有限通信条件下的说服算法
Algorithms for Persuasion with Limited Communication
摘要：
战略沟通的贝叶斯说服范式模型之间的相互作用，一个私人知情的代理人，称为发送者，和一个无知的，但理性的代理人，称为接收者。目标通常是为发送方设计（接近）最佳的通信（或信令）方案。它使发送者能够向接收者披露信息，以激励她采取发送者更喜欢的行动。已知找到最佳信令方案通常在计算上是困难的。当对消息空间的大小也有约束时，这种困难进一步加剧，导致在任何常数因子内近似最优发送者效用的NP-困难。在本文中，我们表明，在几个自然和突出的情况下，即使消息空间是有限的优化问题是易于处理的。特别是，我们研究信号下的对称性或独立性的假设分布的效用值的行动。对于对称分布，我们提供了一个新的表征的最佳信令方案。它的结果在多项式时间算法来计算一个最佳的计划，许多compensated对称分布。在独立情况下，我们设计了一个常数因子的近似算法，这与一般情况下的近似困难形成了鲜明的对比。
英文摘要：
The Bayesian persuasion paradigm of strategic communication models interaction between a privately-informed agent, called the sender, and an ignorant but rational agent, called the receiver. The goal is typically to design a (near-)optimal communication (or signaling) scheme for the sender. It enables the sender to disclose information to the receiver in a way as to incentivize her to take an action that is preferred by the sender. Finding the optimal signaling scheme is known to be computationally difficult in general. This hardness is further exacerbated when there is also a constraint on the size of the message space, leading to NP-hardness of approximating the optimal sender utility within any constant factor. In this paper, we show that in several natural and prominent cases the optimization problem is tractable even when the message space is limited. In particular, we study signaling under a symmetry or an independence assumption on the distribution of utility values for the actions. For symmetric distributions, we provide a novel characterization of the optimal signaling scheme. It results in a polynomial-time algorithm to compute an optimal scheme for many compactly represented symmetric distributions. In the independent case, we design a constant-factor approximation algorithm, which stands in marked contrast to the hardness of approximation in the general case.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
