Discovering Consistent Subelections

TL;DR

This paper introduces methods to discover hidden, large, and consistent subelections within ordinal elections, revealing underlying voter preferences and concepts through computational analysis of synthetic and real data.

Contribution

It presents algorithms for identifying interesting subelections based on identity, antagonism, and clones, and demonstrates their effectiveness on real and synthetic datasets.

Findings

Hidden subelections reveal relevant voter concepts

Algorithms effectively identify consistent subelections

Analysis of real data uncovers meaningful preference patterns

Abstract

We show how hidden interesting subelections can be discovered in ordinal elections. An interesting subelection consists of a reasonably large set of voters and a reasonably large set of candidates such that the former have a consistent opinion about the latter. Consistency may take various forms but we focus on three: Identity (all selected voters rank all selected candidates the same way), antagonism (half of the selected voters rank candidates in some order and the other half in the reverse order), and clones (all selected voters rank all selected candidates contiguously in the original election). We first study the computation of such hidden subelections. Second, we analyze synthetic and real-life data, and find that identifying hidden consistent subelections allows us to uncover some relevant concepts.