Inside Animal Minds: The New Science of Animal Intelligence (6 page)

I
used to think ants knew what they were doing. The ones marching across my kitchen counter looked so confident, I just figured they had a plan, knew where they were going, and what needed to be done. How else could ants organize highways, build elaborate nests, stage epic raids, and do all the other things ants do?

Turns out I was wrong. Ants aren’t clever little engineers, architects, or warriors after all—at least not as individuals. When it comes to deciding what to do next, most ants don’t have a clue. “If you watch an ant try to accomplish something, you’ll be impressed by how inept it is,” says Deborah M. Gordon, a biologist at Stanford University.

How do we explain, then, the success of Earth’s 12,000 or so known ant species? They must have learned something in 140 million years.

“Ants aren’t smart,” Gordon says. “Ant colonies are.” A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies, they respond quickly and effectively to their environment. They do it with something called swarm intelligence.

Where this intelligence comes from raises a fundamental question in nature: How do the simple actions of individuals add up to
the complex behavior of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The collective abilities of such animals—none of which grasps the big picture, but each of which contributes to the group’s success—seem miraculous even to the biologists who know them best. Yet during the past few decades, researchers have come up with intriguing insights.

One key to an ant colony, for example, is that no one’s in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with a half million ants, a colony functions just fine with no management at all—at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.

Consider the problem of job allocation. In the Arizona desert where Deborah Gordon studies red harvester ants
(Pogonomyrmex barbatus)
, a colony calculates each morning how many workers to send out foraging for food. The number can change, depending on conditions. Have foragers recently discovered a bonanza of tasty seeds? More ants may be needed to haul the bounty home. Was the nest damaged by a storm last night? Additional maintenance workers may be held back to make repairs. An ant might be a nest worker one day, a trash collector the next. But how does a colony make such adjustments if no one’s in charge? Gordon has a theory.

Ants communicate by touch and smell. When one ant bumps into another, it sniffs with its antennae to find out if the other belongs to the same nest and where it has been working. (Ants that work outside the nest smell different from those that stay inside.) Before they leave the nest each day, foragers normally wait for early
morning patrollers to return. As patrollers enter the nest, they touch antennae briefly with foragers.

“When a forager has contact with a patroller, it’s a stimulus for the forager to go out,” Gordon says. “But the forager needs several contacts no more than ten seconds apart before it will go out.”

To see how this works, Gordon and her collaborator Michael Greene of the University of Colorado at Denver captured patroller ants as they left a nest one morning. After waiting a half hour, they simulated the ants’ return by dropping glass beads into the nest entrance at regular intervals—some coated with patroller scent, some with maintenance worker scent, some with no scent. Only the beads coated with patroller scent stimulated foragers to leave the nest. Their conclusion: Foragers use the rate of their encounters with patrollers to tell if it’s safe to go out. (If you bump into patrollers at the right rate, it’s time to go foraging. If not, better wait. It might be too windy, or there might be a hungry lizard waiting out there.) Once the ants start foraging and bringing back food, other ants join the effort, depending on the rate at which they encounter returning foragers.

“A forager won’t come back until it finds something,” Gordon says. “The less food there is, the longer it takes the forager to find it and get back. The more food there is, the faster it comes back. So nobody’s deciding whether it’s a good day to forage. The collective is, but no particular ant is.”

That’s how swarm intelligence works: Simple creatures are following simple rules, each one acting on local information. No ant sees the big picture. No ant tells any other ant what to do. Some ant species may go about this with more sophistication than others. (
Temnothorax albipennis
, for example, can rate the quality of a potential nest site using multiple criteria.) But the bottom line, says Iain Couzin, a biologist at Oxford and Princeton Universities, is that no leadership is required. “Even complex behavior may be
coordinated by relatively simple interactions,” he says.

Inspired by the elegance of this idea, Marco Dorigo, a computer scientist at the Université Libre in Brussels, used his knowledge of ant behavior in 1991 to create mathematical procedures for solving particularly complex human problems, such as routing trucks, scheduling airlines, or guiding military robots.

In Houston, for example, a company named American Air Liquide has been using an ant-based strategy to manage a complex business problem. The company produces industrial and medical gases, mostly nitrogen, oxygen, and hydrogen, at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Deregulated power markets in some regions (the price of electricity changes every 15 minutes in parts of Texas) add yet another layer of complexity.

“Right now in Houston, the price is $44 a megawatt for an industrial customer,” says Charles N. Harper, who oversees the supply system at Air Liquide. “Last night the price went up to $64, and Monday when the cold front came through, it went up to $210.” The company needed a way to pull it all together.

Working with the BiosGroup (now NuTech Solutions), a firm that specialized in artificial intelligence, Air Liquide developed a computer model based on algorithms inspired by the foraging behavior of Argentine ants
(Linepithema humile)
, a species that deposits chemical substances called pheromones.

“When these ants bring food back to the nest, they lay a pheromone trail that tells other ants to go get more food,” Harper explains. “The pheromone trail gets reinforced every time an ant goes out and comes back, kind of like when you wear a trail in the forest to collect wood. So we developed a program that sends out billions of software ants to find out where the pheromone trails are strongest for our truck routes.”

Ants had evolved an efficient method to find the best routes in
their neighborhoods. Why not follow their example? So Air Liquide combined the ant approach with other artificial intelligence techniques to consider every permutation of plant scheduling, weather, and truck routing—millions of possible decisions and outcomes a day. Every night, forecasts of customer demand and manufacturing costs are fed into the model.

“It takes four hours to run, even with the biggest computers we have,” Harper says. “But at six o’clock every morning we get a solution that says how we’re going to manage our day.”

