Predicting the outcome of future events is a fundamental problem in fields as varied as computer science, finance, political science and others. To this end, the Wisdom of Crowds principle says that an aggregate of many crowdsourced predictions can significantly outperform even expert individuals or models. In this talk, I will focus on the problem of accurately eliciting these predictions using wagering mechanisms, where participants provide both a probabilistic prediction of the event in question, and a monetary wager that they are prepared to stake.
I will discuss recent progress on the design and analysis of wagering mechanisms. In particular, I will focus on some surprising applications of wagering mechanisms. First, they can be used to select a winner in a winner-takes-all forecasting competition, which turns out to be quite different to the standard setting where we can reward everyone. Similar techniques additionally allow us to design incentive compatible, or truthful, algorithms for the fundamental machine learning problem of prediction from expert advice. Existing algorithms for this problem may not induce truthful reports when the experts value the influence they have on the algorithm’s prediction. Finally, I'll show that any wagering mechanism defines a corresponding allocation mechanism for dividing scarce resources among competing agents, a seemingly unrelated problem. This correspondence immediately leads to advances in both areas.
Bio: Rupert Freeman is an Assistant Professor of Business Administration in the Quantitative Analysis area at the University of Virginia’s Darden School of Business. He received his Ph.D. from Duke University in 2018 and spent two years as a postdoctoral researcher at Microsoft Research New York City. His research focuses on the intersection of artificial intelligence, operations research, and economics, particularly in topics such as resource allocation, voting, and information elicitation. He is the recipient of a Facebook Ph.D. Fellowship and a Duke Computer Science outstanding dissertation award.