# No-Regret and Incentive-Compatible Online Learning

arXiv: https://arxiv.org/abs/2002.08837 · HTML: https://mlsanity.com/paper/2002.08837/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Information Discrepancy in Strategic Learning](https://mlsanity.com/paper/2103.01028/) (2021)
- [Online Learning from Strategic Human Feedback in LLM Fine-Tuning](https://mlsanity.com/paper/2412.16834/) (2024)
- [Efficient Competitions and Online Learning with Strategic Forecasters](https://mlsanity.com/paper/2102.08358/) (2021)
- [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024)
- [Scoring Rules for Performative Binary Prediction](https://mlsanity.com/paper/2207.02847/) (2022)
- [Who Reviews The Reviewers? A Multi-Level Jury Problem](https://mlsanity.com/paper/2211.08494/) (2022)
- [Online Learning for Equilibrium Pricing in Markets under Incomplete Information](https://mlsanity.com/paper/2303.11522/) (2023)
- [COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents](https://mlsanity.com/paper/2505.23720/) (2025)
- [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)
- [Incentive-compatible Bandits: Importance Weighting No More](https://mlsanity.com/paper/2405.06480/) (2024)
- [No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts](https://mlsanity.com/paper/2502.11483/) (2025)
- [Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing](https://mlsanity.com/paper/2605.24052/) (2026)
- [Decentralized Aggregation of LLM Predictions via Wagering Mechanisms](https://mlsanity.com/paper/2607.04389/) (2026)

## Abstract (arXiv)

We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight. Second, we want incentive compatibility, a guarantee that each expert's best strategy is to report his true beliefs about the realization of each event. To achieve this goal, we build on the literature on wagering mechanisms, a type of multi-agent scoring rule. We provide algorithms that achieve no regret and incentive compatibility for myopic experts for both the full and partial information settings. In experiments on datasets from FiveThirtyEight, our algorithms have regret comparable to classic no-regret algorithms, which are not incentive-compatible. Finally, we identify an incentive-compatible algorithm for forward-looking strategic agents that exhibits diminishing regret in practice.