Realistic Assumptions for Attacks on Elections
Authors: Zack Fitzsimmons
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical and empirical analysis are equally important methods to understand election attacks. I propose further study into modeling realistic election attacks and the advancement of the current state of empirical analysis of their hardness by using more advanced statistical techniques. This experimental approach fails to assess the significance of the predictor variables (e.g., voter distribution, size of the candidate and voter sets) with respect to the response (either time required or manipulation possible/not possible). If a more rigorous experimental design is used then stronger conclusions can be made. We will use experimental design techniques such as analysis of variance (ANOVA) to determine which values for our predictors results in a significant difference in the time required by a given algorithm. |
| Researcher Affiliation | Academia | Zack Fitzsimmons College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623, USA zmf6921@rit.edu |
| Pseudocode | No | The paper describes research concepts and future analytical approaches but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making the source code for the described work publicly available, nor does it provide any links to a code repository. |
| Open Datasets | Yes | Before AAAI-15 I intend on exploring partial singlepeakedness in the datasets containing partial votes found on PREFLIB (Mattei and Walsh 2013) |
| Dataset Splits | No | The paper discusses general experimental design techniques and statistical methods for future work but does not provide specific details on training, validation, or test dataset splits for any experiments conducted within this paper. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computational work or experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies or their version numbers required to replicate any part of the described work. |
| Experiment Setup | No | The paper discusses future research directions and general analytical approaches (e.g., ANOVA, regression models) but does not provide specific details on experimental setup, hyperparameters, or training configurations for any conducted experiments. |