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Algorithms for Affirmative Action

Nick Arnosti

INFORMS Transactions on Education2025https://doi.org/10.1287/ited.2023.0039article
AJG 2ABDC C
Weight
0.50

What the paper says

This paper illustrates how fundamental concepts from optimization—such as greedy algorithms, matroids, maximum weight matching, and NP-completeness—arise in domains where policymakers wish to select a set of applicants while ensuring representation for specific groups. Examples of such settings include visa lotteries in the United States, the election for Chile’s constitutional assembly, affordable housing lotteries in New York City, selection for Indian civil service positions, and admission to Indian and Brazilian universities. By providing these examples alongside sample exercises, I aim to offer educators tools to make optimization theory accessible to students at all levels, while highlighting its policy relevance. Supplemental Material: The online data files are available at https://doi.org/10.1287/ited.2023.0039 .

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https://doi.org/https://doi.org/10.1287/ited.2023.0039

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@article{nick2025,
  title        = {{Algorithms for Affirmative Action}},
  author       = {Nick Arnosti},
  journal      = {INFORMS Transactions on Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1287/ited.2023.0039},
}

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Algorithms for Affirmative Action

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.