Modelling the emergence of coordinated collective motion by minimizing dissatisfaction

Area of Science:

• Complex Systems Science
• Computational Social Science
• Agent-Based Modeling

Background:

• Coordinated collective motion (CCM) is often simulated using high-level behavioral rules (repulsion, attraction, alignment).
• These rules may not fully capture the emergent nature of group behavior.
• A need exists for models demonstrating CCM from simpler, fundamental interaction principles.

Purpose of the Study:

• To develop and test an agent-based model (ABM) that generates CCM from low-level dyadic interaction rules.
• To investigate the emergence of CCM through agents minimizing dissatisfaction with inter-individual distances.
• To explore how perception range and memory influence group coordination and cohesion.

Main Methods:

• Development of a novel ABM where agents adjust positions based on dissatisfaction with distances to neighbors.
• Simulation experiments to observe the emergence and characteristics of CCM.
• Systematic variation of agent perception area and memory capacity to assess their impact on group dynamics.

Main Results:

• The ABM successfully produced CCM after a sufficient number of time steps.
• Increased perception area led to more coordinated and cohesive group motion.
• Enhanced memory span and agent identity recognition further improved cohesion and coordination.

Conclusions:

• CCM can emerge from simple, local dissatisfaction minimization rules, challenging previous models.
• Environmental perception and memory are critical factors for robust coordinated collective motion.
• The model provides a foundation for studying emergent group behaviors in various biological and artificial systems.