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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Elections with Few Voters: Candidate Control Can Be Easy
Authors: Jiehua Chen, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the computational complexity of candidate control in elections with few voters (that is, we take the number of voters as a parameter). We use the formal tools of parameterized complexity theory. |
| Researcher Affiliation | Academia | 1TU Berlin, Berlin, Germany EMAIL, EMAIL 2AGH University of Science and Technology, Krakow, Poland EMAIL |
| Pseudocode | No | The paper describes algorithmic approaches and their complexities but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |