Algorithms for Affirmative Action
Year: 2025
Type: article
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. Supplemental Material: The online data files are available at https://doi.org/10.1287/ited.2023.0039 .
Author: Nick Arnosti
Institution: University of Minnesota
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4409283566