The Complexity of Subelection Isomorphism Problems
Authors: Piotr Faliszewski, Krzysztof Sornat, Stanisław Szufa4991-4998
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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}). |