Winner Robustness via Swap- and Shift-Bribery: Parameterized Counting Complexity and Experiments
Authors: Niclas Boehmer, Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |