# Bayesian Persuasion in Sequential Decision-Making

arXiv: https://arxiv.org/abs/2106.05137 · HTML: https://mlsanity.com/paper/2106.05137/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2021.

## Built on by (most cited first, 12 of 12)

- [Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning](https://mlsanity.com/paper/2202.10678/) (2022)
- [Sequential Information Design: Learning to Persuade in the Dark](https://mlsanity.com/paper/2209.03927/) (2022)
- [Information Design in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.06807/) (2023)
- [Markov Persuasion Processes: Learning to Persuade from Scratch](https://mlsanity.com/paper/2402.03077/) (2024)
- [Efficient Model-agnostic Alignment via Bayesian Persuasion](https://mlsanity.com/paper/2405.18718/) (2024)
- [Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems](https://mlsanity.com/paper/2402.12907/) (2024)
- [Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding](https://mlsanity.com/paper/2504.01211/) (2025)
- [Stochastic Principal-Agent Problems: Efficient Computation and Learning](https://mlsanity.com/paper/2306.03832/) (2023)
- [Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition](https://mlsanity.com/paper/2505.17379/) (2025)
- [Information Bargaining: Bilateral Commitment in Bayesian Persuasion](https://mlsanity.com/paper/2506.05876/) (2025)
- [The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability](https://mlsanity.com/paper/2506.09940/) (2025)
- [Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving](https://mlsanity.com/paper/2607.13576/) (2026)

## Abstract (arXiv)

We study a dynamic model of Bayesian persuasion in sequential decision-making settings. An informed principal observes an external parameter of the world and advises an uninformed agent about actions to take over time. The agent takes actions in each time step based on the current state, the principal's advice/signal, and beliefs about the external parameter. The action of the agent updates the state according to a stochastic process. The model arises naturally in many applications, e.g., an app (the principal) can advice the user (the agent) on possible choices between actions based on additional real-time information the app has. We study the problem of designing a signaling strategy from the principal's point of view. We show that the principal has an optimal strategy against a myopic agent, who only optimizes their rewards locally, and the optimal strategy can be computed in polynomial time. In contrast, it is NP-hard to approximate an optimal policy against a far-sighted agent. Further, if the principal has the power to threaten the agent by not providing future signals, then we can efficiently compute a threat-based strategy. This strategy guarantees the principal's payoff as if playing against an agent who is far-sighted but myopic to future signals.