For truck drivers, the new system took some getting used to. Instead of delivering gas from the plant closest to a customer, as they used to do, drivers were now asked to pick up shipments from whichever plant was making gas at the lowest delivered price, even if it was farther away.

“You want me to drive a hundred miles? To the drivers, it wasn’t intuitive,” Harper says. But for the company, the savings have been impressive. “It’s huge. It’s actually huge.”

Other companies also have profited by imitating ants. In Italy and Switzerland, fleets of trucks carrying milk and dairy products, heating oil, and groceries all use ant-foraging rules to find the best routes for deliveries. In England and France, telephone companies have made calls go through faster on their networks by programming messages to deposit virtual pheromones at switching stations, just as ants leave signals for other ants to show them the best trails.

In the United States, Southwest Airlines has tested an ant-based model to improve service at Sky Harbor International Airport in Phoenix. With about 200 aircraft a day taking off and landing on two runways and using gates at three concourses, the company wanted to make sure that each plane got in and out as quickly as possible, even if it arrived early or late.

“People don’t like being only 500 yards away from a gate and having to sit out there until another aircraft leaves,” says Doug
Lawson of Southwest. So Lawson created a computer model of the airport, giving each aircraft the ability to remember how long it took to get into and away from each gate. Then he set the model in motion to simulate a day’s activity.

“The planes are like ants searching for the best gate,” he says. But rather than leaving virtual pheromones along the way, each aircraft remembers the faster gates and forgets the slower ones. After many simulations, using real data to vary arrival and departure times, each plane learned how to avoid an intolerable wait on the tarmac. Southwest was so pleased with the outcome that it may use a similar model to study the ticket counter area.

When it comes to swarm intelligence, ants aren’t the only insects with something useful to teach us. On a small, breezy island off the southern coast of Maine, Thomas Seeley, a biologist at Cornell University, has been looking into the uncanny ability of honeybees to make good decisions. With as many as 50,000 workers in a single hive, honeybees have evolved ways to work through individual differences of opinion to do what’s best for the colony. If only people could be as effective in boardrooms, church committees, and town meetings, Seeley says, we could avoid problems making decisions in our own lives.

During the past decade, Seeley, Kirk Visscher of the University of California, Riverside, and others have been studying colonies of honeybees
(Apis mellifera)
to see how they choose a new home. In late spring, when a hive gets too crowded, a colony normally splits, and the queen, some drones, and about half the workers fly a short distance to cluster on a tree branch. There, the bees bivouac while a small percentage of them go searching for new real estate. Ideally, the site will be a cavity in a tree, well off the ground, with a small entrance hole facing south, and lots of room inside for brood and honey. Once a colony selects a site, it usually won’t move again, so it has to make the right choice.

To find out how, Seeley’s team applied paint dots and tiny plastic tags to identify all 4,000 bees in each of several small swarms that they ferried to Appledore Island, home of the Shoals Marine Laboratory. There, in a series of experiments, they released each swarm to locate nest boxes they’d placed on one side of the half-mile-long island, which has plenty of shrubs but almost no trees or other places for nests.

In one test, they put out five nest boxes, four that weren’t quite big enough and one that was just about perfect. Scout bees soon appeared at all five. When they returned to the swarm, each performed a waggle dance urging other scouts to go have a look. (These dances include a code giving directions to a box’s location.) The strength of each dance reflected the scout’s enthusiasm for the site. After a while, dozens of scouts were dancing their little feet off, some for one site, some for another, and a small cloud of bees was buzzing around each box.

The decisive moment didn’t take place in the main cluster of bees, but out at the boxes, where scouts were building up. As soon as the number of scouts visible near the entrance to a box reached about 15—a threshold confirmed by other experiments—the bees at that box sensed that a quorum had been reached, and they returned to the swarm with the news.

“It was a race,” Seeley says. “Which site was going to build up 15 bees first?”

Scouts from the chosen box then spread through the swarm, signaling that it was time to move. Once all the bees had warmed up, they lifted off for their new home, which, to no one’s surprise, turned out to be the best of the five boxes.

The bees’ rules for decision making—seek a diversity of options, encourage a free competition among ideas, and use an effective mechanism to narrow choices—so impressed Seeley that he now uses them at Cornell as chairman of his department.

“I’ve applied what I’ve learned from the bees to run faculty meetings,” he says. To avoid going into a meeting with his mind made up, hearing only what he wants to hear, and pressuring people to conform, Seeley asks his group to identify all the possibilities, kick their ideas around for a while, then vote by secret ballot. “It’s exactly what the swarm bees do, which gives a group time to let the best ideas emerge and win. People are usually quite amenable to that.”

In fact, almost any group that follows the bees’ rules will make itself smarter, says James Surowiecki, author of
The Wisdom of Crowds
. “The analogy is really quite powerful. The bees are predicting which nest site will be best, and humans can do the same thing, even in the face of exceptionally complex decisions.” Investors in the stock market, scientists on a research project, even kids at a county fair guessing the number of beans in a jar can be smart groups, he says, if their members are diverse, independent minded, and use a mechanism such as voting, auctioning, or averaging to reach a collective decision.

Take bettors at a horse race. Why are they so accurate at predicting the outcome of a race? At the moment the horses leave the starting gate, the odds posted on the pari-mutuel board, which are calculated from all bets put down, almost always predict the race’s outcome: Horses with the lowest odds normally finish first, those with second lowest odds finish second, and so on. The reason, Surowiecki says, is that pari-mutuel betting is a nearly perfect machine for tapping into the wisdom of the crowd.