Ant Colonies

How do ants create paths?

Imagine a colony of ants stumbling upon a sugar cube in your kitchen. Within minutes, you see a near-perfect line of ants traveling between the sugar and their nest. No GPS. No boss. No map. No Wi-Fi. So how do they do it?

In this article, we will explore how something as simple as an ant can contribute to the emergence of complex and efficient behavior. Along the way, you will get to interact with a simulation and discover for yourself how collective intelligence emerges from individual simplicity.

Real ants don’t have much brainpower. Their individual behavior is quite simple. We will show how the seemingly complex behavior of an entire ant colony can emerge from just four simple individual rules:

  1. Wander randomly until food is found.
  2. Return to the nest while leaving a chemical trail (pheromones).
  3. Follow pheromone trails when they are detected.
  4. Allow pheromones to evaporate over time.

Despite the simplicity of these rules, we will see that the collective behavior that emerges from them is surprisingly sophisticated. Let’s use the simulation from the official NetLogo website. We consider a grid-based world with:

  • A nest (in purple), placed at the center.
  • Three food sources (in blue), located around.

Let’s begin by releasing just a single ant from the nest. The ant on the screen will follow the three basic rules described earlier.

Click on “setup,” then click on “go” to launch the simulation.

Watch it wander randomly through the environment. When it founds foods, it returns to the nest, leaving behind a trail of pheromones (in green). You will notice that the pheromone trail will evaporate over time if not reinforced.

This solitary ant does not look very efficient. Now, let’s take plenty of ants. Each one follows the same simple rules describes earlier.

Again, click on “setup” and after on “go” to lunch the simulation.

Now, something remarkable happens: trails begin to form! No ant has an overview of the map. No ant is coordinating the others. But together, they build an efficient transportation network. As you can see, each ant follows local and simple rules. But collectively, the colony finds the shortest paths, allocates workers dynamically, and could also adapt to changes in food supply. Indeed, when enough ants reinforce the pheromone path, an efficient route appears attracting even more ants. But, If food is depleted, the trail fades. Ants will resume exploration, and new trails may emerge, showing that the system is adaptive.

This is an example of stigmergy —a concept in biology that describes indirect coordination through environmental traces. In the case of ants, the environment is marked chemically with pheromones. But the idea is more general: one agent changes the environment, and that change guides future behavior, creating a feedback loop.

To explore this phenomenon more deeply, use the sliders to modify key parameters of the model.

  • Population: number of ants moving around the grid. When there are only a few ants, they don’t explore the environment thoroughly, and trails frequently fail to form.
  • Diffusion rate: controls how much the pheromones spread out from where they were deposited. If the diffusion is very low, the trails stay narrow. But if the diffusion rate is high, the pheromones spread out more widely, and the trails become blurry. While this makes it easier for nearby ants to detect the trail, it can also cause multiple trails to blend together or become less meaningful, making it harder for ants to commit to a specific direction.
  • Evaporation rate: determines how long pheromone trails stay active. When evaporation is slow, the trails stick around longer, so once a path is found, it tends to stay strong. This can make the system very efficient — but it also makes it harder for the colony to adapt if conditions change. In contrast, when evaporation happens quickly, the system is more flexible. This supports adaptability, but may slow the process of forming stable paths, because good trails don’t last long enough to get reinforced.

Together, these three parameters — how many ants there are, how far their pheromones spread, and how long those pheromones last — shape the colony’s ability to explore, learn, and adapt. There’s no single “correct” setting. The most efficient behavior appears when the system finds a balance between exploration and exploitation:

  • Exploration means wandering, trying new paths, occasionally getting lucky. It’s what allows the system to discover food sources in the first place.
  • Exploitation means reinforcing and relying on known successful paths. It’s what makes the system efficient once good paths have been discovered.

Too much exploration, and the ants never settle on a good path. Too much exploitation, and the colony becomes rigid—it stops adapting. This balance is not built into any individual ant. It emerges from the interaction between pheromone chemistry, movement, and time.

This phenomenon also appears in optimization algorithms, like Ant Colony Optimization, used in robotics, logistics, and computer science.