Measuring and Controlling Divisiveness in Rank Aggregation

Résumé

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.

Référence

Rachael Colley, Umberto Grandi, César Hidalgo, Mariana Macedo et Carlos Navarrete, « Measuring and Controlling Divisiveness in Rank Aggregation », dans Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), sous la direction de Edith Elkind, 2023.