Efficient Competitions and Online Learning with Strategic Forecasters

02/16/2021

∙

by   Rafael Frongillo, et al.

∙

0

∙

share

Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. Witkowskiet al. identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of n forecasters, ELF requires Θ(nlog n) events or test data points to select a near-optimal forecaster with high probability. We then show that standard online learning algorithms select an ϵ-optimal forecaster using only O(log(n) / ϵ^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.