Harnessing Collective Intelligence Under a Lack of Cultural Consensus
This provides a computational foundation for consensus-aware technologies in domains like leadership perception and risk assessment, though it is incremental as it extends an existing framework with deep learning enhancements.
The paper tackled the problem of detecting and characterizing heterogeneous consensus beliefs in collective intelligence, which is unworkable with existing Cultural Consensus Theory due to limitations in generalization, sparse data, and lack of external knowledge integration. They introduced iDLC-CCT, a nonparametric Bayesian model that better predicts consensus, generalizes to out-of-sample entities, and works with sparse data, validated across multiple domains.
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.