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Related Experiment Video

Updated: Jan 14, 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 alignment in social networks.

Yu Xia^1,2,3, Alex McAvoy^4,5, Qi Su^1,2,3

¹School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China.

National Science Review

|October 27, 2025

Summary

This summary is machine-generated.

Collective behaviors in groups, like fish schooling, emerge from simple interactions. This study explores how coordination and anti-coordination learning in networks create complex dynamics, revealing network structure

Keywords:

best response complex network evolutionary dynamics game theory

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Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

Complex Systems Science
Network Science
Behavioral Economics

Background:

Collective behaviors in large groups are vital for survival and stability.
Traditional research focused on social learning (imitation).
Emerging research highlights self-exploration and introspective learning.

Purpose of the Study:

Analyze networked systems with coordinating and anti-coordinating individuals.
Investigate the interplay of system dynamics, network structure, and behavior.
Determine equilibrium characteristics, time, and system resilience.

Main Methods:

Modeling networked systems with agents exhibiting coordination/anti-coordination.
Analyzing equilibrium states, convergence times, and resilience metrics.
Correlating network topology (e.g., average path length) with system behavior.

Main Results:

Equilibrium states can be numerous, growing exponentially with network changes.
Network structure significantly influences average equilibrium time.
System dynamics are predictable using a single network metric: average path length.

Conclusions:

Self-exploration learning in networks generates complex collective behaviors.
Network topology is a key determinant of emergent system properties.
Average path length offers a simple yet powerful predictor of system dynamics.