Decoding the Remarkable Algorithms of Ants

Ants are capable of remarkable feats of coordination. They can forge complex paths through the jungle, build sophisticated structures, and adapt foraging patterns to fit their environment, all without orders from a centralized source. , a biologist at Stanford University, hopes to uncover the simple rules that produce complex patterns from simple individual actions.

Ants in particular excel at collective search, automatically tailoring their search strategy to efficiently cover large areas of ground. Gordon has found parallels between the algorithms ant colonies use for foraging and the man-made ones that underlie the Internet. Given how long ants have been solving these kinds of problems, Gordon hopes that she will uncover new algorithms that will ultimately make large-scale computing networks cheaper and more efficient.

 met with Gordon at a social insects conference in Cold Spring Harbor, N.Y., shortly before she left for a trip to Mexico to study routing algorithms in arboreal ants. An edited and condensed version of the conversation follows.

For example, ants are really good at collective search; a group of ants can cover the search area thoroughly without any central control. They do it through simple interactions, just touching antennae. When many ants are in a small space, they meet often and tend to take a convoluted path that keeps them stuck in one place. When few ants are in a large space, they don’t meet that often. They stretch out their paths and cover more ground.

For harvester ants, we’ve learned that an ant decides whether to go out of the nest and forage using the . It’s a form of positive feedback; the faster ants are coming in with food, the more ants go out. Each ant decides only when its rate of interaction is high enough to go out. Overall this system allows the colony to regulate foraging activity so that the ants don’t go out unless there’s enough food to make it worthwhile.

Balaji Prabhakar, a colleague at Stanford, to figure out the algorithm that the harvester ants are using to regulate foraging. He pointed out that the algorithm is similar to the Transmission Control Protocol, which regulates data traffic on the Internet to make sure that data don’t go out unless there’s enough bandwidth. Both systems use simple, local feedback to regulate activity. I think we might be able to find other algorithms that ants use to solve engineering problems that we haven’t thought of yet. I am interested in the idea that evolution might produce different algorithms in different systems to solve the same problems.

the rewards of restraint.” I think this is the first study that’s been able to track the evolution of collective behavior in a natural population of animals. A simple, local behavior—how ants respond when one meets another—is being selected for because of the outcome for the whole colony.

In this case, we are interested in the analogy to routing algorithms in computer systems where the straightest or shortest path might require a lot of information. Say you’re driving somewhere unfamiliar, and the exit you want to take is blocked. You can find your way around if you have a map. But how would you do that without any information? Without an address?

Collective search faces a similar problem. In robotics, there’s now a lot of interest in using the cheapest robots that require as little information as possible and that work together. A system like this is more robust to failure. Rather than sending one really complex robot to explore Mars or to search a burning building, it makes sense to send a group of cheap robots that will still work as a group even if one malfunctions. There are probably many new algorithms that ants have evolved to solve problems like this that we haven’t thought of. What we need to do is go take a look.

We’re surrounded by giant networks—the Internet, our brains—so that got me interested in other systems. How does the behavior of a network scale as it gets larger?

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