The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators. Focusing on US adult heart transplant allocation, we identify critical incentive misalignments across the decision-making pipeline, and present data showing that they are having adverse consequences today. Our main position is that the next generation of allocation policies should be incentive aware. We outline a research agenda for the machine learning community, calling for the integration of mechanism design, strategic classification, causal inference, and social choice to ensure robustness, efficiency, fairness, and trust in the face of strategic behavior from the various constituent groups.

翻译：摘要：稀缺供体器官的分配构成了医疗领域最具影响力的算法挑战之一。尽管该领域正迅速从僵化的基于规则的系统转向机器学习与数据驱动优化，但我们认为当前方法常常忽略了一个根本性障碍：激励机制。在这篇立场论文中，我们强调器官分配不仅是一个优化问题，更是一个涉及器官获取组织、移植中心、临床医生、患者及监管机构的复杂博弈。聚焦美国成人心脏移植分配，我们识别出决策链中关键激励错位问题，并通过数据证明这些问题如今已引发不良后果。我们的核心立场是：下一代分配政策应当具备激励感知能力。我们为机器学习社区提出研究议程，呼吁整合机制设计、策略分类、因果推断与社会选择理论，以确保在应对各参与群体策略性行为时，政策的鲁棒性、效率、公平性与可信度。