Manipulating Elections by Changing Voter Perceptions
Authors: Junlin Wu, Andrew Estornell, Lecheng Kong, Yevgeniy Vorobeychik
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 {junlin.wu, aestornell, jerry.kong, yvorobeychik}@wustl.edu |
| 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. |