Simulation #2: Ant Mill

This experiment models individual ants using local movement rules. Each ant senses nearby trail pheromone, deposits its own trail, responds to neighbours and searches for food. Under some conditions, positive feedback can reinforce a circular procession. The model is illustrative: it does not claim that every natural ant mill forms by exactly these mechanisms.

Simulation #2: Ant Mill

Agent-based model · local rules · collective outcomes

Initialising
Living ants300
Food collected0
Trail intensity0%
Circularity0%
Loop statusNot detected

Experiments

Try starting with dispersed ants, then use "Seed circular trail" as a comparison. A seeded circle is an experimental intervention, not spontaneous emergence. Compare circularity and loop duration across settings. In food mode, ants can follow a food-associated trail as well as other ants' deposited trail signal.

Food Ants carrying food Searching ants Detected circular flow
Model note: Ants use local sensing, directional persistence, trail deposition, trail decay, food seeking and simple neighbour alignment. The loop detector looks for sustained group rotation around a shared centre; it is a heuristic indicator, not a biological diagnosis. Real army-ant mills are associated with trail-following feedback and can have additional ecological causes not represented here.

How to run an experiment

  1. Start with the default population of 300 ants and the default settings.
  2. Observe whether the trail field strengthens into a circular pattern without seeding one.
  3. Select Seed circular trail to compare an imposed circle with spontaneous movement.
  4. Increase randomness or evaporation, then use Disturb the trail to see whether the pattern persists.
  5. Change the environment to obstacles and barriers to observe how the agents alter their routes.

The circularity score is a heuristic based on the distribution and direction of movement. A high score is not, by itself, proof of a biologically realistic ant mill.

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