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..
Winner Robustness via Swap- and Shift-Bribery: Parameterized Counting Complexity and Experiments
Authors: Niclas Boehmer, Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Facing several computational hardness results, using sampling we show experimentally that SWAP-BRIBERY offers a new approach to the robustness analysis of elections. We also present experiments, where we use #SWAP-BRIBERY to evaluate the robustness of election results. |
| Researcher Affiliation | Academia | 1Algorithmics and Computational Complexity, TU Berlin, Germany 2Humboldt-Universit at zu Berlin, Germany 3AGH University, Poland |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. It describes theoretical complexity results and experimental procedures in natural language. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology it describes, nor does it state that such code is released or available. |
| Open Datasets | Yes | We used a dataset of 800 elections, each with 10 candidates and 100 voters, prepared by Szufa et al. [2020]. |
| Dataset Splits | No | The paper describes a sampling procedure for estimation (e.g., "sampled 500 elections at this distance"), rather than typical training, validation, and test splits for a machine learning model, hence specific split information for these purposes is not applicable or provided. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | for each normalized swap distance r {0.05, 0.1, . . . , 1} we sampled 500 elections at this distance and for each candidate recorded the proportion of elections where he or she won (see our full version for a detailed description of the sampling procedure). For these two elections, we estimated PE,c(r) for r {0.0125, 0.025, . . . , 0.5} using 10 000 samples in each case. |