Efficient Interview Scheduling for Stable Matching
Moshe Babaioff, Rotem Gil, Assaf Romm

TL;DR
This paper introduces adaptive algorithms for efficient interview scheduling in stable matching markets with uncertain preferences, minimizing interviews and rounds while ensuring interim-stability.
Contribution
It proposes two novel algorithms that optimize interview scheduling for stable matchings under preference uncertainty, balancing efficiency and stability guarantees.
Findings
Sequential algorithm performs 2 interviews per agent on average.
Hybrid algorithm achieves polylogarithmic interview rounds with about 2 interviews per agent.
Scheduled interviews guarantee interim-stability when using Deferred-Acceptance.
Abstract
The study of stable matchings usually relies on the assumption that agents' preferences over the opposite side are complete and known. In many real markets, however, preferences might be uncertain and revealed only through costly interactions such as interviews. We show how to reach interim-stable matchings, under which all matched pairs must have interviewed and agents use expected utilities whenever true values remain unknown, while minimizing both the expected number of interviews and the expected number of interview rounds. We introduce two adaptive algorithms that produce interim-stable matchings: one operates sequentially, and another is a hybrid algorithm that begins by scheduling some interviews in parallel and continues sequentially. Focusing on cases where agents are ex-ante indifferent between agents on the other side, we show that the sequential algorithm performs 2…
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Taxonomy
TopicsGame Theory and Voting Systems · Auction Theory and Applications · Optimization and Search Problems
