Classification in Equilibrium: Structure of Optimal Decision Rules
Elizabeth Maggie Penn and John W. Patty
本文描述了当个人根据分类规则调整行为时的最佳分类。 我们将设计师与人群之间的互动建模为Stackelberg游戏:设计师选择分类规则,预测个人将如何遵守,欺骗或弃权,以获得有利的分类。 在标准的单调可能性比假设下,最优规则属于一个小型且可解释的家庭(单阈值和双切规则),包括常规和反直觉设计。 我们的结果与先前的研究结果截然不同,即最优分类器奖励更高的信号:在平衡中,设计师可能会故意奖励那些可能性比较低的人,或者将奖励/惩罚集中在中间带中,以提高信息质量。
This paper characterizes optimal classification when individuals adjust their behavior in response to the classification rule. We model the interaction between a designer and a population as a Stackelberg game: the designer selects a classification rule anticipating how individuals will comply, cheat, or abstain in order to obtain a favorable classification. Under standard monotone likelihood ratio assumptions, optimal rules belong to a small and interpretable family (single-threshold and two-cu...