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 Elections by Changing Voter Perceptions
Authors: Junlin Wu, Andrew Estornell, Lecheng Kong, Yevgeniy Vorobeychik
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that controlling elections in this model is, in general, NP-hard, whether issues are binary or real-valued. However, we demonstrate that critical to intractability is the diversity of opinions on issues exhibited by the voting public. When voter views lack diversity, and we can instead group them into a small number of categories for example, as a result of political polarization the election control problem can be solved in polynomial time in the number of issues and candidates for arbitrary scoring rules. |
| Researcher Affiliation | Academia | Junlin Wu , Andrew Estornell , Lecheng Kong and Yevgeniy Vorobeychik Washington University in St. Louis EMAIL |
| Pseudocode | No | The paper describes algorithmic approaches and complexity analysis, such as using Integer Linear Programming, but it does not contain any formal pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | This is a theoretical paper focusing on computational complexity. It does not use empirical datasets for training models or for any other purpose, and thus does not provide information about public access to a train dataset. |
| Dataset Splits | No | This is a theoretical paper. It does not mention or use validation datasets or specific data splits. |
| Hardware Specification | No | This is a theoretical paper and does not report on computational experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper focused on complexity analysis and mathematical proofs. It does not describe any computational implementation that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper that focuses on complexity analysis and theorems. It does not describe any empirical experiments or their setup, and therefore does not provide details like hyperparameters or training configurations. |