🤖 AI Summary
This work addresses the challenge of aggregating subjective preferences in group judgment tasks, where traditional methods such as simple averaging fail to effectively leverage social signals. The authors propose a signal routing framework that dynamically determines whether an individual should report their own preference, estimate others’ preferences, or remain silent. Notably, this approach is the first to formally model silence as an informative mechanism that conveys second-order information about uncertainty or disagreement. Simulations on a music preference dataset demonstrate that, in appropriate contexts, the proposed framework significantly outperforms baseline methods relying solely on individual preferences, with the strategic use of silence playing a crucial role in enhancing the accuracy of collective judgments.
📝 Abstract
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.