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 $\Theta(n\log n)$ 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.

翻译：预测和机器学习方面的赢家-赢家-赢家-赢家-赢家-赢家-赢家-赢家-赢家-赢家-赢家-学习竞赛受到扭曲的激励。 Witkowskiet al. 发现了这个问题,并提出了ELF, 这是一种选择赢家的诚实机制。 我们从一个美元预测器中显示, ELF需要$\ Theta( n\log n) 的事件或测试数据点来选择一个概率高的近乎最佳的预测器。 然后我们显示, 标准在线学习算法只选择一个美元/ 百分数- 最佳的预测器, 仅使用 $O( log(n) /\ epsilon=2) 的事件, 以强烈的近似真实性保证方式选择 。 这一约束与即使在非战略环境下也尽可能匹配。 然后我们运用这些机制来为非战略专家获得第一个无风险保证。