Bayesian persuasion is a model for understanding strategic information revelation: an agent with an informational advantage, called a sender, strategically discloses information by sending signals to another agent, called a receiver. In algorithmic Bayesian persuasion, we are interested in efficiently designing the sender's signaling schemes that lead the receiver to take action in favor of the sender. This paper studies algorithmic Bayesian-persuasion settings where the receiver's feasible actions are specified by combinatorial constraints, e.g., matroids or paths in graphs. We first show that constant-factor approximation is NP-hard even in some special cases of matroids or paths. We then propose a polynomial-time algorithm for general matroids by assuming the number of states of nature to be a constant. We finally consider a relaxed notion of persuasiveness, called CCE-persuasiveness, and present a sufficient condition for polynomial-time approximability.
翻译:贝叶斯说服是理解战略信息披露的模型:一个信息优势的代理人,称为发件人,通过向另一个代理人发送信号,战略上披露信息,称为接收人。在巴伊西亚算法中,我们有兴趣有效地设计发件人的信号计划,引导接收人采取行动支持发件人。本文研究巴伊西亚预测设置的算法,接收人可行的行动是通过组合约束(例如,类固醇或图中路径)指定的。我们首先显示,即使在一些特殊情况下,即使是在类固醇或路径,常态接近也是硬的。然后我们假设自然状态的数量不变,为一般类固醇提议一个多时算法。我们最终认为,一种较宽松的说服性概念,称为CE-说服力,并提出了多营养-时间相适应性的充分条件。