民主选举中竞争的普适统计规律
Universal Statistics of Competition in Democratic Elections
AI总结：
本研究提出无参数投票模型，发现民主选举胜选差距经投票率标化后存在跨国家、跨规模的普适统计规律，可用于识别选举舞弊，揭示选举结果的竞争属性。
摘要：
民主国家的公职选举是集体决策的大规模实例。作为一个存在大量智能体间相互作用的复杂系统，我们可以预期会出现独立于微观细节的普适宏观模式。尽管已有实证选举数据，但这种适用于所有规模、国家和选举的普适性尚未被观测到。本研究中，我们提出一个无参数投票模型，并通过解析证明，胜选差距的分布由选民投票率的分布驱动，而一个取决于差距和投票率的标化度量会产生稳健的普适性。我们利用来自34个国家、跨越数十年、涵盖不同选举规模的实证选举数据证明了这一点。与模型预测及普适性的偏离表明可能存在选举舞弊行为。我们认为，这种普适性是体现选举结果竞争属性的一个典型事实。
英文摘要：
Elections for public offices in democratic nations are large-scale examples of collective decision-making. As a complex system with a multitude of interactions among agents, we can anticipate that universal macroscopic patterns could emerge independent of microscopic details. Despite the availability of empirical election data, such universality, valid at all scales, countries, and elections, has not yet been observed. In this work, we propose a parameter-free voting model and analytically show that the distribution of the victory margin is driven by that of the voter turnout, and a scaled measure depending on margin and turnout leads to a robust universality. This is demonstrated using empirical election data from $34$ countries, spanning multiple decades and electoral scales. The deviations from the model predictions and universality indicate possible electoral malpractices. We argue that this universality is a stylized fact indicating the competitive nature of electoral outcomes.
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