Comparing Ways of Obtaining Candidate Orderings from Approval Ballots
Authors: Théo Delemazure, Chris Dong, Dominik Peters, Magdalena Tydrichova
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude the paper by applying our rules to different datasets: French election surveys, on the votes of justices of the US Supreme Court, and on synthetic data. The simulations show how our rules differ, which perform best, and how they compare to rules that are based on taking rankings rather than approvals as input. |
| Researcher Affiliation | Academia | 1CNRS, LAMSADE, Universit e Paris Dauphine PSL 2Technical University of Munich 3Centrale Sup elec, Paris Saclay University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | for the 2017 [2022] dataset, we obtained approval preferences of 20 076 voters [1 379 voters] and preference rankings of 5 796 voters [412 voters] over 11 candidates [12 candidates]. ... The dataset is based on the opinions authored and joined by the justices, derived from the Supreme Court Database [Spaeth et al., 2023]. |
| Dataset Splits | No | The paper mentions using synthetic and real datasets for experiments but does not provide specific train/validation/test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for key software components or dependencies used in the experiments. |
| Experiment Setup | No | The paper mentions that for m up to about 12, they can find the best axes using a brute force approach and ILP encodings, described in the full version. However, it does not provide specific experimental setup details such as hyperparameters or system-level training settings in the main text. |