A Parameterized Perspective on Protecting Elections
Authors: Palash Dey, Neeldhara Misra, Swaprava Nath, Garima Shakya
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct an empirical study to understand how simple defending strategies perform for two such statistical voter generation models. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology, Kharagpur, India 2Indian Institute of Technology, Gandhinagar, India 3Indian Institute of Technology, Kanpur, India |
| Pseudocode | No | The paper describes algorithms (GREEDY 1 and GREEDY 2) in natural language but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We generate 1000 preference profiles over these alternatives for n = 12000, where each vote is picked uniformly at random from the set of all possible strict preference orders over m alternatives. |
| Dataset Splits | No | The paper describes synthetic data generation but does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | Fix m = 5. We generate 1000 preference profiles over these alternatives for n = 12000, where each vote is picked uniformly at random from the set of all possible strict preference orders over m alternatives. The x-axis shows different values of kd and we fix ka = 12 kd. |