论文标题
随机性和噪声在战略分类中的作用
The Role of Randomness and Noise in Strategic Classification
论文作者
论文摘要
我们研究了在战略分类环境中设计最佳分类器的问题，在战略分类环境中，分类是游戏的一部分，玩家可以修改其功能以获得有利的分类结果（同时产生了一些成本）。以前，从学习理论的角度和算法公平的角度考虑了问题。我们的主要贡献包括1。表明，如果目标是最大化分类过程的效率（定义为结果的准确性减去合格玩家的沉没成本来操纵其功能以获得更好的结果（即使用随机分类器）（即使用给定功能向量的可能性，则必须在分类器中接受分类器之间的可能性是必不可少的。 2。表明，在许多自然情况下，强加的最佳解决方案（就效率而言）具有从未改变其特征向量的结构（随机分类器的结构是以某种方式结构的，因此被分类为1的可能性并不能证明更改特征的费用是合理的）。 3.观察到随机分类不是分类器的观点的稳定最佳响应，并且分类器不会从不在系统中创建不稳定的情况下从随机分类器中受益。 4。表明在某些情况下，嘈杂的信号会导致更好的平衡结果 - 当涉及多个特征调整成本的一个以上亚群时，提高了准确性和公平性。从政策的角度来看，这很有趣，因为很难强迫机构坚持特定的随机分类策略（尤其是在具有多个分类器的市场的背景下），但是可以更改信息环境以使功能信号固有地吵。
We investigate the problem of designing optimal classifiers in the strategic classification setting, where the classification is part of a game in which players can modify their features to attain a favorable classification outcome (while incurring some cost). Previously, the problem has been considered from a learning-theoretic perspective and from the algorithmic fairness perspective. Our main contributions include 1. Showing that if the objective is to maximize the efficiency of the classification process (defined as the accuracy of the outcome minus the sunk cost of the qualified players manipulating their features to gain a better outcome), then using randomized classifiers (that is, ones where the probability of a given feature vector to be accepted by the classifier is strictly between 0 and 1) is necessary. 2. Showing that in many natural cases, the imposed optimal solution (in terms of efficiency) has the structure where players never change their feature vectors (the randomized classifier is structured in a way, such that the gain in the probability of being classified as a 1 does not justify the expense of changing one's features). 3. Observing that the randomized classification is not a stable best-response from the classifier's viewpoint, and that the classifier doesn't benefit from randomized classifiers without creating instability in the system. 4. Showing that in some cases, a noisier signal leads to better equilibria outcomes -- improving both accuracy and fairness when more than one subpopulation with different feature adjustment costs are involved. This is interesting from a policy perspective, since it is hard to force institutions to stick to a particular randomized classification strategy (especially in a context of a market with multiple classifiers), but it is possible to alter the information environment to make the feature signals inherently noisier.
