Monoculture in Matching Markets

Algorithmic monoculture arises when many decision-makers rely on the same algorithm to evaluate applicants. An emerging body of work investigates possible harms of such homogeneity, but has been limited by the challenge of incorporating market effects in which the preferences 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 and have higher variance outcomes depending on their value to decision-makers, and (3) is more robust to disparities in the number of applications submitted. Overall, our approach strengthens, challenges, and broadens the scope of the existing monoculture literature.