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方获取用户偏好的信息。我们的目标是在假设发送方同样遵循其自身最优策略的前提下，刻画平台的最优信息披露策略，该策略旨在最大化用户平均效用。我们证明该问题可简化为一种市场细分模型，其中概率被映射为估值。随后，我们引入了说服平台问题的重复博弈变体，其中短视用户顺序到达。在此设定下，平台控制发送方获取用户信息的权限，并为发送方维护声誉——若发送方未能在特定信号子集上如实行动，平台将对其进行惩罚。我们给出了基于声誉设定下平台最优策略的刻画，并利用该结果简化了平台的优化问题。