# Information Design with Unknown Prior

arXiv: https://arxiv.org/abs/2410.05533 · HTML: https://mlsanity.com/paper/2410.05533/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Algorithmic Bayesian Persuasion](https://mlsanity.com/paper/1503.05988/) (2015)
- [Mechanisms for a No-Regret Agent: Beyond the Common Prior](https://mlsanity.com/paper/2009.05518/) (2020)
- [Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning](https://mlsanity.com/paper/2202.10678/) (2022)
- [Multi-Receiver Online Bayesian Persuasion](https://mlsanity.com/paper/2106.06480/) (2021)
- [Multi-Sender Persuasion: A Computational Perspective](https://mlsanity.com/paper/2402.04971/) (2024)
- [Markov Persuasion Processes: Learning to Persuade from Scratch](https://mlsanity.com/paper/2402.03077/) (2024)
- [No-Regret Bayesian Recommendation to Homogeneous Users](https://mlsanity.com/paper/2202.06135/) (2022)
- [Monitoring with Rich Data](https://mlsanity.com/paper/2312.16789/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning in Bayesian Stackelberg Games With Unknown Follower's Types](https://mlsanity.com/paper/2602.00771/) (2026)
- [A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2504.04976/) (2025)

## Abstract (arXiv)

Information designers, such as online platforms, often do not know the beliefs of their receivers. We design learning algorithms so that the information designer can learn the receivers' prior belief from their actions through repeated interactions. Our learning algorithms achieve no regret relative to the optimality for the known prior at a fast speed, achieving a tight regret bound Theta(log T) in general and a tight regret bound Theta(log log T) in the important special case of binary actions.