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Updated: Jun 8, 2026
06:48
The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
Collective helping and bystander effects in coevolving helping networks.
Hang-Hyun Jo1, Hyun Keun Lee, Hyunggyu Park
1School of Physics, Korea Institute for Advanced Study, Seoul 130-722, Korea.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
Summary
This study explores collective helping behavior and bystander effects in evolving social networks. Findings reveal mechanisms behind bystander effects, offering insights into social dynamics.
Area of Science:
- Social network analysis
- Behavioral economics
- Computational social science
Background:
- Understanding collective helping behavior is crucial for social dynamics.
- Bystander effects can significantly influence prosocial actions.
- Previous models often simplify the coevolution of relationships and behavior.
Purpose of the Study:
- To model and analyze collective helping behavior in a coevolving network.
- To investigate the emergence and mechanisms of bystander effects.
- To map the phase diagram of network dynamics and identify steady states.
Main Methods:
- Developed a coevolving helping network model.
- Employed analytical and numerical simulations to study network dynamics.
- Analyzed phase transitions and steady states of the network.
Main Results:
- Identified distinct active and inactive phases in the network.
- Observed various bystander effects consistent with empirical findings.
- Proposed a mechanism underlying the observed bystander effects.
Conclusions:
- The coevolving network model effectively captures complex helping behaviors.
- Bystander effects arise from the interplay of network structure and helping dynamics.
- The study provides a theoretical framework for understanding social influence in networks.
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