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].
Multiwinner Elections With Diversity Constraints
Authors: Robert Bredereck, Piotr Faliszewski, Ayumi Igarashi, Martin Lackner, Piotr Skowron
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the computational complexity of computing winning committees in this model, obtaining polynomial-time algorithms (exact and approximate) and NP-hardness results. We focus on several natural classes of voting rules and diversity constraints. Our main results are presented in Table 1. |
| Researcher Affiliation | Academia | Robert Bredereck University of Oxford, Oxford, UK; TU Berlin, Berlin, Germany EMAIL Piotr Faliszewski AGH University, Krakow, Poland EMAIL Ayumi Igarashi University of Oxford, Oxford, UK EMAIL Martin Lackner TU Wien, Vienna, Austria EMAIL Piotr Skowron TU Berlin, Berlin, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: Greedy Algorithm 1; Algorithm 2: Greedy Algorithm for BCWD |
| Open Source Code | No | The paper is theoretical and does not mention or provide access to any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or describe any datasets for training. Therefore, it does not provide information on dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not use or describe any datasets, thus no validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity and algorithms. It does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |