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.

翻译：排序选择投票（RCV）在美国选举中的应用正在扩大，但持续面临复杂性、策略操纵和选票耗尽等方面的批评。我们基于真实选举数据，在三种不同情境下对这些关切进行了实证检验：纽约市2021年民主党初选（54场选举）、阿拉斯加2024年全州性初选融合选举（52场选举）以及波特兰2024年多席位市议会选举（4场选举）。我们的算法方法通过缩减选举实例规模（通过候选人淘汰）规避了计算复杂性障碍。研究结果表明，尽管RCV具有复杂的多轮流程和理论上的脆弱性，但在实践中始终展现出简单透明的动态特性，其可解释性与简单多数选举高度相似。采用RCV后，相较于之前的简单多数选举，竞争性显著增强——纽约市的平均胜选优势下降9.2个百分点，阿拉斯加下降11.4个百分点。实证数据显示，复杂的选票追加策略并不比简单策略更有效，且选票耗尽影响甚微，仅在110场选举中的3场改变了结果。这些发现表明，RCV在带来可量化民主效益的同时，在实践中对选票追加操纵具有鲁棒性，对选票耗尽效应具有抵抗力，并保持了透明的竞争动态。该计算框架为选举管理者和研究者提供了选举当晚即时分析的工具，有助于围绕选举动态展开更清晰的讨论。