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.

翻译：群体智慧不仅被证明适用于事实判断，也适用于品味领域——此处的准确性是相对于个人偏好而定义的。然而，在这类领域中应如何选择性地利用不同类型的社会信号，目前仍不明确。本文聚焦于一个音乐偏好数据集，其中贡献者既提供了个人评价（Own），也提供了对群体层面偏好的估计（Estimated）。我们针对品味领域的集体智能提出了一种路由框架。该框架明确了贡献者应在何时发言、应报告何种信息，以及何时保持沉默更为可取。通过基于模拟的聚合分析，我们表明，在适用路由的项目条件下，预测准确性在参数空间的广泛区域内均优于全个人评价基线。重要的是，这些改进仅在允许沉默时才会出现，从而使二阶信号能够有效发挥作用。研究结果表明，品味领域的集体智能依赖于有原则的信号路由，而非简单的平均。