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Fair algorithms for selecting citizens' assemblies

机译：选择公民组件的公平算法

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摘要

Globally, there has been a recent surge in 'citizens' assemblies'~(1), which are a form of civic participation in which a panel of randomly selected constituents contributes to questions of policy. The random process for selecting this panel should satisfy two properties. First, it must produce a panel that is representative of the population. Second, in the spirit of democratic equality, individuals would ideally be selected to serve on this panel with equal probability~(2,3). However, in practice these desiderata are in tension owing to differential participation rates across subpopulations~(4,5). Here we apply ideas from fair division to develop selection algorithms that satisfy the two desiderata simultaneously to the greatest possible extent: our selection algorithms choose representative panels while selecting individuals with probabilities as close to equal as mathematically possible, for many metrics of 'closeness to equality'. Our implementation of one such algorithm has already been used to select more than 40 citizens' assemblies around the world. As we demonstrate using data from ten citizens' assemblies, adopting our algorithm over a benchmark representing the previous state of the art leads to substantially fairer selection probabilities. By contributing a fairer, more principled and deployable algorithm, our work puts the practice of sortition on firmer foundations. Moreover, our work establishes citizens' assemblies as a domain in which insights from the field of fair division can lead to high-impact applications.
机译：在全球范围内，最近的“公民组合”〜（1）浪涌，这是一种公民参与的形式，其中一个随机选择的成员小组有助于政策问题。选择该面板的随机过程应满足两个属性。首先，它必须制作代表人口的面板。其次，本着民主平等的精神，理想地选择个人以相同的概率〜（2,3）在该面板上服务。然而，在实践中，由于群体的差异参与率〜（4,5），这些追逐率呈张力。在这里，我们将思想应用于公平划分，以开发选择算法，以尽可能大的情况下同时满足两个探索之域：我们的选择算法选择代表面板，同时选择具有较近数学的概率的特性，对于“接近平等的许多指标” '。我们的实现一种这样的算法已经用于选择世界各地的40多名公民的大会。正如我们使用来自十个公民组件的数据展示，在代表前一个最先进状态的基准中采用我们的算法，导致基本上更公平的选择概率。通过促进更公平，更原则和可部署的算法，我们的工作将独立的基础上的实践说明。此外，我们的工作建立了公民大会作为一个领域，其中公平部门领域的见解可以导致高影响的应用。

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  • 来源
    《Nature》 |2021年第7873期|548-552|共5页
  • 作者

    Bailey Flanigan; Paul Gölz; Anupam Gupta; Brett Hennig; Ariel D Procaccia;

  • 作者单位

    Computer Science Department Carnegie Mellon University;

    Computer Science Department Carnegie Mellon University;

    Computer Science Department Carnegie Mellon University;

    Sortition Foundation;

    School of Engineering and Applied Sciences Harvard University;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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