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Related Experiment Video

Updated: Jun 10, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Behavioral dynamics and influence in networked coloring and consensus.

Stephen Judd¹, Michael Kearns, Yevgeniy Vorobeychik

¹Computer and Information Science, University of Pennsylvania, Levine Hall, Philadelphia, PA, USA.

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

|August 11, 2010

Summary

Network structure impacts social tasks differently: increased long-distance connections aid consensus but hinder coloring. Individual behavior and task specifics are crucial in social networks.

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Area of Science:

Social network analysis
Behavioral economics
Computational social science

Background:

Social networks are often studied abstractly, focusing on topology.
Coordination games like coloring and consensus are fundamental in social interactions.
Understanding how network structure influences task performance is key.

Purpose of the Study:

To investigate the opposing effects of network structure on coloring and consensus tasks.
To analyze individual influence within social networks.
To highlight the importance of task details and individual behavior in network science.

Main Methods:

Human-subject experiments were conducted on social differentiation (coloring) and social agreement (consensus) tasks.
A parameterized family of social networks was used to vary network structure.
Individual influence and its correlation with behavior were analyzed.

Main Results:

Increased long-distance connectivity facilitated consensus but complicated coloring.
Individual influence varied significantly, extending beyond network position.
Strong correlations were found between subject influence and other behavioral features.

Conclusions:

Network structure's impact on social tasks is task-dependent.
Individual behavior and specific task characteristics are critical factors in social networks.
Network science should integrate task and individual details for a more complete understanding.