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.
@article{arxiv.2205.00492,
title = {Understanding Distance Measures Among Elections},
author = {Niclas Boehmer and Piotr Faliszewski and Rolf Niedermeier and Stanisław Szufa and Tomasz Wąs},
journal= {arXiv preprint arXiv:2205.00492},
year = {2022}
}