对系统性选举违规行为的统计检测
Statistical detection of systematic election irregularities
摘要：
民主社会是建立在自由和公平选举的原则基础上的，即每个公民的选票都应该平等。全国选举可以被视为大规模的社会实验，人们通常被分成大量的选区，并根据自己的喜好投票。大量抽样意味着对投票结果的某些统计结果，可用于查明选举中的违规行为。使用一个合适的数据崩溃，我们发现，所谓的欺诈选举的选票分布显示了一个峰度的数百倍以上的正常选举的某些水平的数据汇总。作为一个例子，我们表明，在最近的俄罗斯选举中报告的违规行为确实很好地解释了系统的选票填充和开发一个参数模型量化到何种程度的欺诈机制存在。我们表明，如果在选举中存在特定的统计特性，结果并不代表人民的意愿。我们制定了一个参数测试检测这些统计特性的选举结果。值得注意的是，无论数据分辨率如何，这一技术都产生了类似的结果，因此可以进行跨国比较。
英文摘要：
Democratic societies are built around the principle of free and fair elections, that each citizen's vote should count equal. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies certain statistical consequences for the polling results which can be used to identify election irregularities. Using a suitable data collapse, we find that vote distributions of elections with alleged fraud show a kurtosis of hundred times more than normal elections on certain levels of data aggregation. As an example we show that reported irregularities in recent Russian elections are indeed well explained by systematic ballot stuffing and develop a parametric model quantifying to which extent fraudulent mechanisms are present. We show that if specific statistical properties are present in an election, the results do not represent the will of the people. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces similar outcomes irrespective of the data resolution and thus allows for cross-country comparisons.
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