In this paper, we introduce a two-stage Bayesian persuasion model in which a third-party platform controls the information available to the sender about users' preferences. We aim to characterize the optimal information disclosure policy of the platform, which maximizes average user utility, under the assumption that the sender also follows its own optimal policy. We show that this problem can be reduced to a model of market segmentation, in which probabilities are mapped into valuations. We then introduce a repeated variation of the persuasion platform problem in which myopic users arrive sequentially. In this setting, the platform controls the sender's information about users and maintains a reputation for the sender, punishing it if it fails to act truthfully on a certain subset of signals. We provide a characterization of the optimal platform policy in the reputation-based setting, which is then used to simplify the optimization problem of the platform.

翻译：本文介绍了一个两阶段贝叶斯说服模型，其中第三方平台控制着发送方可获取的关于用户偏好的信息。我们旨在刻画平台的最优信息披露策略，该策略在假设发送方同样遵循其自身最优策略的前提下，最大化用户的平均效用。我们证明该问题可简化为一个市场细分模型，其中概率被映射为估值。随后，我们引入该说服平台问题的重复变体，在该变体中短视用户依次到达。在此设定下，平台控制发送方关于用户的信息，并维护发送方的声誉，若发送方未能诚实地对某一特定信号子集采取行动，则对其进行惩罚。我们给出了基于声誉设定下最优平台策略的刻画，并据此简化了平台的优化问题。