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].
The Complexity of Proportionality Degree in Committee Elections
Authors: Łukasz Janeczko, Piotr Faliszewski5092-5099
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the complexity of computing committees with a given proportionality degree and of testing if a given committee provides a particular one. This way, we complement recent studies that mostly focused on the notion of (extended) justified representation. We also study the problems of testing if a cohesive group of a given size exists and of counting such groups. |
| Researcher Affiliation | Academia | AGH University, Krak ow, Poland |
| Pseudocode | No | The paper describes an ILP formulation in text, but it does not present it as a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not include any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical, focusing on computational complexity and mathematical properties. It does not conduct experiments on datasets, thus there is no mention of training data, public datasets, or access information for them. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, it does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity. It does not describe any empirical experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any empirical experiments that would require specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on computational complexity. It does not describe an empirical experimental setup, and therefore no hyperparameters or system-level training settings are mentioned. |