Active matter flocking via predictive alignment.

Area of Science:

• Physics
• Complex Systems
• Collective Behavior

Background:

• Collective self-organization in active matter is a significant challenge.
• Alignment interactions are crucial for flocking but insufficient for cohesion against noise.
• Traditional models often rely on artificial boundaries or explicit attractive forces.

Purpose of the Study:

• To propose a novel model for cohesive flocking in active matter.
• To achieve robust group cohesion using predictive alignment strategies.
• To enhance noise resistance in self-organizing systems.

Main Methods:

• Developed a discrete-time Vicsek-type model incorporating predictive alignment.
• Agents predict future positions and optimize orientation based on neighbor count and alignment.
• Evaluated model performance in various noise and parameter conditions.

Main Results:

• The predictive alignment model achieves robust, noise-resistant cohesion without extra parameters.
• Flock size scales linearly with interaction radius in the stable regime.
• Groups maintain coherent leader following even under significant noise.

Conclusions:

• Predictive strategies significantly enhance self-organization in active matter.
• This approach offers a new framework for active matter models integrating physics with cognitive-like dynamics.
• The model demonstrates effective cohesion and leader following, applicable to biological systems.