UCLA

Numerous dynamic matching market models pursuing different objectives have been developed for kidney exchange studies. These objectives range from minimizing waiting time and maximizing welfare to reducing the fraction of unmatched agents. Motivated by the medical observation that better matching outcomes are often achieved when donors and recipients share the same race, we extend the dynamic matching model by Akbarpour et al. (2020) to a matching market with two types of agents, in which the compatible probability depends on agent types. In this study, we examine the performance of two matching algorithms, namely Greedy algorithm and Patient algorithm, from both theoretical and empirical perspectives. Our aim is to investigate whether delaying the matching process to thicken the market can effectively decrease the fraction of unmatched agents within the market.