Harnessing collective intelligence to drive effective decision-making and collaboration benefits from the ability to detect and characterize heterogeneity in consensus beliefs. This is particularly true in domains such as technology acceptance or leadership perception, where a consensus defines an intersubjective truth, leading to the possibility of multiple "ground truths" when subsets of respondents sustain mutually incompatible consensuses. Cultural Consensus Theory (CCT) provides a statistical framework for detecting and characterizing these divergent consensus beliefs. However, it is unworkable in modern applications because it lacks the ability to generalize across even highly similar beliefs, is ineffective with sparse data, and can leverage neither external knowledge bases nor learned machine representations. Here, we overcome these limitations through Infinite Deep Latent Construct Cultural Consensus Theory (iDLC-CCT), a nonparametric Bayesian model that extends CCT with a latent construct that maps between pretrained deep neural network embeddings of entities and the consensus beliefs regarding those entities among one or more subsets of respondents. We validate the method across domains including perceptions of risk sources, food healthiness, leadership, first impressions, and humor. We find that iDLC-CCT better predicts the degree of consensus, generalizes well to out-of-sample entities, and is effective even with sparse data. To improve scalability, we introduce an efficient hard-clustering variant of the iDLC-CCT using an algorithm derived from a small-variance asymptotic analysis of the model. The iDLC-CCT, therefore, provides a workable computational foundation for harnessing collective intelligence under a lack of cultural consensus and may potentially form the basis of consensus-aware information technologies.

翻译：利用集体智慧推动有效决策与协作，其优势在于能够检测并刻画共识信念中的异质性。这在技术接受或领导力感知等领域尤为关键——共识在此定义了主体间真理，当受访者子集持有相互矛盾的共识时，可能产生多种"基本真理"。文化共识理论（CCT）提供了检测和刻画这些分歧共识信念的统计框架，但在现代应用中难以奏效，因其无法泛化至高度相似的信念，对稀疏数据效果不佳，且既无法利用外部知识库也无法借助学习型机器表征。为此，我们通过无限深度潜结构文化共识理论（iDLC-CCT）突破这些局限——这是一种非参数贝叶斯模型，通过扩展CCT引入潜结构，将预训练深度神经网络对实体的嵌入与一个或多个受访者子集对这些实体的共识信念建立映射。我们跨风险源感知、食品健康性、领导力、第一印象及幽默感等领域验证该方法，发现iDLC-CCT能更准确预测共识程度、对样本外实体具有良好的泛化能力，甚至在稀疏数据下仍保持有效性。为提升可扩展性，我们基于模型小方差渐近分析推导算法，提出iDLC-CCT的高效硬聚类变体。因此，iDLC-CCT为在缺乏文化共识下利用集体智慧提供了可计算基础，并可能构成共识感知信息技术的底层支撑。