A Minimax Perspective on Almost-Stable Matchings
Frederik Glitzner, David Manlove
TL;DR
We address stability in matching markets when full stability is infeasible and propose a minimax fairness approach that minimizes the maximum number of blocking pairs any agent faces. The paper defines two primary problems, Minimax-AlmostStable-sri and Minimax-AlmostStable-Max-smi, and provides a comprehensive complexity landscape: NP-completeness results even for constant-bounded preferences, polynomial-time algorithms for very short lists, and both approximation strategies and exact ILP formulations. Empirical results suggest that balanced (low per-agent instability) almost-stable matchings are common in practice and efficiently computable via ILP, even when exact guarantees are hard in theory. Overall, the work delineates the trade-offs between fairness guarantees and computational feasibility, offering practical tools for real-world market design and insights into the algorithmic limits of distributing instability fairly.
Abstract
Stability is crucial in matching markets, yet in many real-world settings - from hospital residency allocations to roommate assignments - full stability is either impossible to achieve or can come at the cost of leaving many agents unmatched. When stability cannot be achieved, algorithmicists and market designers face a critical question: how should instability be measured and distributed among participants? Existing approaches to "almost-stable" matchings focus on aggregate measures, minimising either the total number of blocking pairs or the count of agents involved in blocking pairs. However, such aggregate objectives can result in concentrated instability on a few individual agents, raising concerns about fairness and incentives to deviate. We introduce a fairness-oriented approach to approximate stability based on the minimax principle: we seek matchings that minimise the maximum number of blocking pairs any agent is in. Equivalently, we minimise the maximum number of agents that anyone has justified envy towards. This distributional objective protects the worst-off agents from a disproportionate amount of instability. We characterise the computational complexity of this notion across fundamental matching settings. Surprisingly, even very modest guarantees prove computationally intractable: we show that it is NP-complete to decide whether a matching exists in which no agent is in more than one blocking pair, even when preference lists have constant-bounded length. This hardness applies to both Stable Roommates and maximum-cardinality Stable Marriage. On the positive side, we provide polynomial-time algorithms when agents rank at most two others, and present approximation algorithms and integer programs. Our results map the algorithmic landscape and reveal fundamental trade-offs between distributional guarantees and computational feasibility.