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..
Determining Winners in Elections with Absent Votes
Authors: Qishen Han, Amelie Marian, Lirong Xia
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the WAV problem is NP-complete for single transferable vote, Maximin, and Copeland, and propose a special case of positional scoring rule such that the problem can be computed in polynomial time. |
| Researcher Affiliation | Academia | Qishen Han1 , Am elie Marian2 , Lirong Xia1 1Rensselaer Polytechnic Institute 2Rutgers University |
| Pseudocode | No | The paper describes algorithms conceptually through text and mathematical notation, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology or a direct link to a code repository. It only links to a full version of the paper on arXiv. |
| Open Datasets | No | This paper is theoretical and focuses on computational complexity. It does not involve experimental training on datasets, nor does it mention any publicly available datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets. Therefore, it does not specify dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not involve empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not involve empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not involve empirical experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations. |