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Related Experiment Video

Updated: Jan 8, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Individual incentives that promote collective intelligence.

Guocheng Wang^1,2, Qi Su^3,4, Long Wang¹

¹Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China.

Proceedings of the National Academy of Sciences of the United States of America

|December 15, 2025

Summary

This summary is machine-generated.

Collective intelligence emerges in groups through social learning, even without central control. Rewarding individuals who reduce collective error (reformers) is more effective than rewarding accurate individuals (experts) for promoting group problem-solving.

Keywords:

collective behavior evolutionary dynamics imitation learning problem solving

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Area of Science:

Computational Social Science
Behavioral Economics
Evolutionary Game Theory

Background:

Collective intelligence, where groups outperform individuals, is observed in nature but not fully explained by algorithms requiring central control.
Social learning via imitation typically reduces diversity and hinders collective intelligence in natural populations.
Existing models struggle to explain the evolution of collective intelligence in systems lacking sophisticated central planning.

Purpose of the Study:

To investigate how collective intelligence can emerge in natural populations through social learning without central control.
To propose and analyze novel reward structures that promote collective intelligence.
To compare the effectiveness of different reward mechanisms in fostering group problem-solving.

Main Methods:

A prediction task simulation where individuals observe one factor of a continuous outcome.
Development of two reward structures: one for personal accuracy (experts) and one for reducing collective error (reformers).
Mathematical analysis to prove the emergence of collective intelligence and diversity under these reward schemes.

Main Results:

Both expert and reformer reward structures can provably maintain diversity and establish collective intelligence.
Rewards based on collective error (reformers) are more robust across diverse problem settings than rewards based on personal accuracy (experts).
Identifying and rewarding reformers is more effective than identifying experts for promoting collective intelligence.

Conclusions:

Collective intelligence can evolve in natural populations through social learning guided by appropriate reward structures.
Reward mechanisms focusing on collective benefit, rather than individual accuracy, are superior for fostering robust group problem-solving.
The findings offer insights into designing systems that enhance group performance in complex prediction tasks.