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..
Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise
Authors: Ioannis Caragiannis, Christos Kaklamanis, Nikos Karanikolas, George A. Krimpas
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are. In particular, we identify (in Section 3) the modal committee rule (MC) as the ultimately robust ABCC rule: MC is robust against all kinds of reasonable noise. AV follows in terms of robustness and seems to outperform other known ABCC rules (see Section 4). In contrast, the well-known approval Chamberlin Courant (CC) rule is the least robust. On the other hand, all ABCC rules are robust if we restrict noise sufficiently (see Section 5). |
| Researcher Affiliation | Academia | 1Department of Computer Engineering and Informatics, University of Patras, 26504 Rion, Greece 2Computer Technology Institute Diophantus , 26504 Rion, Greece EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit release statements) to open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical, focusing on mathematical properties and noise models, and does not involve empirical training on datasets. Therefore, it does not mention public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation or dataset splits for such purposes. It focuses on analytical properties of voting rules. |
| Hardware Specification | No | The paper is theoretical and describes mathematical analysis rather than computational experiments, so it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and describes mathematical analysis rather than computational experiments, so it does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on analytical properties of voting rules, not empirical experiments that would require details about experimental setup or hyperparameters. |