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arXiv 2109.11536econ.TH
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接收者偏好模糊下的说服

Persuasion with Ambiguous Receiver Preferences

Eitan Sapiro-Gheiler

AI总结:

本文研究接收者偏好模糊下的贝叶斯说服问题,将其建模为零和博弈,形式化其与政治支出等博弈的联系,并得出二元与连续状态下发送者的最优后验均值分布形式。

摘要:

本文描述了一个贝叶斯说服问题:接收者拥有私人类型,该类型代表其选择发送者偏好行动的阈值;发送者对所有均值已知、有上下界的接收者类型分布具有最大最小偏好。该问题可表示为一个零和博弈:发送者选择后验均值信念的分布,该分布是状态先验分布的均值保持收缩;敌对的自然选择具有已知均值的接收者类型分布;从各自选择的分布中得到更高实现值的参与者获胜。本文形式化了最大最小说服与用于建模政治支出、全支付拍卖和竞争性说服的类似博弈之间的联系。在标准的二元状态设定和新的连续状态设定中,发送者最优地将状态先验分布线性化,以构造后验均值分布——该分布在已知区间上均匀分布,且在其支撑集的下界处存在一个原子。

英文摘要:

I describe a Bayesian persuasion problem where Receiver has a private type representing a cutoff for choosing Sender's preferred action, and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a distribution of posterior mean beliefs that is a mean-preserving contraction of the prior over states, and an adversarial Nature chooses a Receiver type distribution with the known mean; the player with the higher realization from their chosen distribution wins. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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