Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise
Authors: Ioannis Caragiannis, Christos Kaklamanis, Nikos Karanikolas, George A. Krimpas
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {caragian, kakl, nkaranik, krimpas}@ceid.upatras.gr |
| 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. |