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 a static optimization problem, but rather a complex game involving transplant centers, clinicians, 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, and fairness in the face of strategic behavior from the various constituent groups.

翻译：稀缺供体器官的分配构成了医疗保健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和数据驱动的优化，但我们认为当前方法往往忽视了一个根本性障碍：激励。在这篇立场论文中，我们强调器官分配不仅仅是一个静态优化问题，而是一个涉及移植中心、临床医生和监管机构的复杂博弈。聚焦于美国成人心脏移植分配，我们识别了决策流程中关键的激励错配，并提供了数据表明这些错配正在当下产生不利后果。我们的核心立场是：下一代分配策略应具备激励意识。我们为机器学习界勾勒了一个研究议程，呼吁整合机制设计、策略分类、因果推断和社会选择理论，以确保在面对各利益相关方策略性行为时的鲁棒性、效率和公平性。