Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair Pairwise Exchange among Groups
Authors: Zhaohong Sun, Taiki Todo, Toby Walsh
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the pairwise organ exchange problem among groups motivated by real-world applications and consider two types of group formulations. Each group represents either a certain type of patientdonor pairs who are compatible with the same set of organs, or a set of patient-donor pairs who reside in the same region. We address a natural research question, which asks how to match a maximum number of pairwise compatible patient-donor pairs in a fair and individually rational way. We first propose a natural fairness concept that is applicable to both types of group formulations and design a polynomial-time algorithm that checks whether a matching exists that satisfies optimality, individual rationality, and fairness. We also present several running time upper bounds for computing such matchings for different graph structures. |
| Researcher Affiliation | Academia | Zhaohong Sun1 , Taiki Todo2 and Toby Walsh1 1UNSW Sydney 2Kyushu University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Computing a fair matching w.r.t. δ and δ |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical datasets or provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and analysis; it does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper discusses algorithms and problem formulations but does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or solvers with their versions). |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |