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.

中文翻译：

---

战略预报员的高效竞赛和在线学习

预测和机器学习中的优胜者竞赛受到扭曲的激励。Witkowskiet等。确定了这个问题，并提出了ELF，这是一种选择获胜者的真实机制。我们显示，从一个$ n $个预测器池中，ELF需要$ \ Theta（n \ log n）$个事件或测试数据点来选择一个具有较高概率的接近最优的预测器。然后，我们展示了标准的在线学习算法仅通过$ O（\ log（n）/ \ epsilon ^ 2）$事件选择了一个\ epsilon $最优预测器，这是通过强逼真性保证实现的。即使在非战略环境中，此界限也要尽可能匹配。然后，我们应用这些机制为非近视战略专家获得第一个无悔保证。