🤖 AI Summary
This study addresses the misaligned incentives among transplant centers, clinicians, and regulatory agencies—a critical yet overlooked factor that undermines the effectiveness of current heart allocation policies. For the first time, organ allocation is modeled as a multi-agent strategic process, and an incentive-aware allocation framework is proposed that integrates mechanism design with data-driven methodologies. By synthesizing insights from mechanism design, strategic classification, causal inference, and social choice theory, the work systematically quantifies the real-world adverse impacts of incentive misalignment in adult heart transplantation in the United States. The resulting framework offers a novel paradigm for designing allocation policies that are not only more equitable and efficient but also robust to strategic behavior, thereby advancing interdisciplinary innovation at the intersection of machine learning and social science.
📝 Abstract
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.