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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Toward Completing the Picture of Control in Schulze and Ranked Pairs Elections
Authors: Cynthia Maushagen, David Niclaus, Paul Nüsken, Jörg Rothe, Tessa Seeger
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 19,785 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,306 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We solve a number of these open cases for Schulze and ranked pairs. In addition, we fix a flaw in the reduction of Menton and Singh showing that Schulze is resistant to constructive control by deleting candidates and reestablish a vulnerability result for destructive control by deleting candidates. In some of our proofs, we study variants of s-t vertex cuts in graphs that are related to our control problems. |
| Researcher Affiliation | Academia | Cynthia Maushagen , David Niclaus , Paul N usken , J org Rothe and Tessa Seeger Heinrich-Heine-Universit at D usseldorf, MNF, Institut f ur Informatik, D usseldorf, Germany EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve datasets for training or empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |