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..
The Metric Distortion of Multiwinner Voting
Authors: Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris4900-4907
AAAI 2022 | Venue PDF | 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. |