Understanding Distance Measures Among Elections
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2022.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 2.
5 papers.
- 2022Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learningcited by 5
- 2025Drawing a Map of Electionscited by 4
- 2025Diversity of Structured Domains via k-Kemeny Scorescited by 2
- 2022A Map of Diverse Synthetic Stable Roommates Instancescited by 1
- 2023Eliciting Kemeny Rankingscited by 0
- 2025Diversity of Structured Domains via k-Kemeny Scores19 Sep 2025
- 2025Drawing a Map of Elections4 Apr 2025
- 2023Eliciting Kemeny Rankings18 Dec 2023
- 2022A Map of Diverse Synthetic Stable Roommates Instances8 Aug 2022
- 2022Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning16 May 2022