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.

翻译：在排名聚合中，群体成员对议题进行排序以决定集体偏好。本研究转而聚焦于识别那些体现个体间偏好分歧的分裂性议题。我们分析了分裂性测度的性质及其与现有极化概念的关系，并研究了其在偏好不完备条件下的鲁棒性，以及针对分裂性的控制与操纵算法。研究结果加深了我们对如何量化集体决策中分歧的理解。