Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Authors: Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, Stanisław Szufa
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use the map of elections approach of Szufa et al. (AAMAS-2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its frequency matrix, which captures the probability that a given candidate appears in a given position in a sampled vote. We use these matrices to draw the skeleton map of distributions, evaluate its robustness, and analyze its properties. Finally, we develop a general and unified framework for learning the distribution of real-world preferences using the frequency matrices of established vote distributions. |
| Researcher Affiliation | Academia | Niclas Boehmer Algorithmics and Computational Complexity Technische Universität Berlin niclas.boehmer@tu-berlin.de Robert Bredereck TU Clausthal robert.bredereck@tu-clausthal.de Edith Elkind University of Oxford elkind@cs.ox.ac.uk Piotr Faliszewski AGH University faliszew@agh.edu.pl Stanisław Szufa AGH University Jagiellonian University szufa@agh.edu.pl |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The source code used for the experiments is available in a Git Hub repository1. 1github.com/Project-PRAGMA/Expected-Frequency-Matrices-Neur IPS-2022 |
| Open Datasets | Yes | We consider elections from the real-world datasets used by Boehmer et al. (2021b). They generated 15 elections with 10 candidates and 100 voters (with strict preferences) from each of the eleven different real-world election datasets (so, altogether, they generated 165 elections, most of them from Preflib (Mattei & Walsh, 2013)). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test splits for the datasets used. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or cloud resources) were mentioned for running experiments. |
| Software Dependencies | No | The paper mentions 'Python sklearn.manifold.MDS package' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Let Φ = {0, 0.05, 0.1, . . . , 1} be a set of normalized dispersion parameters that we will be using for Mallows-based distributions in this section. |