Think Complexity

Chapter 12. Case Study: Ant Trails

Chloe Vilain and Andrew Pikler

Introduction

In nature, ants scavenge for food as a swarm. They choose their paths from the nest based on pheromone density, favoring paths with higher concentrations of a pheromone. Individuals lay pheromone trails upon finding food, allowing other members of their colony to follow them passively to the food source. Because paths leading to food have higher pheromone density, increasing numbers of ants choose these successful paths. As a result, ant trails transition over time from random paths to streamlined routes. This organization and appearance of patterns over time is an example of emergence, the process by which complex systems and patterns arise from a multiplicity of simple systems.

Ant feeding patterns lend themselves to simulation with agent-based models, which simulate the actions and interactions of autonomous agents to assess their effects on the system as a whole. When we model ant feeding patterns, each ant is an agent in the model, a self-governing individual that makes decisions based on its surroundings. When simulating large numbers of ants, behavior emerges that is reflective of ant behavior in the natural world. Beyond being intrinsically fascinating, such models have applications in the real world in areas ranging from city planning to film production.

Model Overview

We can see one example of an agent-based model in Deneuborg et al.’s 1989 paper, “The Blind Leading the Blind: Modeling Chemically ...