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Algorithms for Affirmative Action

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  • Nick Arnosti

    (College of Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

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.

Suggested Citation

  • Nick Arnosti, 2026. "Algorithms for Affirmative Action," INFORMS Transactions on Education, INFORMS, vol. 26(2), pages 119-133, January.
    • Handle: RePEc:inm:orited:v:26:y:2026:i:2:p:119-133
    DOI: 10.1287/ited.2023.0039
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    References listed on IDEAS

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