文章基本信息

标题：The Essential Role of Empirical Validation in Legislative Redistricting Simulation
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作者：Benjamin Fifield ; Kosuke Imai ; Jun Kawahara 等
期刊名称：Statistics and Public Policy
电子版ISSN：2330-443X
出版年度：2020
卷号：7
期号：1
页码：1-18
DOI：10.1080/2330443X.2020.1791773
出版社：Taylor and Francis Ltd
摘要：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.
关键词：Enumeration ; Gerrymandering ; Graph partition ; Markov chain Monte Carlo ; Redistricting ; Zero-suppressed binary decision diagram