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Unbalanced Random Matching Markets with Partial Preferences

Authors Aditya Potukuchi , Shikha Singh

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  • Theory of computation → Randomness, geometry and discrete structures
Keywords
  • stable matching
  • probabilistic method
  • Gale-Shapley algorithm

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Abstract

Properties of stable matchings in the popular random-matching-market model have been studied for over 50 years. In a random matching market, each agent has complete preferences drawn uniformly and independently at random. Wilson (1972), Knuth (1976) and Pittel (1989) proved that in balanced random matching markets, the proposers are matched to their ln nth choice on average. In this paper, we consider competitive markets with n jobs and n+k candidates, and partial lists where each agent only ranks their top d choices. Despite the long history of the problem, the following fundamental question remains unanswered for these generalized markets: what is the tight threshold on list length d that results in a perfect stable matching with high probability? In this paper, we answer this question exactly - we prove a sharp threshold d₀ = ln n ⋅ ln (n+k)/(k+1) on the existence of perfect stable matchings when k = o(n). That is, we show that if d < (1-ε) d₀, then no stable matching matches all jobs; moreover, if d > (1+ ε) d₀, then all jobs are matched in every stable matching with high probability. This bound improves and generalizes recent results by Kanoria, Min and Qian (2021). 
Furthermore, we extend the line of work studying the effect of imbalance on the expected rank of the proposers (termed the "stark effect of competition"). We establish the regime in unbalanced markets that forces this stark effect to take shape in markets with partial preferences.

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Aditya Potukuchi and Shikha Singh. Unbalanced Random Matching Markets with Partial Preferences. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 125:1-125:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICALP.2025.125

Author Details

Aditya Potukuchi
  • Department of Electrical Engineering and Computer Science, York University, Toronto, Canada
Shikha Singh
  • Department of Computer Science, Williams College, Williamstown, MA, USA

Funding

  • Potukuchi, Aditya: Research supported by an NSERC Discovery Grant.
  • Singh, Shikha: Research supported in part by NSF CCF 1947789.

Acknowledgements

We thank anonymous reviewers for their valuable feedback to help improve this paper.

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