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 [1].
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. |