Near-Feasible Stable Matchings: Incentives and Optimality

Researchers have developed new algorithms that guarantee stable matchings in complex systems like school admissions and job markets by making minimal capacity adjustments. The breakthrough proves optimal modifications can be computed efficiently while maintaining stability, solving a fundamental problem where traditional methods leave participants unmatched. The framework analyzes agent incentives and shows different strategies significantly affect outcomes, with experimental results confirming practical applications for both tractable and complex matching scenarios.