论文标题
所有政治都是本地的：通过当地的公平重新划分
All Politics is Local: Redistricting via Local Fairness
论文作者
论文摘要
在本文中，我们建议将当地公平性的概念用于审核和对重新分配计划。鉴于重新划分计划，一个偏离人群平衡的连续区域，其中大多数个人具有相同的利益，而在其各自的地区中，大多数人都具有相同的利益；这样的人对如何制定重新划分计划有合理的投诉。没有偏离群体的重新划分计划称为本地公平。我们表明，审核给定的当地公平计划的问题是NP统计的。我们提出了一种MCMC审核方法以及对重新划分计划的排名。我们还提出了一种基于动态编程的算法，用于审核问题，以证明MCMC方法的功效。使用这些工具，我们在现实世界选举数据上测试本地公平性，表明确实可以找到几乎或完全公平的计划。此外，我们表明，在紧凑和现有的公平措施（例如各地区的竞争力或计划的股份）方面牺牲很少，可以制定此类计划。
In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complaint with how the redistricting plan was drawn. A redistricting plan with no deviating groups is called locally fair. We show that the problem of auditing a given plan for local fairness is NP-complete. We present an MCMC approach for auditing as well as ranking redistricting plans. We also present a dynamic programming based algorithm for the auditing problem that we use to demonstrate the efficacy of our MCMC approach. Using these tools, we test local fairness on real-world election data, showing that it is indeed possible to find plans that are almost or exactly locally fair. Further, we show that such plans can be generated while sacrificing very little in terms of compactness and existing fairness measures such as competitiveness of the districts or seat shares of the plans.
