Perpetual Voting: Fairness in Long-Term Decision Making
Authors: Martin Lackner2103-2110
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 lackner@dbai.tuwien.ac.at |
| 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 first 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. |