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Related Experiment Video

Updated: Jul 12, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Naïve information aggregation in human social learning.

J-Philipp Fränken¹, Simon Valentin², Christopher G Lucas²

¹Stanford University, United States of America; The University of Edinburgh, United Kingdom.

|October 28, 2023

Summary

This summary is machine-generated.

People often struggle to distinguish evidence from hearsay in social networks. This study shows human learning is typically naive, not rational, challenging idealized models of social inference.

Keywords:

Bayesian modeling Causal inference Social learning Testimony

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Published on: July 21, 2021

Area of Science:

Cognitive Science
Social Psychology
Network Science

Background:

Information accuracy in social networks is crucial.
Hearsay and reliance on others' beliefs can distort information.
Understanding social learning dynamics is key to accurate collective inference.

Purpose of the Study:

Compare human social inference to idealized rational models.
Investigate how network structures influence information aggregation.
Identify mechanisms of collective wisdom in social learning.

Main Methods:

Conducted three multi-player experiments with human and artificial agents.
Analyzed social network dynamics and information flow.
Modeled human inferences using naïve and rational learning accounts.

Main Results:

Human inferences align with a naïve learning model, insensitive to network dependencies.
Rational agent simulations using human communication data made systematic errors.
Collective learning in human groups appears robust despite naïve signaling.

Conclusions:

Everyday social inference is not well-captured by idealized rational models.
Naïve learning and aggregation are computationally efficient and robust for human groups.
Understanding network communication pathways is essential for collective wisdom.