Iterative Calculus of Voting under Plurality
Authors: Fabricio Vasselai5208-5218
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented ICV in Python 3.7.3. First, 10,000 simulations were performed using Multinomial pivotal probabilities. Then, their pseudo-random seeds were used to repeat the simulations twice, each time using Poisson or Skellam probabilities for a total of 30,000 simulations. |
| Researcher Affiliation | Academia | Fabricio Vasselai University of Michigan Ann Arbor, MI, USA vasselai@umich.edu |
| Pseudocode | Yes | Algorithm 1: Listing pivotal outcomes Vα and Vβ; Algorithm 2: Joint calculation of Poisson pivotal probs. |
| Open Source Code | Yes | Online Appendix and all code necessary to replicate the paper can be found at https://github.com/vasselai/aaai22-icv-plurality. |
| Open Datasets | No | The paper uses simulated data for its experiments: 'Electors candidate utilities were drawn from Beta distributions, with varying parameters: ui Beta(Uniform(0.1, 5.0), Uniform(0.1, 5.0)).' It does not mention using a publicly available or open dataset. |
| Dataset Splits | No | The paper describes running 10,000 simulations but does not specify any training, validation, or test dataset splits, as it generates synthetic data for its simulations rather than using a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations (e.g., CPU, GPU, or memory specifications). |
| Software Dependencies | Yes | We implemented ICV in Python 3.7.3. |
| Experiment Setup | Yes | In those simulations, model hyperparameters were specified as: λ Uniform(2, 100) and m Uniform(3, 6). Electors candidate utilities were drawn from Beta distributions, with varying parameters: ui Beta(Uniform(0.1, 5.0), Uniform(0.1, 5.0)). |