摘要翻译：
我们研究了一个两阶段模型，在这个模型中，学生1）通过入学考试被大学录取，这是一个关于他们资格（类型）的噪声信号；然后2）那些被大学录取的学生可以被雇主雇佣，这是他们大学成绩的函数，这是一个独立绘制的关于他们类型的噪声信号。学生来自两个群体中的一个，这两个群体可能有不同的类型分布。我们假设处于管道末端的雇主是理性的，因为它根据所有可用的信息（大学录取、成绩和小组成员资格）计算学生类型的后验分布，并根据后验期望做出决定。然后，我们研究了大学通过设置招生规则和评分政策可以实现什么样的公平目标。例如，大学的目标可能是保证不同人口的机会平等：通过管道和被雇主雇用的可能性应该独立于群体成员，以类型为条件。或者，学院的目标可能是激励雇主制定集体盲目招聘规则。我们表明，当学院不报告成绩时，这两个目标都可以实现。另一方面，我们表明，在合理的条件下，当学院使用（甚至是最低限度的）信息评分政策时，即使孤立地实现这些目标也是不可能的。
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英文标题：
《Downstream Effects of Affirmative Action》
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作者：
Sampath Kannan and Aaron Roth and Juba Ziani
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  We study a two-stage model, in which students are 1) admitted to college on the basis of an entrance exam which is a noisy signal about their qualifications (type), and then 2) those students who were admitted to college can be hired by an employer as a function of their college grades, which are an independently drawn noisy signal of their type. Students are drawn from one of two populations, which might have different type distributions. We assume that the employer at the end of the pipeline is rational, in the sense that it computes a posterior distribution on student type conditional on all information that it has available (college admissions, grades, and group membership), and makes a decision based on posterior expectation. We then study what kinds of fairness goals can be achieved by the college by setting its admissions rule and grading policy. For example, the college might have the goal of guaranteeing equal opportunity across populations: that the probability of passing through the pipeline and being hired by the employer should be independent of group membership, conditioned on type. Alternately, the college might have the goal of incentivizing the employer to have a group blind hiring rule. We show that both goals can be achieved when the college does not report grades. On the other hand, we show that under reasonable conditions, these goals are impossible to achieve even in isolation when the college uses an (even minimally) informative grading policy.
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PDF链接：
https://arxiv.org/pdf/1808.09004