# Efficient Competitions and Online Learning with Strategic Forecasters

arXiv: https://arxiv.org/abs/2102.08358 · HTML: https://mlsanity.com/paper/2102.08358/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Fighting Bandits with a New Kind of Smoothness](https://mlsanity.com/paper/1512.04152/) (2015)
- [No-Regret and Incentive-Compatible Online Learning](https://mlsanity.com/paper/2002.08837/) (2020)
- [Online Prediction with Selfish Experts](https://mlsanity.com/paper/1702.03615/) (2017)
- [Online Learning via the Differential Privacy Lens](https://mlsanity.com/paper/1711.10019/) (2017)
- [Incentive-Compatible Forecasting Competitions](https://mlsanity.com/paper/2101.01816/) (2021)
- [Constant Regret, Generalized Mixability, and Mirror Descent](https://mlsanity.com/paper/1802.06965/) (2018)

## Built on by (most cited first, 9 of 9)

- [Online Learning from Strategic Human Feedback in LLM Fine-Tuning](https://mlsanity.com/paper/2412.16834/) (2024)
- [Scoring Rules for Performative Binary Prediction](https://mlsanity.com/paper/2207.02847/) (2022)
- [WOMAC: A Mechanism For Prediction Competitions](https://mlsanity.com/paper/2508.17907/) (2025)
- [Sample Complexity of Forecast Aggregation](https://mlsanity.com/paper/2207.13126/) (2022)
- [Forecasting Competitions with Correlated Events](https://mlsanity.com/paper/2303.13793/) (2023)
- [No-Regret Online Prediction with Strategic Experts](https://mlsanity.com/paper/2305.15331/) (2023)
- [On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX](https://mlsanity.com/paper/2404.05155/) (2024)
- [Hedging and Approximate Truthfulness in Traditional Forecasting Competitions](https://mlsanity.com/paper/2409.19477/) (2024)
- [No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts](https://mlsanity.com/paper/2502.11483/) (2025)

## Abstract (arXiv)

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(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) / ε2) 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.