让合适的人在合适时沉默，能提升集体审美判断的准确性
Silence Routing: When Not Speaking Improves Collective Judgment
- 根据个人偏好与群体认知差异，动态决定何时发言或保持沉默
- 在允许沉默的前提下，预测准确率显著优于全量发言基准
- 适用于需权衡个体偏好与群体共识的场景，如音乐推荐
群体智慧不仅适用于事实判断，也适用于品味领域，其中准确性以个体偏好为参照。然而，在此类领域中，不同类型的社会信号应如何选择性使用仍不明确。本文基于一个音乐偏好数据集，参与者既提供个人评价（Own），也估算群体层面的偏好（Estimated）。我们提出一种针对品味领域的集体智能路由框架，明确何时发言、报告什么内容，以及何时保持沉默更优。通过基于仿真的聚合方法，发现在适用路由的项目上，预测准确率在广泛参数范围内均优于全量个人评价基准。重要的是，这些提升仅在允许沉默的情况下出现，使二级信号得以有效运作。结果表明，品味领域的集体智慧依赖于有原则的信号路由，而非简单平均。
原文摘要 · Abstract (English)
The wisdom of crowds has been shown to operate not only for factual judgments but also in matters of taste, where accuracy is defined relative to an individual's preferences. However, it remains unclear how different types of social signals should be selectively used in such domains. Focusing on a music preference dataset in which contributors provide both personal evaluations (Own) and estimates of population-level preferences (Estimated), we propose a routing framework for collective intelligence in taste. The framework specifies when contributors should speak, what they should report, and when silence is preferable. Using simulation-based aggregation, we show that prediction accuracy improves over an all-own baseline across a broad region of the parameter space, conditional on items where routing applies. Importantly, these gains arise only when silence is allowed, enabling second-order signals to function effectively. The results demonstrate that collective intelligence in matters of taste depends on principled signal routing rather than simple averaging.
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