Algorithms for Affirmative Action
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| Title: | Algorithms for Affirmative Action |
|---|---|
| Language: | English |
| Authors: | Nick Arnosti (ORCID |
| Source: | INFORMS Transactions on Education. 2026 26(2):119-133. |
| Availability: | Institute for Operations Research and the Management Sciences (INFORMS). 5521 Research Park Drive Suite 200, Catonsville, Maryland 21228. Tel: 800-446-3676; Tel: 443-757-3500; Fax: 443-757-3515; e-mail: informs@informs.org; Web site: https://pubsonline.informs.org/journal/ited |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Descriptive |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Algorithms, Affirmative Action, Diversity, Instructional Materials, Foreign Countries, College Students, Instructional Effectiveness, College Admission, Competitive Selection, College Applicants, Foreign Students, Immigrants, Course Objectives, Housing |
| Geographic Terms: | United States, Chile, India, Brazil, Israel |
| DOI: | 10.1287/ited.2023.0039 |
| ISSN: | 1532-0545 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1495240 |
| Database: | ERIC |
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| 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. |
|---|---|
| ISSN: | 1532-0545 |
| DOI: | 10.1287/ited.2023.0039 |
