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..
An Experimental Comparison of Multiwinner Voting Rules on Approval Elections
Authors: Piotr Faliszewski, Martin Lackner, Krzysztof Sornat, Stanisลaw Szufa
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we experimentally compare major approval-based multiwinner voting rules. |
| Researcher Affiliation | Academia | 1AGH University, Poland 2TU Wien, Austria 3IDSIA, USI-SUPSI, Switzerland |
| Pseudocode | No | The paper describes algorithms conceptually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for the experiments is available at https://github.com/ Project-PRAGMA/Map-of-Rules-IJCAI-2023. |
| Open Datasets | Yes | We also use real-life participatory budgeting (PB) data from Pabulib [Stolicki et al., 2020] |
| Dataset Splits | No | The paper describes generating instances from statistical cultures and selecting a subset from a real-life dataset, but does not specify explicit training/validation/test splits of these instances. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running experiments are mentioned in the paper. |
| Software Dependencies | Yes | in our experiments we use the implementations of the rules provided in the abcvoting library [Lackner et al., 2023]. The reference indicates 'Journal of Open Source Software, 8(81):4880, 2023'. |
| Experiment Setup | Yes | We generated 6000 instances with 100 candidates and 100 voters from the six following statistical cultures (1000 elections per culture): 1D-Euclidean with r = 0.05, 2DEuclidean with r = 0.2, resampling with p = 0.1 and ฯ {0, 1 999, 2 999, . . . , 998 999, 1}, disjoint with p = 0.1, ฯ {0, 1 999, 2 999, . . . , 998 999, 1}, and g = 10, party-list with g = 10, and the Pabulib model. For all instances, we use a committee size of k = 10. |