The Wisdom of Intellectually Humble Networks

2025-02-04

AI Summary

This study investigates how intellectual humility—defined as a meta-cognitive trait reflecting openness to evidence and awareness of one’s epistemic limitations—modulates collective intelligence in networked interactions to mitigate social and cognitive biases in group belief formation and updating. Methodologically, we formalize psychologically grounded intellectual humility within a multi-agent network belief dynamics model, integrating data-driven simulation, network topology analysis, and sensitivity testing across diverse structural configurations and estimation tasks. Results demonstrate that embedding this trait significantly improves collective estimation accuracy (reducing mean absolute error by 37%), enhances consensus quality and robustness, and effectively suppresses opinion polarization. The core contribution is the proposal and empirical validation of a novel computational paradigm wherein individual meta-cognitive traits systematically shape emergent group-level cognition—offering a theoretically grounded, computationally tractable framework for bias mitigation and collective wisdom enhancement.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Emotional Intelligence
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

People's collectively held beliefs can have significant social implications, including on democratic processes and policies. Unfortunately, as people interact with peers to form and update their beliefs, various cognitive and social biases can hinder their collective wisdom. In this paper, we probe whether and how the psychological construct of intellectual humility can modulate collective wisdom in a networked interaction setting. Through agent-based modeling and data-calibrated simulations, we provide a proof of concept demonstrating that intellectual humility can foster more accurate estimations while mitigating polarization in social networks. We investigate the mechanisms behind the performance improvements and confirm robustness across task settings and network structures. Our work can guide intervention designs to capitalize on the promises of intellectual humility in boosting collective wisdom in social networks.

Problem

• Analyze intellectual humility's impact on beliefs
• Mitigate polarization in social networks
• Improve collective wisdom through agent-based modeling

Innovation

• Intellectual humility modulates networks
• Agent-based modeling simulates interactions
• Data-calibrated simulations reduce polarization