Proportional Representation in Rank Aggregation

Abstract

In rank aggregation, the task is to aggregate multiple weighted input rankings into a single output ranking. While numerous methods, so-called social welfare functions (SWFs), have been suggested for this problem, all of the classical SWFs tend to be majoritarian and are thus not acceptable when a proportional ranking is required. Motivated by this observation, we design SWFs that guarantee that every input ranking is proportionally represented by the output ranking. Specifically, our central fairness condition requires that the number of pairwise comparisons between candidates on which an input ranking and the output ranking agree is at least proportional to the weight of the input ranking. As our main contribution, we present a simple SWF called the Proportional Sequential Borda rule which satisfies this condition. Moreover, we introduce a more involved variant of this rule, the Flow-adjusting Borda rule, which satisfies a stronger fairness condition that applies to arbitrary groups of rankings. Many of our axioms and techniques are inspired by results in approval-based committee voting and participatory budgeting, where the concept of proportional representation has been studied in depth.