战略排名
Strategic Ranking
摘要：
策略分类研究的是如何设计一个对策略个体的输入操作具有鲁棒性的分类器。然而，现有的文献并没有考虑个体之间的竞争的影响，诱导的算法设计。受限制的分配设置，如大学录取的动机，我们引入战略排名，其中（设计）个人奖励取决于申请人的努力后排名的测量兴趣。我们的研究结果说明了申请人之间的竞争如何影响所产生的均衡和模型的见解。我们分析了如何各种排名奖励设计，属于一个家庭的步骤功能，权衡申请人，学校和社会效用，以及如何排名设计计数器所产生的不公平的不同访问资源。特别是，我们发现，随机化的奖励设计可以减轻两个措施的不同影响，福利差距和访问。
英文摘要：
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the effect of competition among individuals as induced by the algorithm design. Motivated by constrained allocation settings such as college admissions, we introduce strategic ranking, in which the (designed) individual reward depends on an applicant's post-effort rank in a measurement of interest. Our results illustrate how competition among applicants affects the resulting equilibria and model insights. We analyze how various ranking reward designs, belonging to a family of step functions, trade off applicant, school, and societal utility, as well as how ranking design counters inequities arising from disparate access to resources. In particular, we find that randomization in the reward design can mitigate two measures of disparate impact, welfare gap and access.
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。
