Timing Objectives in Dynamic Kidney Exchange

Abstract

I adapt a state-of-the-art dynamic kidney exchange framework to account for timing considerations by introducing an additional timing variable in the state of the art kidney exchange framework in order to examine timing-aware extensions of offline medical objectives. I develop algorithms that learn an objective corresponding to various weighted averages of the timing and offline medical objectives and examine their performance with respect to both the total number of matches and the average amount of time patients spend in the exchange. I then examine the tradeoff between the performance of these algorithms with respect to both timing and match quality in order to determine whether introducing timing considerations in these objective functions results in more effective matching strategies. The results suggest that, at low levels of death in the kidney graph (i.e., patients do not die quickly), timing-aware algorithms generally achieve comparable results on the medical objective while reducing the average amount of time people spend in the exchange.