政治地理的不对称性与选区的紧密性：一个偏差的计算分析
Asymmetry in Political Geography and Compactness in Districting: a Computational Analysis of Bias
摘要：
我们调查了十个不同州的党派分布，以阐明选票如何转化为赢得的席位和其他指标。马尔可夫链模拟考虑到党派分布同意令人惊讶的是，一个简单的模型，只涵盖平等的投票人口加权分布的选区结果不包含空间信息。我们发现民主党赢得的选区比共和党少，但这样做与大多数的不对称。这种倾斜解释了共和党对州议会和国会席位的持续控制，即使在一些民主党在全州范围内占多数的州。尽管总体结果显示共和党的优势，在许多国家的基础上，从模拟的平均结果，涵盖了许多随机方案，模拟产生了广泛的指标，这表明偏见可以最大限度地减少更好地选择分区计划与低价值的效率差距，而不是通过选择计划的平均值附近的合奏随机模拟。我们研究县分裂的限制，以实现更高的紧凑性和调查的政策，需要凝聚力的利益社区筛选出最明显的gerrymanders。最大限度地减少县分裂并不一定会减少党派偏见，除了宾夕法尼亚州，限制县分裂似乎会减少偏见。
英文摘要：
We investigate the distribution of partisanship in a cross-section of ten diverse States to elucidate how votes translate into seats won and other metrics. Markov chain simulations taking into account partisanship distribution agree surprisingly well with a simple model covering only equal voting population-weighted distributions of precinct results containing no spatial information. We find asymmetries where Democrats win fewer precincts than Republicans but do so with large marjorities. This skew accounts for persistent Republican control of State Legislatures and Congressional seats even in some states with statewide vote majorities for Democrats. Despite overall results showing Republican advantages in many states based on mean results from simulations covering many random scenarios, the simulations yield a wide range in metrics, suggesting bias can be minimized better by selecting districting plans with low values for efficiency gap than by selecting plans with values near the means for the ensemble of random simulations. We examine constraints on county splits to achieve higher compactness and investigate policies requiring cohesiveness for communities of interest as to screen out the most obvious gerrymanders. Minimizing county splits does not necessarily reduce partisan bias, except for Pennsylvania, where limiting county splits appears to reduce bias.
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