Measuring and Controlling Divisiveness in Rank Aggregation
Abstract
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.
Cite
Text
Colley et al. "Measuring and Controlling Divisiveness in Rank Aggregation." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/291Markdown
[Colley et al. "Measuring and Controlling Divisiveness in Rank Aggregation." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/colley2023ijcai-measuring-a/) doi:10.24963/IJCAI.2023/291BibTeX
@inproceedings{colley2023ijcai-measuring-a,
title = {{Measuring and Controlling Divisiveness in Rank Aggregation}},
author = {Colley, Rachael and Grandi, Umberto and Hidalgo, César A. and Macedo, Mariana and Navarrete, Carlos},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {2616-2623},
doi = {10.24963/IJCAI.2023/291},
url = {https://mlanthology.org/ijcai/2023/colley2023ijcai-measuring-a/}
}