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arXiv · 2102.08358

Efficient Competitions and Online Learning with Strategic Forecasters

Rafael Frongillo·Robert Gomez·Anish Thilagar·Bo Waggoner
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Abstract

Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. Witkowski et al. 2018 identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of $n$ forecasters, ELF requires $\Theta(n\log n)$ events or test data points to select a near-optimal forecaster with high probability. We then show that standard online learning algorithms select an $\epsilon$-optimal forecaster using only $O(\log(n) / \epsilon^2)$ 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.

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Rafael Frongillo, Robert Gomez, Anish Thilagar, Bo Waggoner. 2021-02-16. Efficient Competitions and Online Learning with Strategic Forecasters. https://arxiv.org/abs/2102.08358

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