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The Wisdom of Intellectually Humble Networks

Mohammad Ratul Mahjabin, Raiyan Abdul Baten

TL;DR

Intellectual humility (IH) is proposed as a lever to enhance the wisdom of crowds in networked belief updates. The authors develop a DeGroot-based agent model in which IH modulates self-weights through mechanisms tied to evidence evaluation and resistance to homophily, and they validate this approach with data-calibrated simulations across diverse network structures. The results show that IH reduces both estimation errors and polarization, increases the Revision Coefficient, and yields a net improvement in collective accuracy as per the Diversity Prediction Theorem, with robustness across tasks and topologies. These findings point to practical IH-inspired interventions to bolster collective judgment in political and organizational settings, and outline directions for scalable implementations. The work thus links individual-level IH processes to emergent network-level wisdom and provides a mechanistic framework for future empirical validation.

Abstract

People's collectively shared 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.

The Wisdom of Intellectually Humble Networks

TL;DR

Intellectual humility (IH) is proposed as a lever to enhance the wisdom of crowds in networked belief updates. The authors develop a DeGroot-based agent model in which IH modulates self-weights through mechanisms tied to evidence evaluation and resistance to homophily, and they validate this approach with data-calibrated simulations across diverse network structures. The results show that IH reduces both estimation errors and polarization, increases the Revision Coefficient, and yields a net improvement in collective accuracy as per the Diversity Prediction Theorem, with robustness across tasks and topologies. These findings point to practical IH-inspired interventions to bolster collective judgment in political and organizational settings, and outline directions for scalable implementations. The work thus links individual-level IH processes to emergent network-level wisdom and provides a mechanistic framework for future empirical validation.

Abstract

People's collectively shared 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.

Paper Structure

This paper contains 20 sections, 12 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Social Influence and the Collective Wisdom of Networks
  3. Intellectual Humility
  4. The Present Work: Impact of Intellectual Humility on Collective Wisdom in Networks
  5. Theoretical Modeling and Simulation
  6. Capturing IH in Self-weight Parameters
  7. Effects of Evidence Quality Evaluation.
  8. Effects of Homophily.
  9. Metrics
  10. Individual Error.
  11. Polarization.
  12. Revision Coefficient.
  13. Notations for the Diversity Prediction Theorem.
  14. Results
  15. Estimation Errors and Polarization Reduce in Intellectually Humble Networks
  16. ...and 5 more sections

Figures (1)

  • Figure 1: (A) Normalized estimation errors and (B) Polarization (average pairwise distance) significantly reduce in treatment networks, while (C) Revision Coefficients increase. (D) The partial correlation between $\tilde{e}_{i,q}$ and $\triangle \tilde{x}_{i,q}$ (adjusted for $s_{i,q}$) improves in the treatment condition. Shaded regions and whiskers denote $95\%$ C.I. *** $P<0.001$, ** $P<0.01$.