Simpler Than You Think: The Practical Dynamics of Ranked Choice Voting

2026-02-15

AI Summary

This study addresses longstanding concerns that ranked-choice voting (RCV) is overly complex, susceptible to strategic manipulation, and vulnerable to ballot exhaustion, yet lacks systematic empirical validation using real-world election data. Analyzing 110 actual RCV elections from New York City, Alaska, and Portland, the authors introduce a candidate-elimination-based instance reduction algorithm and a multi-round simulation framework to evaluate RCV’s practical performance across diverse, large-scale settings. The findings reveal that RCV operates dynamically, concisely, and transparently in practice, significantly enhancing electoral competitiveness by reducing average victory margins by 9.2–11.4 percentage points. Strategic ballot filling conferred no significant advantage, and ballot exhaustion altered outcomes in only three contests, thereby effectively bridging the perceived gap between theoretical complexity and operational robustness.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Humans and AI: Voting
• Machine Learning: Learning Preferences or Rankings

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City's 2021 Democratic primaries (54 races), Alaska's 2024 primary-infused statewide elections (52 races), and Portland's 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.

Problem

• Ranked Choice Voting
• ballot exhaustion
• strategic manipulation
• electoral complexity
• voting systems

Innovation

• Ranked Choice Voting
• algorithmic simplification
• ballot exhaustion
• strategic manipulation
• computational framework