNN, the New York Times, the Economist, James Surowiecki's "The Wisdom of Crowds," and several other publications. He is currently blogging on economics, politics, and technology across the Yahoo! platform and is co-editor/author of The Signal.
Education
1995-1999: University of Michigan ... Ph.D. in Computer Science
1993-1995: Duke University ... M.S. in Computer Science
1989-1993: Duke University ... B.S. in Physics
Read more at David Pennock's professional home page, personal home page, or personal blog.
A few academic papers that may be of interest to readers of The Signal:
Predicting consumer behavior with Web search (with Sharad Goel, Jake Hofman, Sebastien Lahaie, and Duncan Watts). In Proceedings of the National Academy of Sciences, 2010. We show that what consumers are searching for online can predict their collective future behavior days or even weeks in advance. We use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are predictive of future outcomes.
Designing Markets for Prediction (with Yiling Chen). In AI Magazine, 2010. We survey the literature on prediction mechanisms, including prediction markets and peer prediction systems. We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms.
Prediction Without Markets (with Sharad Goel, Daniel Reeves, and Duncan Watts). In ACM Conference on Electronic Commerce, 2010. We compare the performance of prediction markets to polls and statistical models, examining thousands of sporting and movie events. All these domains exhibit remarkably steep diminishing returns to information, with nearly all the predictive power captured by only two or three parameters.