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

arXiv: https://arxiv.org/abs/2602.04990 · HTML: https://mlsanity.com/paper/2602.04990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 77 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [A Survey of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2312.14925/) (2023)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback](https://mlsanity.com/paper/2404.10271/) (2024)
- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Strategyproof Linear Regression in High Dimensions](https://mlsanity.com/paper/1805.10693/) (2018)
- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
- [Policy Optimization for Dynamic Heart Transplant Allocation](https://mlsanity.com/paper/2512.12497/) (2025)
- [Incentive-Compatible Kidney Exchange in a Slightly Semi-Random Model](https://mlsanity.com/paper/2106.11387/) (2021)
- [Automated Mechanism Design for Classification with Partial Verification](https://mlsanity.com/paper/2104.05182/) (2021)

## Built on by (most cited first, 4 of 4)

- [Learning Potentials for Dynamic Matching and Application to Heart Transplantation](https://mlsanity.com/paper/2602.08878/) (2026)
- [Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis](https://mlsanity.com/paper/2606.02671/) (2026)
- [Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values](https://mlsanity.com/paper/2608.28620/) (2026)
- [Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring](https://mlsanity.com/paper/2610.12538/) (2026)

## Abstract (arXiv)

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.