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Main Authors: Sandholtz, Will, Tai, Andrew
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2605.26367
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author Sandholtz, Will
Tai, Andrew
author_facet Sandholtz, Will
Tai, Andrew
contents We study stochastic object assignment problems in which objects may have minimum and maximum requirements, such as with classes with upper and lower enrollment bounds. We construct a new random assignment mechanism, the minimums probabilistic serial (MPS) mechanism, which generalizes the Probabilistic Serial mechanism of Bogomolnaia and Moulin (2001). The random allocation produced by MPS is guaranteed to be Pareto efficient; that is, there is no other implementable allocation that all agents prefer via first order stochastic dominance. We also show that MPS is i) envy-free, in that no agent will strictly prefer another agent's assignment, and ii) weak strategyproof, in that agents cannot achieve a better assignment by misreporting their preferences.
format Preprint
id arxiv_https___arxiv_org_abs_2605_26367
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Random Matching with Minimums
Sandholtz, Will
Tai, Andrew
Theoretical Economics
J.4
We study stochastic object assignment problems in which objects may have minimum and maximum requirements, such as with classes with upper and lower enrollment bounds. We construct a new random assignment mechanism, the minimums probabilistic serial (MPS) mechanism, which generalizes the Probabilistic Serial mechanism of Bogomolnaia and Moulin (2001). The random allocation produced by MPS is guaranteed to be Pareto efficient; that is, there is no other implementable allocation that all agents prefer via first order stochastic dominance. We also show that MPS is i) envy-free, in that no agent will strictly prefer another agent's assignment, and ii) weak strategyproof, in that agents cannot achieve a better assignment by misreporting their preferences.
title Random Matching with Minimums
topic Theoretical Economics
J.4
url https://arxiv.org/abs/2605.26367