Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

Area of Science:

• Social Psychology
• Network Science
• Computational Social Science

Background:

• Collective behavior, such as coordinated motion in bird flocks and fish schools, emerges from local interactions without central control.
• Similar phenomena like consensus, clustering, and bipolarization are observed in human crowd dynamics and social network opinion formation.

Purpose of the Study:

• To draw parallels between human crowd dynamics and opinion dynamics in social networks.
• To demonstrate the mathematical similarities and analogous phenomena generated by models of crowd and opinion dynamics.
• To propose a unified framework of collective dynamics applicable to various psychological collectives.

Main Methods:

• Analysis of crowd networks and development of crowd dynamics models.
• Comparison of crowd dynamics models with existing opinion dynamics models in social networks.
• Multiagent simulations to observe emergent phenomena in both domains.

Main Results:

• Crowd dynamics and opinion dynamics models exhibit similar mathematical structures.
• Both types of models generate analogous collective behaviors, including consensus, clustering, and bipolarization.
• The study highlights the self-organized nature of decision-making in both crowds and social networks.

Conclusions:

• Human crowd behavior and social network opinion formation can be unified by a common collective dynamics.
• This unified model offers a way to understand collective decision-making without assuming pre-existing mental structures.
• The findings suggest broader applicability to other psychological collectives.