Efficient Incentive-Compatible Forecasting Competitions
Speaker: YJ Choe
Abstract: In this talk, I will review a selection of results from two recent papers on designing incentive-compatible forecasting competitions. I will first follow Witkowski et al. (2022)'s discussion on how deterministic (e.g., winner-take-all) forecasting competitions do not incentivize forecasters to report their predictions truthfully. I will then introduce their proposed stochastic alternative, called the Event Lotteries Forecasting Competition Mechanism (ELF), that is incentive-compatible. After that, I will go over Frongillo et al. (2021)'s proof that shows how ELF requires Θ(n log n) test events to select a near-optimal forecaster, where n is the number of forecasters in the competition. Time permitting, I will also briefly describe some of their other results, including an approximately incentive-compatible mechanism that can select an ε-optimal forecaster with just O(log n/ε^2) events and how it leads to the first no-regret algorithm for online prediction with non-myopic strategic experts.