Making noise: emergent stochasticity in collective motion

Area of Science:

• Mathematical modeling
• Collective animal behavior
• Statistical physics

Background:

• Individual-based models (IBMs) are widely used to study collective animal motion.
• Existing IBMs for self-propelled particles (SPPs) often lack a unified framework.
• Stochasticity, or noise, in individual behavior is crucial for emergent group dynamics.

Purpose of the Study:

• To present a new, fully stochastic one-dimensional SPP model.
• To introduce an innovative method for incorporating noise into SPP models.
• To provide a unified framework for understanding collective animal behavior.

Main Methods:

• Developed a purely individual-to-individual, local stochastic SPP model in one dimension.
• Focused on a novel approach to noise integration within the model.
• Ensured the model's compatibility with existing frameworks and potential for higher-dimensional extension.

Main Results:

• The model successfully preserves emergent behaviors like coherent groups and spontaneous direction switching.
• Demonstrated a new, effective way to introduce stochasticity into SPP models.
• Qualitatively reproduced coarse-grained behaviors observed in recent locust movement data.

Conclusions:

• The proposed stochastic SPP model offers a simplified and unified approach to studying collective animal motion.
• This new noise-introduction method provides mechanistic insights into emergent properties of animal groups.
• The model's adaptability facilitates extension to more complex, higher-dimensional scenarios.