Fair Pairwise Exchange among Groups

Authors: Zhaohong Sun, Taiki Todo, Toby Walsh

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {zhaohong.sun, t.walsh}@unsw.edu.au, todo@inf.kyushu-u.ac.jp
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.