Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

*Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, StanisÅ‚aw Szufa*

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.

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