发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对接收者拥有发送者未知私人信号方案的在线贝叶斯说服问题，设计了遗憾值为$\tilde{O}(T^{3/4})$的学习算法，将信念空间划分学习简化为变点检测问题。
AI 中文摘要
贝叶斯说服研究知情发送者如何通过策略性信息披露影响接收者的行为。标准模型假设发送者是接收者的唯一信息来源，但在许多应用场景中，接收者还会咨询发送者既无法观察也无法控制的外部信息源。我们研究一类在线贝叶斯说服问题：二元行动接收者拥有一个发送者未知的固定信号方案；在T轮中，发送者承诺一个信号方案并发送信号，接收者将其与自身私人信号结合后采取行动，而发送者仅能观察到行动。我们设计了一种学习算法，相对于知晓接收者私人信号方案的最优方案，其遗憾值达到$\tilde{O}(T^{3/4})$，且与状态空间规模、接收者信号字母表规模呈多项式依赖关系。我们的核心洞见是将学习由私人方案诱导的指数级大信念空间划分的问题，简化为一维变点检测问题。
英文摘要
Bayesian persuasion studies how an informed sender can influence the behavior of a receiver through strategic information disclosure. Standard models assume the sender is the receiver's only source of information, yet in many applications receivers also consult external sources the sender can neither observe nor control. We study an online Bayesian persuasion problem in which a binary-action receiver has access to a fixed signaling scheme that is unknown to the sender. Over $T$ rounds, the sender commits to a signaling scheme and sends a signal; the receiver combines it with its private signal and acts, while the sender observes only the action. We design a learning algorithm that achieves regret $\widetilde{O}(T^{3/4})$ relative to the optimal scheme of a sender who knows the private signaling scheme of the receiver, with polynomial dependence on the sizes of the state space and the receiver's signal alphabet. Our key insight is reducing the problem of learning the exponentially large belief-space partitioning induced by the private scheme to a one-dimensional change-point detection problem.
