Optimal Strategies in Ranked Choice Voting

TL;DR

This paper investigates optimal strategic behaviors in Ranked Choice Voting, developing efficient algorithms and theoretical insights to influence election outcomes, demonstrated through analysis of the US 2024 Republican Primary data.

Contribution

It introduces novel algorithmic methods to identify optimal strategies in RCV and STV elections, overcoming computational challenges and analyzing strategy effectiveness under various information conditions.

Findings

Efficient algorithms for reducing election instances while preserving optimization accuracy.

Analysis of strategy effectiveness under perfect and imperfect polling data.

Application to US 2024 Republican Primary data revealing potential strategic boosts.

Abstract

Ranked Choice Voting (RCV) and Single Transferable Voting (STV) are widely valued; but are complex to understand due to intricate per-round vote transfers. Questions like determining how far a candidate is from winning or identifying effective election strategies are computationally challenging as minor changes in voter rankings can lead to significant ripple effects - for example, lending support to a losing candidate can prevent their votes from transferring to a more competitive opponent. We study optimal strategies - persuading voters to change their ballots or adding new voters - both algorithmically and theoretically. Algorithmically, we develop efficient methods to reduce election instances while maintaining optimization accuracy, effectively circumventing the computational complexity barrier. Theoretically, we analyze the effectiveness of strategies under both perfect and imperfect polling information. Our algorithmic approach applies to the ranked-choice polling data on the US 2024 Republican Primary, finding, for example, that several candidates would have been optimally served by boosting another candidate instead of themselves.