🐦 Boids – Emergent Collective Motion¶

This simulation implements Craig Reynolds' Boids model (1987): each agent follows three simple local rules, and complex flock-like motion emerges without any central control.

🧠 Idea¶

Every boid perceives only its nearby neighbours and applies three steering forces:

Rule	Effect
Separation	Steer away from neighbours that are too close
Alignment	Match heading with nearby neighbours
Cohesion	Steer toward the average position of nearby neighbours

No boid knows the shape of the flock. Yet the swarm self-organises into coherent, fluid formations – splitting around obstacles, merging again, and flowing like a living organism.

Comparison with Stigmergy Swarm¶

	Stigmergy (ants)	Boids (flocking)
Communication	Indirect (pheromone)	Direct (local sensing)
Goal	Path optimisation	Collective motion
Memory	External (environment)	None (stateless)
Structure	Trails and networks	Dynamic formations

Both models produce global order from local rules – but via fundamentally different coordination mechanisms.

🖼 Visualisation¶

The matplotlib window shows:

Coloured dots – each boid, colour-coded by heading (HSV hue = flight angle) so aligned sub-flocks share a colour
Faint trails – recent trajectory of each boid
Dark background – simulates a night-sky aesthetic

The world is toroidal (wrap-around edges).

Press ESC to exit.

🔗 Connection to System Intelligence¶

Regulation (R): The flock maintains cohesion (a target variable) without any explicit set-point
Adaptive Capacity (A): When the flock is disrupted, it reforms dynamically – no recovery plan needed
Emergent structure: Global formations are not prescribed by any individual rule

▶ Run¶

cd simulation-models/boids-flocking
python3 boids.py

Experiment ideas¶

Increase W_SEPARATION to 4.0 → the flock dissolves into loose gas
Decrease R_COHESION to 5.0 → many small sub-flocks instead of one
Set NUM_BOIDS = 500 for dramatic large-flock dynamics (slower)
Try MAX_SPEED = 4.0 for fast, chaotic motion