The Complexity of Stable Matchings under Substitutable Preferences.

In various matching market settings, such as hospital-doctor matching markets (Hatfield and Milgrom 2005), the existence of stable outcomes depends on substitutability of preferences. But can these stable matchings be computed efficiently, as in the one-to-one matching case? The algorithm of (Hatfield and Milgrom 2005) requires efficient implementation of a choice function over substitutable preferences. We show that even given efficient access to a value oracle or preference relation satisfying substitutability, exponentially many queries may be required in the worst case to implement a choice function. Indeed, this extends to examples where a stable matching requires exponential time to compute. We characterize the computational complexity of stable matchings by showing that efficient computation of a choice function is equivalent to efficient verification-determining whether or not, for a given set, the most preferred subset is the entire set itself. Clearly, verification is necessary for computation, but we show that it is also sufficient: specifically, given a verifier, we design a polynomial-time algorithm for computing a choice function, implying an efficient algorithm for stable matching. We then show that a verifier can be implemented efficiently for various classes of functions, such as submodular functions, implying efficient stable matching algorithms for a broad range of settings. We also investigate the effect of ties in the preference order, which causes complications both in defining substitutes and in computation. In this case, we tightly connect the computational complexity of the choice function to a measure on the number of ties.