Approval-Based Committee Voting under Incomplete Information
Authors: Aviram Imber, Jonas Israel, Markus Brill, Benny Kimelfeld5076-5083
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate approval-based committee voting with incomplete information about the approval preferences of voters. We study the complexity of some fundamental computational problems for a number of classic approval-based committee voting rules |
| Researcher Affiliation | Academia | 1 Technion Israel Institute of Technology, Haifa, Israel 2 Research Group Efficient Algorithms, TU Berlin, Germany |
| Pseudocode | No | The paper describes methods and proofs using narrative text and mathematical notation, but it does not contain any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code for the methodologies described, nor does it provide links to any code repositories. |
| Open Datasets | No | This paper is theoretical and focuses on computational complexity analysis. It does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and focuses on computational complexity analysis. It does not involve empirical validation on datasets, therefore there are no validation splits mentioned. |
| Hardware Specification | No | This paper is theoretical and focuses on computational complexity analysis. It does not describe any empirical experiments, and therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and focuses on computational complexity analysis. It does not describe any specific software or libraries with version numbers that were used for its research. |
| Experiment Setup | No | This paper is theoretical and focuses on computational complexity analysis. It does not describe empirical experiments, and therefore no specific experimental setup details like hyperparameters or training configurations are provided. |