Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives

• URL: http://arxiv.org/abs/2602.04990v2
• Date: Fri, 06 Feb 2026 02:55:11 GMT
• Title: Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
• Authors: Ioannis Anagnostides, Itai Zilberstein, Zachary W. Sollie, Arman Kilic, Tuomas Sandholm,
• Abstract summary: The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare.<n>Current approaches often overlook a fundamental barrier: incentives.<n>We argue that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators.
• Score: 45.83272225462161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Quark Medical Alignment: A Holistic Multi-Dimensional Alignment and Collaborative Optimization Paradigm [7.449373800890174]
  Reinforcement learning for large language model alignment has progressed rapidly in recent years.<n> transferring these paradigms to high-stakes medical question answering reveals a fundamental paradigm mismatch.<n>We propose a robust medical alignment paradigm to address these challenges.
  arXiv  Detail & Related papers  (2026-02-12T07:26:23Z)
• Learning Potentials for Dynamic Matching and Application to Heart Transplantation [45.83272225462161]
  We propose a novel framework for non-myopic policy optimization in general online matching relying on potentials.<n>Our approach is a form of self-supervised imitation learning: the potentials are trained to mimic an algorithm that has perfect foresight.
  arXiv  Detail & Related papers  (2026-02-09T16:39:12Z)
• Policy Optimization for Dynamic Heart Transplant Allocation [48.56507763517103]
  Heart transplantation is a viable path for patients suffering from advanced heart failure.<n>The current allocation policy does not adequately take into account pretransplant and post-transplant mortality.<n>We develop a new simulator that enables us to evaluate and compare the performance of different policies.
  arXiv  Detail & Related papers  (2025-12-13T23:51:31Z)
• Collective dynamics of strategic classification [49.14942990735098]
  We apply evolutionary game theory to the problem of feedback loops between collectives of users and institutions.<n>We show that increased detection capabilities reduce social costs and could lead to users' improvement.<n>The speed at which the institutions re-adapt to the user's population plays a role in the final outcome.
  arXiv  Detail & Related papers  (2025-08-12T20:57:17Z)
• When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems [78.04679174291329]
  We introduce a proof-of-concept to simulate the risks of malicious multi-agent systems (MAS)<n>We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud.<n>Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones.
  arXiv  Detail & Related papers  (2025-07-19T15:17:30Z)
• Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges [2.7624021966289605]
  We provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine.<n>The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians.
  arXiv  Detail & Related papers  (2025-04-11T09:01:01Z)
• Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation [15.55939232262832]
  Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States.<n>We propose a novel efficient and adaptive federate learning method for cardiac segmentation.
  arXiv  Detail & Related papers  (2025-01-06T18:57:18Z)
• Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework [3.577634519691725]
  We propose a novel Hierarchical Multi-Agent Reinforcement Learning framework.<n>Our architecture deploys specialized and dedicated agents for each organ system.<n>We introduce a dual-layer state representation technique that contextualizes patient conditions at both global and organ-specific levels.
  arXiv  Detail & Related papers  (2024-09-06T12:26:47Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Learning Directional Feature Maps for Cardiac MRI Segmentation [13.389141642517762]
  We propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes. Specifically, we perform cardiac segmentation and learn a direction field pointing away from the nearest cardiac tissue boundary to each pixel. Based on the learned direction field, we then propose a feature rectification and fusion (FRF) module to improve the original segmentation features.
  arXiv  Detail & Related papers  (2020-07-22T11:31:04Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.