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..
The Complexity of Subelection Isomorphism Problems
Authors: Piotr Faliszewski, Krzysztof Sornat, Stanisław Szufa4991-4998
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using our problems in experiments, we provide some insights into the nature of several statistical models of elections.In this section we use the MAX. COMMON VOTER-SUBELECTION problem to analyze similarity between elections generated from various statistical models. |
| Researcher Affiliation | Academia | 1AGH University, Krak ow, Poland 2Jagiellonian University, Krak ow, Poland |
| Pseudocode | No | The paper states that a formal ILP formulation is available in the full version, but it does not include pseudocode or a clearly labeled algorithm block in the main text. |
| Open Source Code | Yes | The source code used for the experiments is available in a Git Hub repository1. 1https://github.com/Project-PRAGMA/Subelections-AAAI2022 |
| Open Datasets | No | The paper describes generating its own datasets using various statistical models ('For each scenario and each two of the above-described models, we have generated 1000 pairs of elections'), but it does not provide concrete access information (link, DOI, repository, or citation) for these generated datasets to be publicly available. |
| Dataset Splits | No | The paper describes generating elections and analyzing their similarity, but it does not specify training, validation, or test dataset splits, as the experiments do not involve model training. |
| Hardware Specification | Yes | We ran CPLEX on a single thread (Intel(R) Xeon(R) Platinum 8280 CPU @ 2.70GH) of a 448 thread machine with 6TB of RAM. |
| Software Dependencies | No | The paper mentions using 'the CPLEX ILP solver' but does not specify its version number, nor does it list any other software components with version numbers. |
| Experiment Setup | Yes | We consider elections with 4, 7, or 10 candidates and with 50 voters. For each scenario and each two of the above-described models, we have generated 1000 pairs of elections (for urn elections, we used α {0.1, 0.5} and for the Mallows model, we used norm-φ {1/3, 2/3}). |