# Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist

arXiv: https://arxiv.org/abs/2401.14562 · HTML: https://mlsanity.com/paper/2401.14562/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [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)
- [What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting](https://mlsanity.com/paper/2508.06454/) (2025)
- [Fairness in the Multi-Secretary Problem](https://mlsanity.com/paper/2511.23097/) (2025)

## Abstract (arXiv)

The Mallows model is a popular distribution for ranked data. We empirically and theoretically analyze how the properties of rankings sampled from the Mallows model change when increasing the number of alternatives. We find that real-world data behaves differently than the Mallows model, yet is in line with its recent variant proposed by Boehmer et al. [2021]. As part of our study, we issue several warnings about using the model.