Approval with Runoff

Authors: Théo Delemazure, Jérôme Lang, Jean-François Laslier, M. Remzi Sanver

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We analyse the outcome of these rules on one-dimensional Euclidean profiles (Section 5), and move on to applying the rules on real data (Section 6).
Researcher Affiliation Academia 1CNRS, Universit e Paris-Dauphine, Universit e PSL, LAMSADE 2CNRS, Paris School of Economics, Universit e PSL
Pseudocode No The paper provides mathematical definitions and formulas for the rules, but it does not include pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is released or available.
Open Datasets Yes We used approval ballot datasets from different sources: Datasets from several cities conducted during the 2017 French presidential election [Bouveret et al., 2019]... Two datasets of a poster competition held at the San Sebastian Summer School on Computational Social Choice7... 7Available on www.preflib.org
Dataset Splits No The paper mentions using datasets for analysis but does not specify any training, validation, or test dataset splits.
Hardware Specification No The paper does not provide any specific details about the hardware used to run its simulations or analyses.
Software Dependencies No The paper defines mathematical voting rules and their properties. It does not mention any specific software or libraries, along with their version numbers, that were used for implementation or analysis.
Experiment Setup Yes We sampled 20,000 voters and 1,000 candidates. Again, every voter approves candidates at distance d. For each d and α we compute the two finalists and observed their positions on the line. The first selected finalist is always the closest to the center.