Election with Bribe-Effect Uncertainty: A Dichotomy Result
Authors: Lin Chen, Lei Xu, Shouhuai Xu, Zhimin Gao, Weidong Shi
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Texas Tech University, TX, USA 2Department of Computer Science, University of Texas Rio Grande Valley, TX, USA 3Department of Computer Science, University of Texas at San Antonio, TX, USA 4Department of Computer Science, Auburn University at Montgomery, AL, USA 5Department of Computer Science, University of Houston, TX, USA |
| Pseudocode | No | The paper describes algorithms and proofs in prose and mathematical notation but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a PDF file ('https://github.com/IJCAIpaper/willingness/blob/master/Fullversion.pdf') which is a full version of the paper, not source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any hardware specifications, as it focuses on theoretical analysis rather than empirical experimentation. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers, as it is a theoretical work without an implementation. |
| Experiment Setup | No | The paper does not provide details on an experimental setup, such as hyperparameters or training configurations, as no empirical experiments are described. |