实证验证在立法选区重新划分模拟中的重要作用

IF 1.5 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Statistics and Public Policy Pub Date : 2020-01-01 DOI:10.1080/2330443x.2020.1791773
Benjamin Fifield, K. Imai, J. Kawahara, Christopher T. Kenny
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引用次数: 13

摘要

随着有关选举和选民的细粒度数据的可用性,选区重划模拟方法在立法机构通过选区重划计划和法院确定其合法性时发挥着越来越重要的作用。这些模拟方法的目的是产生所有重新划分计划的代表性样本,这些计划满足法定指导方针和要求,如邻近性、人口平价和紧凑性。如果根据党派公平指标,一个拟议的重新划分计划相对于这个样本构成了一个异常值,那么它可以被认为是不公正的。尽管它们的使用越来越多,但在经验验证模拟方法的准确性方面所做的努力还不够。我们采用了一种最新开发的计算方法,该方法可以有效地枚举所有可能的重新划分计划,并从这个群体中产生一个独立的样本。我们展示了这个算法扩展到一个有几百个地理单位的状态。最后,我们实证研究了现有的仿真方法如何在现实验证数据集上执行。
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The Essential Role of Empirical Validation in Legislative Redistricting Simulation
ABSTRACT As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines and requirements such as contiguity, population parity, and compactness. A proposed redistricting plan can be considered gerrymandered if it constitutes an outlier relative to this sample according to partisan fairness metrics. Despite their growing use, an insufficient effort has been made to empirically validate the accuracy of the simulation methods. We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans and yield an independent sample from this population. We show that this algorithm scales to a state with a couple of hundred geographical units. Finally, we empirically examine how existing simulation methods perform on realistic validation datasets.
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来源期刊
Statistics and Public Policy
Statistics and Public Policy SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
3.20
自引率
6.20%
发文量
13
审稿时长
32 weeks
期刊最新文献
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