Proportional Ranking in Primary Elections: A Case Study

TL;DR

This study evaluates various algorithmic methods for primary elections, demonstrating that some, like Sequential Proportional Approval and Phragmen, achieve better proportional representation than traditional Approval voting.

Contribution

The paper compares six primary election algorithms using real-world data, highlighting methods that improve proportional ranking over standard approaches.

Findings

Sequential Proportional Approval outperforms Approval voting in proportionality.

Phragmen method provides better voter group representation.

Traditional Approval voting is less effective for proportional representation.

Abstract

Many democratic political parties hold primary elections, which nicely reflects their democratic nature and promote, among other things, the democratic value of inclusiveness. However, the methods currently used for holding such primary elections may not be the most suitable, especially if some form of proportional ranking is desired. In this paper, we compare different algorithmic methods for holding primaries (i.e., different aggregation methods for voters' ballots), by evaluating the degree of proportional ranking that is achieved by each of them using real-world data. In particular, we compare six different algorithms by analyzing real-world data from a recent primary election conducted by the Israeli Democratit party. Technically, we analyze unique voter data and evaluate the proportionality achieved by means of cluster analysis, aiming at pinpointing the representation that is granted to different voter groups under each of the algorithmic methods considered. Our finding suggest that, contrary to the most-prominent primaries algorithm used (i.e., Approval), other methods such as Sequential Proportional Approval or Phragmen can bring about better proportional ranking and thus may be better suited for primary elections in practice.