Motivated by putting empirical work based on (synthetic) election data on a more solid mathematical basis, we analyze six distances among elections, including, e.g., the challenging-to-compute but very precise swap distance and the distance used to form the so-called map of elections. Among the six, the latter seems to strike the best balance between its computational complexity and expressiveness.

翻译：通过将基于(合成的)选举数据的经验性工作建立在更坚实的数学基础上,我们分析了选举之间的六条距离,包括具有挑战性的、但非常精确的互换距离和用来构成所谓的选举地图的距离。 在六条距离中,后者似乎在计算复杂性和表达性之间取得了最佳平衡。