Measuring and Controlling Divisiveness in Rank Aggregation
This work addresses the challenge of quantifying disagreements in collective decision-making for populations using rank aggregation, representing an incremental advancement in understanding divisiveness.
The paper tackled the problem of identifying divisive issues in rank aggregation by analyzing properties of divisiveness measures and their relation to polarization, and it studied robustness under incomplete preferences and algorithms for control and manipulation of divisiveness.
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.