Putting a Compass on the Map of Elections

TL;DR

This paper introduces four canonical extreme elections as a compass to interpret a visual map of elections, enhancing understanding of election similarities and revealing a new Mallows model variant that fits real-world data effectively.

Contribution

It provides a novel interpretation framework for election maps using canonical elections and introduces a new Mallows model variant that models real elections more accurately.

Findings

Canonical elections serve as a meaningful interpretative compass.

The new Mallows model variant captures real-life election data well.

Enhanced understanding of election similarities and structures.

Abstract

Recently, Szufa et al. [AAMAS 2020] presented a "map of elections" that visualizes a set of 800 elections generated from various statistical cultures. While similar elections are grouped together on this map, there is no obvious interpretation of the elections' positions. We provide such an interpretation by introducing four canonical "extreme" elections, acting as a compass on the map. We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. In effect, we find a new variant of the Mallows model and show that it captures real-life scenarios particularly well.