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..
A Quantitative Analysis of Multi-Winner Rules
Authors: Martin Lackner, Piotr Skowron
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With both theoretical and experimental methods we classify multi-winner rules in terms of their quantitative alignment with these two opposing objectives. In Section 4, we complement the worst-case analysis from Section 3 with an experimental study yielding approximation ratios for actual data sets. |
| Researcher Affiliation | Academia | TU Wien, Vienna, Austria 2University of Warsaw, Warsaw, Poland |
| Pseudocode | No | The paper describes methods in text and uses mathematical formulas, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We have used two data sets: proļ¬les obtained from preļ¬ib. org [Mattei and Walsh, 2013] and proļ¬les generated via a uniform distribution. |
| Dataset Splits | No | The paper describes characteristics of the datasets and the parameters used for experiments (e.g., committee size k), but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We restricted our attention to proļ¬les where both the AV-ratio of CC and the CC-ratio of AV is at most 0.9. This excludes proļ¬les where an (almost) perfect compromise between AV and CC exists. The uniform dataset consists of 500 proļ¬les with 20 candidates and 50 voters, each. Voters approval sets are of size 2 5 (chosen uniformly at random); the approval sets of a given size are also chosen uniformly at random. Experiments for the uniform dataset use a committee size of k = 5. |