Weighted Fairness Notions for Indivisible Items Revisited
Authors: Mithun Chakraborty, Erel Segal-Halevi, Warut Suksompong4949-4956
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit the setting of fairly allocating indivisible items when agents have different weights representing their entitlements. First, we propose a parameterized family of relaxations for weighted envy-freeness and the same for weighted proportionality; the parameters indicate whether smallerweight or larger-weight agents should be given a higher priority. We show that each notion in these families can always be satisfied, but any two cannot necessarily be fulfilled simultaneously. We then introduce an intuitive weighted generalization of maximin share fairness and establish the optimal approximation of it that can be guaranteed. Furthermore, we characterize the implication relations between the various weighted fairness notions introduced in this and prior work, and relate them to the lower and upper quota axioms from apportionment. |
| Researcher Affiliation | Academia | 1 Department of Electrical Engineering and Computer Science, University of Michigan, USA 2 Department of Computer Science, Ariel University, Israel 3 School of Computing, National University of Singapore, Singapore |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not describe a software implementation of its proposed notions or rules, hence it does not provide access to open-source code for its methodology. |
| Open Datasets | No | The paper focuses on theoretical concepts and does not use or describe any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper describes theoretical work and does not report on computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical concepts and does not describe software implementations or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup with hyperparameters or system-level training settings. |