没有代表就没有阶层

Gerdus Benade, Paul Gölz, A. Procaccia
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引用次数: 12

摘要

分选是民主的另一种方式,代表不是选举出来的,而是从人口中随机选出的。大多数选举民主国家甚至不能准确地代表少数受保护的群体。相比之下,排序保证了人口的每个子集都将在预期中填补其公平份额的可用职位。当基于已知特征对样本进行分层时,这种公平性仍然得到满足。此外,分层可以大大减少任何未知群体所占位置的变化,只要该群体与地层相关。我们的主要结果是,即使在存在不可分割性和舍入的情况下,分层也不能使这种方差增加超过一个可忽略不计的因素。当未知群体不均匀分布在地层上时,我们给出了相对于均匀抽样方差减小的保证。我们还将分层和均匀抽样置于公平抽样算法的空间中。最后,我们将我们的见解应用于实证案例研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
No Stratification Without Representation
Sortition is an alternative approach to democracy, in which representatives are not elected but randomly selected from the population. Most electoral democracies fail to accurately represent even a handful of protected groups. By contrast, sortition guarantees that every subset of the population will in expectation fill their fair share of the available positions. This fairness property remains satisfied when the sample is stratified based on known features. Moreover, stratification can greatly reduce the variance in the number of positions filled by any unknown group, as long as this group correlates with the strata. Our main result is that stratification cannot increase this variance by more than a negligible factor, even in the presence of indivisibilities and rounding. When the unknown group is unevenly spread across strata, we give a guarantee on the reduction in variance with respect to uniform sampling. We also contextualize stratification and uniform sampling in the space of fair sampling algorithms. Finally, we apply our insights to an empirical case study.
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