Proportionality in Approval-Based Elections With a Variable Number of Winners
Authors: Rupert Freeman, Anson Kahng, David M. Pennock
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we show an upper bound on AS that any deterministic rule can provide, and that straightforward adaptations of deterministic rules from the fixed number of winners setting do not achieve better than a 1/2 approximation to AS even for large numbers of candidates. We then prove that a natural randomized rule achieves a 29/32 approximation to AS. |
| Researcher Affiliation | Collaboration | 1Microsoft Research New York City 2Carnegie Mellon University 3Rutgers University |
| Pseudocode | No | The paper describes algorithms in text but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets; therefore, no information about publicly available datasets or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving datasets; therefore, no information about training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe conducting experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe conducting experiments; therefore, no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe conducting experiments; therefore, no experimental setup details like hyperparameters are provided. |