# Putting a Compass on the Map of Elections

arXiv: https://arxiv.org/abs/2105.07815 · HTML: https://mlsanity.com/paper/2105.07815/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2021.

## Built on by (most cited first, 8 of 8)

- [Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning](https://mlsanity.com/paper/2205.07831/) (2022)
- [Learning How to Vote with Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks](https://mlsanity.com/paper/2410.16170/) (2024)
- [Drawing a Map of Elections](https://mlsanity.com/paper/2504.03809/) (2025)
- [Abductive and Contrastive Explanations for Scoring Rules in Voting](https://mlsanity.com/paper/2408.12927/) (2024)
- [A Map of Diverse Synthetic Stable Roommates Instances](https://mlsanity.com/paper/2208.04041/) (2022)
- [Fairness in the Multi-Secretary Problem](https://mlsanity.com/paper/2511.23097/) (2025)
- [Independence of Approximate Clones](https://mlsanity.com/paper/2601.20779/) (2026)
- [Axiom Satisfiability of Linear Rewards in Alignment](https://mlsanity.com/paper/2610.06892/) (2026)

## Abstract (arXiv)

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.