TL;DR本文从计算的角度出发，重点研究了李嘉图和根岑科夫在 Bayesian Persuasion Model 中提出的两个角色 —— 发送者和接收者，以及发送者如何通过优化任务最大化自己的收益。论文重点分析了三种自然输入模型下发情况的最优化问题，并针对每一种模型讨论了其计算复杂度。
Abstract
persuasion, defined as the act of exploiting an informational advantage in
order to effect the decisions of others, is ubiquitous. Indeed, persuasive
communication has been estimated to account for almost a third
Bayesian persuasion and learning algorithms are used to address Markov persuasion processes where the sender has no prior knowledge, ensuring sublinear growth of regret in the number of episodes and matching the guarantees of the algorithm.