Markovian persuasion
将经典贝叶斯说服模型扩展到动态环境，其中自然状态按马尔可夫过程演化，研究发送者在重复互动中如何平衡当前收益与未来信息泄露的代价，并分析不同贴现因子下的最优策略。
In the classical Bayesian persuasion model, an informed player and an uninformed one engage in a static interaction. This work extends this classical model to a dynamic setting where the state of nature evolves according to a Markovian law, allowing for a more realistic representation of real‐world situations where the state of nature evolves over time. In this repeated persuasion model, an optimal disclosure strategy of the sender must balance between obtaining a high‐stage payoff and disclosing information that may have negative implications on future payoffs. We discuss optimal strategies under different discount factors and characterize when the asymptotic value achieves the maximal possible value.