Harnessing Collective Intelligence Under a Lack of Cultural Consensus

TL;DR

This paper introduces iDLC-CCT, a nonparametric Bayesian model that extends Cultural Consensus Theory with deep neural network embeddings to better detect and characterize divergent consensus beliefs across various domains.

Contribution

The paper presents iDLC-CCT, a novel model that generalizes Cultural Consensus Theory using deep learning and nonparametric Bayesian methods, improving scalability, generalization, and effectiveness with sparse data.

Findings

iDLC-CCT better predicts consensus levels across domains.

The model generalizes well to unseen entities.

The scalable variant performs efficiently with large datasets.

Abstract

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.