Poster
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Niclas Boehmer · Robert Bredereck · Edith Elkind · Piotr Faliszewski · Stanisław Szufa
Hall J (level 1) #718
Keywords: [ vote distributions ] [ visualizing experimental results ] [ single-peaked elections ] [ Mallows model ]
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 develop a general and unified framework for learning the distribution of real-world preferences using the frequency matrices of established vote distributions.