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..
Perpetual Voting: Fairness in Long-Term Decision Making
Authors: Martin Lackner2103-2110
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper explores the proposed voting rules via an axiomatic analysis as well as a quantitative evaluation by computer simulations. |
| Researcher Affiliation | Academia | Martin Lackner TU Wien Vienna, Austria EMAIL |
| Pseudocode | No | The paper describes the rules and their calculations in paragraph form and mathematical notation, but does not include any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | The Python code used for these experiments can be found at https://github.com/martinlackner/perpetual. |
| Open Datasets | No | The paper describes generating synthetic data for simulations: 'We generate voters and alternatives in a two-dimensional Euclidean space, similar to the setup used by Elkind et al. (2017).' It does not refer to a publicly available dataset with specific access information. |
| Dataset Splits | No | The paper conducts numerical simulations over '10,000 instances' of generated data but does not describe conventional train/validation/test dataset splits. The 'instances' refer to distinct simulation runs rather than a partitioned dataset. |
| Hardware Specification | No | The paper mentions 'computer simulations' but does not provide any specific details about the hardware used (e.g., CPU, GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper states 'The Python code used for these experiments can be found at https://github.com/martinlackner/perpetual.' However, it only mentions 'Python' without specifying its version or any other software libraries with their version numbers. |
| Experiment Setup | Yes | We consider a set of 20 voters which decide upon 20 decision instances, i.e., we have 20-decision sequences. For each decision 5 alternatives are available these differ from round to round. ... Voters are split in two groups and are placed on the 2d plane by a bivariate normal distribution. For the ο¬rst group (6 voters) both xand y-coordinates are independently drawn from N( 0.5, 0.2); for the second group (14 voters) xand y-coordinates are from N(0.5, 0.2). ... Alternatives are distributed uniformly in the rectangle [ 1, 1] [ 1, 1]. Voters approve all alternatives that have a distance of at most 1.5 times the distance to the closest alternative. |