多党选举中拉票改划的非参数检测
Nonparametric Detection of Gerrymandering in Multiparty Elections
AI总结：
针对多党及每区候选人数不同的选举，本文提出两种基于非参数统计学习的拉票改划检测方法，并在17个FPTP国家的真实选举数据上验证。
摘要：
党派拉票改划，即出于政治利益操纵选区边界，是当代民主国家选举诚信面临的主要挑战之一。然而，现有的大多数检测党派拉票改划的方法都狭隘地针对全面竞争的两党选举，若存在更多政党或每个选区的候选人数量不同（美国以外许多基于相对多数制的选举制度就是如此），这些方法便会失效。我们提出两种基于非参数统计学习的方法，能够处理此类情形。多种方法的使用使所提解决方案在各自假设被违反时仍具稳健性。随后，我们利用来自17个采用FPTP制度的国家的全国及地方选举真实数据，对所提方法进行了测试。
英文摘要：
Partisan gerrymandering, i.e., manipulation of electoral district boundaries for political advantage, is one of the major challenges to election integrity in modern day democracies. Yet most of the existing methods for detecting partisan gerrymandering are narrowly tailored toward fully contested two-party elections, and fail if there are more parties or if the number of candidates per district varies (as is the case in many plurality-based electoral systems outside the United States). We propose two methods, based on nonparametric statistical learning, that are able to deal with such cases. The use of multiple methods makes the proposed solution robust against violation of their respective assumptions. We then test the proposed methods against real-life data from national and subnational elections in 17 countries employing the FPTP system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
