Information Design with Unknown Prior
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 7 Oct 2024.
Builds on
8 of 25 references.
References · 8
- 2015Algorithmic Bayesian Persuasioncited by 21
- 2020Mechanisms for a No-Regret Agent: Beyond the Common Priorcited by 18
- 2022Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learningcited by 18
- 2021Multi-Receiver Online Bayesian Persuasioncited by 12
- 2024Multi-Sender Persuasion: A Computational Perspectivecited by 9
- 2024Markov Persuasion Processes: Learning to Persuade from Scratchcited by 8
- 2022No-Regret Bayesian Recommendation to Homogeneous Userscited by 3
- 2023Monitoring with Rich Datacited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.