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 no- tions 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.

Reference

Rachael Colley, Umberto Grandi, César Hidalgo, Mariana Macedo, and Carlos Navarrete, “Measuring and Controlling Divisiveness in Rank Aggregation”, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), Edith Elkind (ed.), 2023.