# Measuring and Controlling Divisiveness in Rank Aggregation

arXiv: https://arxiv.org/abs/2306.08511 · HTML: https://mlsanity.com/paper/2306.08511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [Designing Recommender Systems to Depolarize](https://mlsanity.com/paper/2107.04953/) (2021)
- [Adversarial Perturbations of Opinion Dynamics in Networks](https://mlsanity.com/paper/2003.07010/) (2020)
- [Safe Voting: Resilience to Abstention and Sybils](https://mlsanity.com/paper/2001.05271/) (2020)
- [Quantifying consensus of rankings based on q-support patterns](https://mlsanity.com/paper/1905.12966/) (2019)
- [Manipulating Elections by Changing Voter Perceptions](https://mlsanity.com/paper/2205.00102/) (2022)

## Built on by (most cited first, 1 of 1)

- [Selecting the Most Conflicting Pair of Candidates](https://mlsanity.com/paper/2405.05870/) (2024)

## Abstract (arXiv)

In rank aggregation, members of a population rank issues to decide which are collectively preferred. We focus instead on identifying divisive issues that express disagreements among the preferences of individuals. We analyse the properties of our divisiveness measures and their relation to existing notions of polarisation. We also study their robustness under incomplete preferences and algorithms for control and manipulation of divisiveness. Our results advance our understanding of how to quantify disagreements in collective decision-making.