比较多种算法选观点，找到兼顾代表性和多样性的最佳方案。
Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
- 基于社会选择理论设计新算法，同时考虑观点多样性和代表性。
- 实验证明现有方法难以同时满足代表性和多样性，新算法平衡最优。
- 适合关注在线讨论公平性与民主价值的平台设计者使用。
在线协商平台中，算法常被用于自动筛选少量有代表性的观点以生成报告。然而，过度追求共识可能忽视分歧意见，削弱少数群体声音并降低内容多样性。本文对比了多种观点选择策略（如共识、多样性），并基于社会选择理论提出一种融合多样性与均衡代表性的新算法。实验表明，尽管没有单一策略能在所有民主标准上表现最优，但该新算法在比例代表性和多样性之间实现了最强的权衡，为协商平台的算法设计提供了更公平的参考。
原文摘要 · Abstract (English)
During deliberation processes, mediators and facilitators typically need to select a small and representative set of opinions later used to produce digestible reports for stakeholders. In online deliberation platforms, algorithmic selection is increasingly used to automate this process. However, such automation is not without consequences. For instance, enforcing consensus-seeking algorithmic strategies can imply ignoring or flattening conflicting preferences, which may lead to erasing minority voices and reducing content diversity. More generally, across the variety of existing selection strategies (e.g., consensus, diversity), it remains unclear how each approach influences desired democratic criteria such as proportional representation. To address this gap, we benchmark several algorithmic approaches in this context. We also build on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy. We find empirically that while no single strategy dominates across all democratic desiderata, our social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.
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