优化公民大会替补人选，提升代表性和决策公正性
Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies
- 基于历史数据预测淘汰概率，智能选替补
- 实测显示用更少替补就显著改善代表性
- 适合关注民主制度设计与公平性的研究者
公民大会是日益重要的协商民主形式，由随机选取的公众成员讨论政策议题。其正当性依赖于对整体人口的代表性，但参与者流失常导致结构失衡。实践中，流失者由预先选定的替补填补，但现有方法未解决如何最优选择替补的问题。为此，本文提出一种优化框架：利用学习理论工具，基于历史数据估计淘汰概率，并选择替补以最小化预期代表性偏差。理论分析提供了样本复杂度的保证（对计算效率有启示），以及因淘汰概率估计误差带来的损失上限。基于真实数据的实证评估表明，相比当前做法，该方法在减少替补数量的同时显著提升了代表性。
原文摘要 · Abstract (English)
Citizens' assemblies are an increasingly influential form of deliberative democracy, where randomly selected people discuss policy questions. The legitimacy of these assemblies hinges on their representation of the broader population, but participant dropout often leads to an unbalanced composition. In practice, dropouts are replaced by preselected alternates, but existing methods do not address how to choose these alternates. To address this gap, we introduce an optimization framework for alternate selection. Our algorithmic approach, which leverages learning-theoretic machinery, estimates dropout probabilities using historical data and selects alternates to minimize expected misrepresentation. Our theoretical bounds provide guarantees on sample complexity (with implications for computational efficiency) and on loss due to dropout probability mis-estimation. Empirical evaluation using real-world data demonstrates that, compared to the status quo, our method significantly improves representation while requiring fewer alternates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。