Deep Learning for Two-Sided Matching

Two-sided matching markets, such as Uber, Airbnb, stock markets, and dating apps, play a significant role in today's world. As a result, there is a tremendous and rising interest to design better mechanisms for two-sided matching. The seminal work of Gale and Shapley [14] introduced a simple mechanism for stable matching in two-sided markets--Deferred-acceptance (DA)--which has since has been applied in doctor-hospital matching [25], school choice [3, 22, 2], and the matching of cadets to their branches of military service [30, 29]. DA is stable, i.e., no pair of agents mutually prefer each other to their DA partners. On the other hand, DA is not strategy-proof (SP); that is, under fully general preferences, it is always possible that some agent can mis-report her preferences to obtain a better matching than she would receive under the DA mechanism.