Harnessing Collective Intelligence Under a Lack of Cultural Consensus
Necdet G\"urkan, Jordan W. Suchow

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…
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Taxonomy
TopicsCultural Differences and Values · Complex Network Analysis Techniques · Advanced Text Analysis Techniques
MethodsAttention Is All You Need · Softmax · Dense Connections · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Residual Connection · Adam · Linear Layer · Multi-Head Attention · Dropout
