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..
Manipulating Districts to Win Elections: Fine-Grained Complexity
Authors: Eduard Eiben, Fedor Fomin, Fahad Panolan, Kirill Simonov1902-1909
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We initiate the rigorous study of the gerrymandering problem from the perspectives of parameterized and ο¬ne-grained complexity and provide asymptotically matching lower and upper bounds on its computational complexity. We prove that the problem is W[1]-hard parameterized by k + n and that it does not admit an f(n, k) mo(k) algorithm for any function f of k and n only, unless the Exponential Time Hypothesis (ETH) fails. Our lower bounds hold already for 2 parties. On the other hand, we give an algorithm that solves the problem for a constant number of parties in time (m + n)O(k). |
| Researcher Affiliation | Academia | 1Department of Computer Science, Royal Holloway, University of London, UK 2Department of Informatics, University of Bergen, Norway 3Department of Computer Science and Engineering, IIT Hyderabad, India |
| Pseudocode | No | The paper describes algorithms and proofs but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | This is a theoretical paper that does not use datasets for training or empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve dataset validation splits. |
| Hardware Specification | No | This is a theoretical paper, and thus no hardware specifications for running experiments are provided. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers needed to replicate empirical results. |
| Experiment Setup | No | This is a theoretical paper, and therefore, no experimental setup details like hyperparameters or training configurations are relevant or provided. |