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：本文提出了一种两阶段贝叶斯说服模型，其中第三方平台控制发送者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——该策略在假设发送者遵循自身最优策略的前提下，最大化用户平均效用。研究表明该问题可简化为市场细分模型，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，短视用户按序到达。在此设定下，平台控制发送者关于用户的信息，并为发送者维护声誉，若其未能在特定信号子集上如实行动，平台便予以惩罚。我们给出了基于声誉设定下平台最优策略的刻画，该刻画可简化平台的优化问题。