# Monoculture in Matching Markets

arXiv: https://arxiv.org/abs/2312.09841 · HTML: https://mlsanity.com/paper/2312.09841/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2023.

## Built on by (most cited first, 5 of 5)

- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [When Neutral Summaries are not that Neutral: Quantifying Political Neutrality in LLM-Generated News Summaries](https://mlsanity.com/paper/2410.09978/) (2024)
- [The Subjectivity of Monoculture](https://mlsanity.com/paper/2602.24086/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring](https://mlsanity.com/paper/2609.22169/) (2026)

## Abstract (arXiv)

Algorithmic monoculture arises when many decision-makers rely on the same algorithm to evaluate applicants. An emerging body of work investigates possible harms of this kind of homogeneity, but has been limited by the challenge of incorporating market effects in which the preferences and behavior of many applicants and decision-makers jointly interact to determine outcomes. Addressing this challenge, we introduce a tractable theoretical model of algorithmic monoculture in a two-sided matching market with many participants. We use the model to analyze outcomes under monoculture (when decision-makers all evaluate applicants using a common algorithm) and under polyculture (when decision-makers evaluate applicants independently). All else equal, monoculture (1) selects less-preferred applicants when noise is well-behaved, (2) matches more applicants to their top choice, though individual applicants may be worse off depending on their value to decision-makers and risk tolerance, and (3) is more robust to disparities in the number of applications submitted.