Abstract:
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 some recent progress on the design and analysis of wagering mechanisms. In particular, I will focus on two surprising applications of wagering mechanisms. First, they can be used to design incentive compatible, or truthful, algorithms for the 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. Second, 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.
This talk is based on joint work with David Pennock, Chara Podimata, and Jennifer Wortman Vaughan.