Measuring and Controlling Divisiveness in Rank Aggregation

Rank aggregation is the problem of ordering a set of issues according to a set of individual rankings given as input. This problem has been studied extensively in computational social choice (see, e.g., Brandt et al. 2016) when the rankings are assumed to represent human preferences over, for example, candidates in a political election, projects to be funded, or more generally alternative proposals. The most common approach in this literature is to find normative desiderata for the aggregation process, including computational requirements such as the existence of tractable algorithms for its calculation and characterisations of the aggregators that satisfy them. Rank aggregation also has a wide spectrum of applications from metasearch engines [Dwork et al., 2001] to bioinformatics