An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections
Authors: Michał T. Godziszewski, Paweł Batko, Piotr Skowron, Piotr Faliszewski5448-5455
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider two main issues: First, we ask for the complexity of computing election results. Second, we evaluate election outcomes experimentally, following the visualization technique of Elkind et al. (2017). |
| Researcher Affiliation | Academia | 1 University of Warsaw, Poland, 2 AGH University, Krak ow, Poland |
| Pseudocode | No | The paper describes algorithms and constructions (e.g., for NP-hardness proofs) but does not present any formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper mentions "We used implementations from the abcvoting library (https://github.com/martinlackner/abcvoting)". This is a reference to a third-party library used by the authors, not a release of their own source code for the work described in this paper. |
| Open Datasets | No | The paper describes generating synthetic data: "To draw a histogram for a given voting rule and models of generating agents points and radii, we proceed as follows. First, we generate 2000 elections with 100 candidates and 100 voters each." This is data generation, not the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper generates synthetic data for its experiments but does not mention any traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using "the abcvoting library (https://github.com/martinlackner/abcvoting)" but does not specify a version number for this library or any other software dependencies. |
| Experiment Setup | Yes | To draw a histogram for a given voting rule and models of generating agent s points and radii, we proceed as follows. First, we generate 2000 elections with 100 candidates and 100 voters each. Then we compute their winning committees of size 10.3 Next, we consider the [ 3, 3] [ 3, 3] square partitioned into cells of size 0.05 0.05 and for each cell we count how many members of the winning committees fall there. |