Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Iterative Calculus of Voting under Plurality
Authors: Fabricio Vasselai5208-5218
AAAI 2022 | Venue PDF | 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 EMAIL |
| 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)). |