Bayesian Persuasion in Sequential Trials
Bayesian Persuasion in Sequential Trials
复制标题
DOI：
10.1007/978-3-030-94676-0_2
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Shih-Tang Su;V. Subramanian;G. Schoenebeck
中科院分区：
文献类型：
--
作者：
Shih-Tang Su;V. Subramanian;G. Schoenebeck
我们考虑一个贝叶斯说服问题，发送者试图说服接收者采取特定的行动，通过一系列的信号。我们通过考虑基于先前实验结果进行不同实验的多阶段试验来建模。与大多数文献相比，我们考虑的问题与信号施加在发送者的约束。我们通过以一种外生的方式固定某些实验来实现这一点;这些实验被称为确定实验。这种建模有助于我们理解发生这种情况的真实情况：例如，多阶段药物试验，其中FDA确定一些实验，大公司的启动收购，后期评估由潜在收购方确定，多轮工作面试，候选人最初通过展示他们的资格来发出信号，但其余的筛选程序由面试官决定。多阶段试验中的非确定性实验（信号）由发送方选择，以便最好地说服接收方。随着世界的二进制状态，我们开始推导出最佳的信号策略，在唯一的非平凡配置的两阶段试验与二进制结果的实验。然后，我们推广到多阶段试验与二进制结果的实验，其中确定的实验可以放置在任意节点的试验树。在这里，我们提出了一个动态规划算法来获得最佳的信令策略，使用两阶段试验解决方案的结构性见解。我们还对比了最优的信号策略结构与经典的贝叶斯说服策略，突出的信号约束对发送者的影响。
We consider a Bayesian persuasion problem where the sender tries to persuade the receiver to take a particular action via a sequence of signals. This we model by considering multi-phase trials with different experiments conducted based on the outcomes of prior experiments. In contrast to most of the literature, we consider the problem with constraints on signals imposed on the sender. This we achieve by fixing some of the experiments in an exogenous manner; these are called determined experiments. This modeling helps us understand real-world situations where this occurs: e.g., multi-phase drug trials where the FDA determines some of the experiments, start-up acquisition by big firms where late-stage assessments are determined by the potential acquirer, multi-round job interviews where the candidates signal initially by presenting their qualifications but the rest of the screening procedures are determined by the interviewer. The non-determined experiments (signals) in the multi-phase trial are to be chosen by the sender in order to persuade the receiver best. With a binary state of the world, we start by deriving the optimal signaling policy in the only non-trivial configuration of a two-phase trial with binary-outcome experiments. We then generalize to multi-phase trials with binary-outcome experiments where the determined experiments can be placed at arbitrary nodes in the trial tree. Here we present a dynamic programming algorithm to derive the optimal signaling policy that uses the two-phase trial solution’s structural insights. We also contrast the optimal signaling policy structure with classical Bayesian persuasion strategies to highlight the impact of the signaling constraints on the sender.