区分党派不公正选区划分的比较研究
A Comparison of Metrics for the Identification of Partisan Gerrymandering
摘要：
目前，还没有有效的标准化方法来确定是否存在党派不公正划分选区的行为。一个相对较新提出的识别方法是集成分析。这是通过生成一个大的投票计划的中性集合，然后通过使用数学度量将给定的投票计划与集合进行比较来完成的。在本文中，我们调查了七个最常见的数学指标用于此识别过程中，这是效率差距，加权效率差距与权重2，相对效率差距与权重1，相对效率差距与权重2，平均值中位数得分，党派偏见，和declaration。我们定义并讨论所有这些。分析是通过对十次选举进行整体分析来完成的，每个度量都确定每个度量是否将每个选举确定为选区划分。所有这些选举都在文献中进行了研究，因此我们将这些结果与指标发现的结果进行比较，以评估准确性。我们发现，虽然平均中位数得分和党派偏见在所有七个指标中最适用，但它们也是最不准确的。其余五个指标之间没有发现显著差异，因此我们的最终建议是使用效率差距的四个变量之一，如果适用，则使用decompression，如果不适用，则使用平均-中位数得分。党派偏见是所有测试指标中最不准确的。
英文摘要：
Currently, there is currently no effective, standardized way to identify the presence of partisan gerrymandering. A relatively newly proposed method of identification is ensemble analysis. This is done by generating a large neutral ensemble of voting plans, then comparing the given voting plan against the ensemble through the use of a mathematical metric. In this paper, we survey seven of the most common mathematical metrics used for this identification process, which are the efficiency gap, weighted efficiency gap with weight 2, relative efficiency gap with weight 1, relative efficiency gap with weight 2, mean-median score, partisan bias, and declination. We define and discuss all of these. The analysis is done by performing ensemble analysis on ten elections with each metric to determine whether each metric determines each election as gerrymandered. All ten of these elections have been studied in the literature, so we compare those results to the results found by the metrics to assess accuracy. We show that, while the mean-median score and partisan bias are the most applicable across all seven metrics, they are also the most inaccurate. No significant difference was detected between the remaining five metrics, so our final recommendation is to use one of the four variants of the efficiency gap or the declination if applicable, and the mean-median score if not. The partisan bias was the least accurate of all the metrics tested.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
