The Metric Distortion of Multiwinner Voting
Authors: Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris4900-4907
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We reveal a surprising trichotomy on the distortion of multiwinner voting rules in terms of k and q: The distortion is unbounded when q k/3, asymptotically linear in the number of agents when k/3 < q k/2, and constant when q > k/2. We propose a novel deterministic multiwinner voting rule, called POLAROPPOSITES (see Algorithm 1), which runs in polynomial time and achieves a distortion of O(n). |
| Researcher Affiliation | Academia | Ioannis Caragiannis,1 Nisarg Shah,2 Alexandros A. Voudouris3 1Department of Computer Science, Aarhus University 2Department of Computer Science, University of Toronto 3School of Computer Science and Electronic Engineering, University of Essex |
| Pseudocode | Yes | Algorithm 1: POLAROPPOSITES |
| Open Source Code | No | The paper does not mention providing open-source code or include any links to code repositories. |
| Open Datasets | No | This is a theoretical paper focusing on mathematical proofs and algorithm design, not empirical studies using datasets. Thus, it does not mention public datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments with dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper that defines algorithms and mathematical concepts but does not include details about an experimental setup with hyperparameters or training configurations. |