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..
Approval-Based Elections and Distortion of Voting Rules
Authors: Grzegorz Pierczyński, Piotr Skowron
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend the idea of distortion to approvalbased preferences. First, we compute the distortion of Approval Voting. Second, we introduce the concept of acceptability-based distortion the main idea behind is that the optimal candidate is the one that is acceptable to most voters. We determine acceptability-distortion for a number of rules, including Plurality, Borda, k-Approval, Veto, Copeland, Ranked Pairs, the Schulze s method, and STV. |
| Researcher Affiliation | Academia | Grzegorz Pierczynski , Piotr Skowron University of Warsaw, Warsaw, Poland EMAIL, EMAIL |
| Pseudocode | No | The paper describes voting rules in text, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper references a technical report on arXiv, but does not provide a direct link to open-source code for the described methodology. |
| Open Datasets | No | The paper operates within a theoretical metric model (e.g., E1 for one-dimensional Euclidean spaces) rather than using real-world datasets that would require training splits. No dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data, thus no training/validation/test splits are mentioned or provided. |
| Hardware Specification | No | The paper is theoretical and does not discuss any hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or training configurations. |