Colley, Rachael, Grandi, UmbertoORCID: https://orcid.org/0000-0002-1908-5142, Hidalgo, Cesar AugustoORCID: https://orcid.org/0000-0002-6977-9492, Macedo, Mariana and Navarrete Lizama, Carlos Camilo (2023) Measuring and Controlling Divisiveness in Rank Aggregation. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23). pp. 2616-2623.

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.