Individual Representation in Approval-Based Committee Voting
Authors: Markus Brill, Jonas Israel, Evi Micha, Jannik Peters4892-4899
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To complement our theoretical results, we performed experiments on generated approval profiles.Setup. Inspired by Peters et al. (2021), we used six models to generate approval profiles: a voter-interval model (VI), a candidate-interval model (CI), a two-dimensional Euclidean model (2D), an impartial culture model (IC), an urn model (Urn), and a Mallows model (Mallows). |
| Researcher Affiliation | Academia | Markus Brill,1 Jonas Israel,1 Evi Micha,2 Jannik Peters1 1 Research Group Efficient Algorithms, TU Berlin, Germany 2 Department of Computer Science, University of Toronto, Canada |
| Pseudocode | Yes | Algorithm 1: (2, 4)-IR for Voter Interval Profiles |
| Open Source Code | No | The paper cites the `abcvoting` Python library, but does not state that the specific code for the methodology or experiments described in this paper is open-source or available via a link. The full version is an arXiv technical report, which typically doesn't include code. |
| Open Datasets | No | The paper uses generated approval profiles based on six models, rather than publicly available datasets. No access information (link, citation for the generated data) is provided. |
| Dataset Splits | No | The paper describes experiments on generated data, but it does not specify training, validation, or testing splits as it is not a machine learning paper involving model training. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper mentions the `abcvoting` Python library but does not provide specific version numbers for it or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | For all conducted experiments, we have 100 voters and 50 candidates and generated 500 instances for each model and for each k [50]. |