TO THE U.S. ORGAN TRANSPLANTATION AND ALLOCATION NETWORK

Organ transplantation and allocation has been a contentious issue in the U.S. for decades. The scarcity of donors highlights the need for improving the efficiency of the current policy. While the system is too complex for analytical modeling, agent-based modeling and simulation (ABMS), provides a promising approach to evaluate the allocation policy alternatives, since ABMS can model interactions of autonomous individuals in a network. In this paper, we develop an ABMS model in which organ procurement organizations (OPOs) and recipient candidates are modeled as agents. We model the multi-criteria decision making process for organ offering, in which we prioritize patients in the organ matching process in terms of mortality risk and geographic preference. We take liver transplantation and allocation as our case study for detailed policy modeling, input analysis, and output analysis. For future work, we plan to 1) incorporate more system heterogeneity, 2) develop a simulation optimization framework to select the optimal policy, and 3) empower OPOs with some learning capability