Related papers: Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning

Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning

URL: http://arxiv.org/abs/2205.07831v1
Date: Mon, 16 May 2022 17:40:22 GMT
Title: Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Authors: Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, Stanis{\l}aw Szufa
Abstract summary: We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. We draw the "skeleton map" of distributions, evaluate its robustness, and analyze its properties.
Score: 58.23459346724491
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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. We further use them to identify the nature of several real-world elections.

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