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].
Poll-Confident Voters in Iterative Voting
Authors: Anaëlle Wilczynski2205-2212
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are given for testing the practical convergence of the dynamics and the quality of their outcomes. |
| Researcher Affiliation | Academia | Ana elle Wilczynski Universit e Paris-Dauphine, PSL, CNRS, LAMSADE, Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Margin rebalance on two candidates |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | We conduct experiments over 10,000 generated instances with 100 voters and 10 candidates, under impartial culture for the preferences and random Erd os and R enyi[1959] s graphs (see Fig. 2). |
| Dataset Splits | No | The paper describes generating instances for experiments but does not explicitly provide details about training/validation/test dataset splits in the conventional sense. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We examine unanimous thresholds of value 1, 5 and 10 and heterogeneous ones uniformly distributed over the voters with values in [1..5] or [1..10]. |