摘要翻译:
我们提出了三种新的不公正选区划分算法,它们结合了选民的空间分布,旨在构建不公正的、人口相等的、连通的选区。此外,我们建立了选民分布的格点模型,基于静电势的类比,以比较不同的不公正选区划分策略。由于我们的选民模型固有的概率人口波动,蒙特卡罗方法可以应用于通过我们的不公正选区划分算法构建的地区。通过Monte Carlo研究,我们量化了每种不公正划分算法的有效性,并且我们还认为不包括空间数据的不公正划分策略会导致(法律禁止的)高度分离的区域。在我们提出的三种算法中,两种是基于不同的反对派选民打包策略,第三种是基于遗传算法的算法不划分选区的新方法,它自动保证所有地区都是连接的。此外,我们使用我们的格选民模型来检验等周商检验的有效性,我们的结果为在现实世界的政治选区划分中实现紧凑性检验提供了进一步的定量支持。
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英文标题:
《Lattice Studies of Gerrymandering Strategies》
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作者:
Kyle Gatesman and James Unwin
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最新提交年份:
2018
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分类信息:
一级分类:Physics 物理学
二级分类:Physics and Society 物理学与社会
分类描述:Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体(人类或其他)的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统(如能源网、运输网络)的物理和工程。
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一级分类:Computer Science 计算机科学
二级分类:Computers and Society 计算机与社会
分类描述:Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类:Economics 经济学
二级分类:General Economics 一般经济学
分类描述:General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类:Quantitative Finance 数量金融学
二级分类:Economics 经济学
分类描述:q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学,包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要:
We propose three novel gerrymandering algorithms which incorporate the spatial distribution of voters with the aim of constructing gerrymandered, equal-population, connected districts. Moreover, we develop lattice models of voter distributions, based on analogies to electrostatic potentials, in order to compare different gerrymandering strategies. Due to the probabilistic population fluctuations inherent to our voter models, Monte Carlo methods can be applied to the districts constructed via our gerrymandering algorithms. Through Monte Carlo studies we quantify the effectiveness of each of our gerrymandering algorithms and we also argue that gerrymandering strategies which do not include spatial data lead to (legally prohibited) highly disconnected districts. Of the three algorithms we propose, two are based on different strategies for packing opposition voters, and the third is a new approach to algorithmic gerrymandering based on genetic algorithms, which automatically guarantees that all districts are connected. Furthermore, we use our lattice voter model to examine the effectiveness of isoperimetric quotient tests and our results provide further quantitative support for implementing compactness tests in real-world political redistricting.
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PDF链接:
https://arxiv.org/pdf/1808.02826