投票弹性和竞争力的计算方法
A Computational Approach to Measuring Vote Elasticity and Competitiveness
摘要：
最近对党派划分选区的关注浪潮，伴随着对全国各地管理政治选区重划的法律的完善或取代。几个州的改革努力中的一个共同点是纳入了竞争力指标，即根据地区一级的结果在多大程度上发挥作用或可能受到激烈竞争来评估地区规划的分数。在本文中，我们研究了几类竞争力指标的动机最近的改革建议，然后评估其潜在的结果在大合奏的选区计划在国会和州参议院的水平。这是越来越多的文献中使用MCMC技术的一部分，从应用统计到有效的重新划分备选方案的背景下的规划和标准。我们的实证分析集中在五个州-犹他州，佐治亚州，威斯康星州，弗吉尼亚州和马萨诸塞州-选择代表一系列的党派属性。我们强调特定情况下的困难，创造良好的竞争力指标，并表明，优化竞争力可以产生意想不到的后果对其他党派指标。这些结果证明了以下两点的重要性：（1）避免将详细的度量限制写入长期的宪法改革;（2）在每个选区重划周期中对真实的地理选举数据进行仔细的数学建模。
英文摘要：
The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states' reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested. In this paper, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states---Utah, Georgia, Wisconsin, Virginia, and Massachusetts---chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.
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