论文标题
预期频率矩阵：计算，几何学和偏好学习
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
论文作者
论文摘要
我们使用Szufa等人的``选举地图''方法。 （AAMAS-2020）分析了几个著名的投票分布。对于每个人，我们给出了一个明确的公式或用于计算其频率矩阵的有效算法，该算法捕获了给定候选人在采样的投票中出现在给定位置的概率。我们使用这些矩阵来绘制分布的``骨架图''，评估其稳健性并分析其特性。最后，我们开发了一个通用和统一的框架，用于使用既定的投票分布的频率矩阵来学习现实世界偏好的分布。
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.
