英文标题：
《Combining Outcome-Based and Preference-Based Matching: A Constrained
  Priority Mechanism》
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作者：
Avidit Acharya, Kirk Bansak, Jens Hainmueller
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最新提交年份：
2020
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英文摘要：
  We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the predicted probability of employment, while in the student assignment context it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families, students) based on their preferences, but subject to meeting the planner\'s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner\'s threshold.
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中文摘要：
我们引入了一种约束优先机制，该机制将机器学习中基于结果的匹配与市场设计中常见的基于偏好的分配方案相结合。利用真实世界的数据，我们说明了如何将我们的机制应用于将难民家庭分配到东道国，将幼儿园教师分配到学校。我们的机制允许规划师首先为分配应达到的最低可接受平均结果分数指定一个阈值$\\ bar g$。在难民匹配情况下，该分数对应于预测的就业概率，而在学生作业情况下，该分数对应于标准化考试分数。该机制是一种优先机制，通过根据代理人（难民家庭、学生）的偏好分配代理人（难民家庭、学生），同时考虑结果和偏好，但必须满足规划者规定的阈值。该机制具有策略证明和约束有效性，因为它总是生成一个匹配，而不是由任何其他符合规划者阈值的匹配所支配。
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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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：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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