# On Incentivized Exploration beyond Bayesianism and Full-Information

arXiv: https://arxiv.org/abs/2607.18300 · HTML: https://mlsanity.com/paper/2607.18300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Bayesian Exploration: Incentivizing Exploration in Bayesian Games](https://mlsanity.com/paper/1602.07570/) (2016)
- [The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity](https://mlsanity.com/paper/2002.00558/) (2020)
- [Incentivizing Exploration with Selective Data Disclosure](https://mlsanity.com/paper/1811.06026/) (2018)
- [Algorithmic Bayesian Persuasion](https://mlsanity.com/paper/1503.05988/) (2015)
- [Bayesian Exploration with Heterogeneous Agents](https://mlsanity.com/paper/1902.07119/) (2019)
- [Learning to Persuade on the Fly: Robustness Against Ignorance](https://mlsanity.com/paper/2102.10156/) (2021)
- [Exploration and Incentives in Reinforcement Learning](https://mlsanity.com/paper/2103.00360/) (2021)
- [Algorithmic Persuasion with No Externalities](https://mlsanity.com/paper/1609.06825/) (2016)
- [Competing Bandits: Learning under Competition](https://mlsanity.com/paper/1702.08533/) (2017)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [Fiduciary Bandits](https://mlsanity.com/paper/1905.07043/) (2019)
- [Optimal Algorithm for Bayesian Incentive-Compatible Exploration](https://mlsanity.com/paper/1810.10304/) (2018)
- [Efficient Algorithms for Planning with Participation Constraints](https://mlsanity.com/paper/2205.07767/) (2022)
- [Persuasion with Limited Communication](https://mlsanity.com/paper/1606.07843/) (2016)

## Abstract (arXiv)

We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al. [2014]. We consider agents that may possess external information unknown to the principal. We show such settings require new notions of incentivized exploration, as well as going beyond a Bayesian perspective, and we introduce a definition where agents choose any reasonable (undominated) action. Furthermore, our framework provides for a more robust treatment of ties, and extends to settings where agents lack a single common prior and instead only know that reward distributions belong to a collection of potential priors.