Efficient Competitions and Online Learning with Strategic Forecasters

Abstract

Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. Witkowski et al. [23] identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of forecasters, ELF requires T( log) events or test data points to select a near-optimal forecaster with high probability. We then show that standard online learning algorithms select an -optimal forecaster using only (log()/2) events, by way of a strong approximate-truthfulness guarantee. This bound matches the best possible even in the nonstrategic setting. We then apply these mechanisms to obtain the first no-regret guarantee for non-myopic strategic experts.