论文标题
从选择性披露的数据中推断
Inference from Selectively Disclosed Data
论文作者
论文摘要
我们考虑发送者的披露问题，其中有大量的数据集，他们想说服接收者采取更高的行动。因为接收器将根据所看到的数据的分布进行推断，所以发件人有动力下降观察以模仿在更好的状态下观察到的分布。我们预测，哪些观察结果使用了一个模型，该模型近似于具有连续数据的大型数据集。对于发件人而言，发挥模仿策略是最佳的，在该策略下，他们提出的证据模仿了一些更理想的目标状态下的自然分布。我们表征了这些模仿策略下的部分造成结果，并表明它们得到了有关最大程度区分较高状态的结果的数据。相对于完整信息，具有自愿披露的平衡减少了发件人的福利，几乎没有数据或有利的状态，他们充分披露了他们的数据，但会遭受接收者的怀疑，并使对大型数据集的访问者受益，他们可以在低州下盈利地观察。
We consider the disclosure problem of a sender with a large data set of hard evidence who wants to persuade a receiver to take higher actions. Because the receiver will make inferences based on the distribution of the data they see, the sender has an incentive to drop observations to mimic the distributions that would be observed under better states. We predict which observations the sender discloses using a model that approximates large datasets with a continuum of data. It is optimal for the sender to play an imitation strategy, under which they submit evidence that imitates the natural distribution under some more desirable target state. We characterize the partial-pooling outcomes under these imitation strategies, and show that they are supported by data on the outcomes that maximally distinguish higher states. Relative to full information, the equilibrium with voluntary disclosure reduces the welfare of senders with little data or a favorable state, who fully disclose their data but suffer the receiver's skepticism, and benefits senders with access to large datasets, who can profitably drop observations under low states.
