Efficient and Fair Healthcare Rationing
Authors: Haris Aziz, Florian Brandl
JAIR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable. We prove that Reverse Rejecting rules characterize all outcomes that satisfy the first three properties. We show how Reverse Rejecting rules can be used to obtain the Smart Reverse Rejecting rule, which processes a given number of unreserved units first and last and which incorporates the additional goal of allocating the largest feasible number of units from a designated subset of categories called preferential categories. The Smart Reverse Rejecting rule satisfies a new axiom we call order preservation, which is parameterized by how many unreserved units are processed first and last. Moreover, it generalizes two well-known reserves rules over-and-above and minimum-guarantees (Galanter, 1961, 1984) that are understood in the context of preferential categories having consistent priorities. |
| Researcher Affiliation | Academia | Haris Aziz EMAIL UNSW Sydney, Australia Florian Brandl EMAIL University of Bonn, Germany |
| Pseudocode | Yes | The REV π rule then works as follows: Let the set of rejected agents R be empty at the start and consider the agents in ascending order of π. When considering agent i, add i to the rejected agents R if and only if ms(B (R {i}) I ) = ms(BI). After the last agent has been considered, let RI be the final set of rejected agents and choose a maximum size matching of the reduced reservation graph B RI I . More precisely, S REV π works as follows. Let the set of agents to be given unreserved units from c1 u, N1, be empty at the start and consider the agents in order of the baseline ordering in descending order. When agent i is considered, add i to N1 if N1 contains fewer than qc1u agents and the agents in N \ (N1 {i}) can form a maximum beneficiary assignment. After the last agent has been considered, give each agent in N1 an unreserved unit from c1 u. Use REV π to allocate the units reserved for the preferential categories Cp to the remaining agents. Lastly, give the unreserved units from c2 u to the remaining agents in descending order of the baseline ordering. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the release of source code for the methodology described. |
| Open Datasets | No | The paper presents theoretical work on allocation rules and uses illustrative examples (e.g., Example 3.5, 4.1) rather than empirical evaluation on specific datasets. No publicly available or open datasets are mentioned or referenced for experimental use. |
| Dataset Splits | No | The paper does not conduct experiments on datasets, therefore no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper describes theoretical models and algorithms. It does not include experimental results that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not describe empirical experiments. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical in nature, presenting definitions, theorems, and algorithms without empirical validation. Consequently, there are no experimental setup details such as hyperparameters or system-level training settings. |