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 [1].
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. |