# Efficient, Fair, and Incentive-Compatible Healthcare Rationing

arXiv: https://arxiv.org/abs/2102.04384 · HTML: https://mlsanity.com/paper/2102.04384/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2021.

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

- [Local Justice and the Algorithmic Allocation of Societal Resources](https://mlsanity.com/paper/2112.01236/) (2021)
- [Differentiable Arbitrating in Zero-sum Markov Games](https://mlsanity.com/paper/2302.10058/) (2023)
- [Individual Fairness under Varied Notions of Group Fairness in Bipartite Matching - One Framework to Approximate Them All](https://mlsanity.com/paper/2208.09951/) (2022)
- [First-Choice Maximality Meets Ex-ante and Ex-post Fairness](https://mlsanity.com/paper/2305.04589/) (2023)

## Abstract (arXiv)

Rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories and each category has a priority ranking of the patients. We present an allocation rule that respects the priorities, complies with the eligibility requirements, allocates the largest feasible number of units, and does not incentivize agents to hide that they qualify through a category. The rule characterizes all possible allocations that satisfy the first three properties and is polynomial-time computable.