A wagering mechanism is a zero-sum competition among forecasters. Many wagering mechanisms exist, starting with parimutuel wagering in 1867, and today handle billions of dollars in trade. Recently, we invented the first *truthful* wagering mechanisms (2008, 2014, 2017). Truthful wagering mechanisms are designed to elicit subjective probabilities and aggregate them, leveraging the "wisdom of crowds". I will present the Double-Clinching Auction (2017), the first wagering mechanism that is truthful and close to Pareto efficient. I will then present three surprising applications of the math of wagering mechanisms, but without money or even uncertainty. First, we find new ways to divide property (e.g., "cake cutting") using wagering mechanisms. Vice versa, we find that property allocation mechanisms are useful (and practical!) for wagering. Second, we show how wagering mechanisms relate to a class of phantom median functions and can enable participatory budgeting. Third, we adapt wagering mechanisms to choose the best forecaster in a winner-takes-all contest.
David Pennock is a Principal Researcher at Microsoft Research New York City. His largest contributions are novel prediction markets and wagering mechanisms: financial markets harnessed to elicit probabilistic information from a crowd. He has over 70 publications cited 14,000 times and an h-index of 50. He has over twenty patent applications, over twenty press mentions, and has given more than fifty talks including seminars at many of the top CS departments. His Ph.D. is in artificial intelligence and he has been an intellectual and organizational leader in the economics and computation subfield of AI for two decades. He co-founded two research areas, three workshops, and an ACM journal, and was a founding member of three corporate basic-research labs. He served as Assistant Managing Director of MSR NYC for six years. He was Chair of ACM SIGecom, Program co-Chair of ACM EC, and is co-Editor-in-Chief of ACM TEAC. In addition to his primary research area, he has published work in machine learning (including NeurIPS and ICML), theory (including STOC), information retrieval (including a Test of Time Award honorable mention in SIGIR), web science, sponsored search, Bayesian networks, constraint satisfaction, and recommender systems. He led the development of several popular online market games and blogged for Yahoo News. In 2005, he was named to MIT Technology Review’s list of 35 "top technology innovators under age 35" having the potential to profoundly impact the world.