论文标题
几乎是基斯二次说服（扩展版）
Almost-Bayesian Quadratic Persuasion (Extended Version)
论文作者
论文摘要
在本文中，我们在Kamenica＆Gentzkow引入的贝叶斯说服力的现在传统模型中放松了贝叶斯性假设。与先前存在的方法不同 - 考虑到接收者（BOB）是非拜拜西亚人的可能性，因为考虑到他的思维过程尚不是贝叶斯人，但发件人（Alice）可能会符合参数的贝叶斯人 - 我们让爱丽丝（Alice）只是假设鲍勃（Alice）只是假设鲍勃（Bob）在某种意义上不采取某种意义，而没有任何特定的模型。 在这个假设下，当两个公用事业都是二次且先验是各向同性的时，我们研究了爱丽丝的策略。我们表明，与贝叶斯案相反，爱丽丝的最佳响应可能不再是线性的。这一事实是不幸的，因为线性策略仍然是唯一已知的信念分布的策略。此外，评估线性策略在特定情况下，更不用说找到最佳的策略了。尽管如此，我们得出了证明线性策略几乎最佳的边界，并允许爱丽丝以数值来计算近乎最佳的线性策略。借助此解决方案，我们表明，爱丽丝与鲍勃（Bob）分享了更少的信息，因为他与贝叶斯（Bayesianity）相去甚远，这造成了损害。
In this article, we relax the Bayesianity assumption in the now-traditional model of Bayesian Persuasion introduced by Kamenica & Gentzkow. Unlike preexisting approaches -- which have tackled the possibility of the receiver (Bob) being non-Bayesian by considering that his thought process is not Bayesian yet known to the sender (Alice), possibly up to a parameter -- we let Alice merely assume that Bob behaves 'almost like' a Bayesian agent, in some sense, without resorting to any specific model. Under this assumption, we study Alice's strategy when both utilities are quadratic and the prior is isotropic. We show that, contrary to the Bayesian case, Alice's optimal response may not be linear anymore. This fact is unfortunate as linear policies remain the only ones for which the induced belief distribution is known. What is more, evaluating linear policies proves difficult except in particular cases, let alone finding an optimal one. Nonetheless, we derive bounds that prove linear policies are near-optimal and allow Alice to compute a near-optimal linear policy numerically. With this solution in hand, we show that Alice shares less information with Bob as he departs more from Bayesianity, much to his detriment.
