Peter Doe

Typically, matching market models ignore prior commitments. Yet many job seekers, for example, are already employed. I analyze two-sided matching markets with pre-existing binding agreements between market participants. In this model, a pair of participants bound to each other by a pre-existing agreement must agree to any action they take. To analyze their behavior, I propose a new solution concept, the agreeable core, consisting of the matches which cannot be renegotiated without violating the binding agreements. My main contribution is an algorithm that constructs such a match by a novel combination of the Deferred Acceptance and Top Trading Cycles algorithms. The algorithm is robust to various manipulations and has applications to numerous markets including the resident-to-hospital match, college admissions, school choice, and labor markets.

I study a behavioral model of early matching within the context of the National Resident Matching Program. In my model, two hospitals choose to give early offers to doctors prior to a stable match. Some doctors have a behavioral preference to early match while others do not. I show that the less-desirable program benefits from the option to make early offers. My results provide a theoretical foundation for behavior widely documented within the medical ethics and graduate medical education literature and confirm beliefs commonly held by residency program directors.