Two-Sided Time-Independent Regret for Matching Markets with Limited Interviews
Researchers have developed a new framework for matching markets that incorporates limited interviews, allowing participants to gather partial preference information before committing to applications or offers. This approach models interactions as queried "hints" that reveal preferences while constraining subsequent actions. The framework also addresses firm-side uncertainty and introduces strategic deferral, enabling temporary vacancies to correct premature commitments. Algorithms designed for both centralized and decentralized markets demonstrate that a constant number of interviews per round is sufficient for horizon-independent regret, improving upon existing guarantees. AI
IMPACT Introduces novel algorithms for optimizing matching processes with limited information, potentially impacting AI-driven recruitment and resource allocation systems.