论文标题
经验验证在立法重新划分模拟中的基本作用
The Essential Role of Empirical Validation in Legislative Redistricting Simulation
论文作者
论文摘要
随着有关选举和选民的详细数据，当立法机关采用重新划分计划和法院确定其合法性时，重新划分模拟方法起着越来越重要的作用。这些仿真方法旨在产生所有重新划分计划的代表性样本，这些计划满足法定准则和要求，例如连续性，人口平等和紧凑性。根据党派公平指标，如果构成与该样本的异常值，则提议的重新划分计划可以被认为是纠缠的。尽管使用了日益增长的使用，但仍在努力验证模拟方法的准确性。我们采用了最近开发的计算方法，该方法可以有效地列举所有可能的重新划分计划，并从该人群中产生独立的统一样本。我们表明，该算法扩展到具有数百个地理单元的状态。最后，我们从经验上检查现有模拟方法如何在现实验证数据集上执行。
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 uniform 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 data sets.
