Inside Animal Minds: The New Science of Animal Intelligence (7 page)

“If you ever go to the track, you find a really diverse group, experts who spend all day perusing daily race forms, people who know something about some kinds of horses, and others who are betting at random, like the woman who only likes black horses,” he says. Like bees trying to make a decision, bettors gather all kinds of information, disagree with one another, and distill their collective judgment when they place their bets.

That’s why it’s so rare to win on a long shot.

There’s a small park near the White House in Washington, D.C., where I like to watch flocks of pigeons swirl over the traffic and trees. Sooner or later, the birds come to rest on ledges of buildings surrounding the park. Then something disrupts them, and they’re off again in synchronized flight.

The birds don’t have a leader. No pigeon is telling the others what to do. Instead, they’re each paying close attention to the pigeons next to them, each bird following simple rules as they wheel across the sky. These rules add up to another kind of swarm intelligence—one that has less to do with making decisions than with precisely coordinating movement.

Craig Reynolds, a computer graphics researcher, was curious about what these rules might be. So in 1986, he created a deceptively simple steering program called boids. In this simulation, generic birdlike objects, or boids, were each given three instructions: (1) avoid crowding nearby boids, (2) fly in the average direction of nearby boids, and (3) stay close to nearby boids. The result, when set in motion on a computer screen, was a convincing simulation of flocking, including lifelike and unpredictable movements.

At the time, Reynolds was looking for ways to depict animals realistically in TV shows and films. (
Batman Returns
in 1992 was the first movie to use his approach, portraying a swarm of bats and an army of penguins.) Today, he works at Sony doing research for games, such as an algorithm that simulates in real time as many as 15,000 interacting birds, fish, or people.

By demonstrating the power of self-organizing models to mimic swarm behavior, Reynolds was also blazing the trail for robotics engineers. A team of robots that could coordinate its actions like a flock of birds could offer significant advantages over a solitary robot. Spread out over a large area, a group could function as a powerful mobile sensor net, gathering information about what’s out there.
If the group encountered something unexpected, it could adjust and respond quickly, even if the robots in the group weren’t very sophisticated, just as ants are able to come up with various options by trial and error. If one member of the group were to break down, others could take its place. And, most important, control of the group could be decentralized, not dependent on a leader.

“In biology, if you look at groups with large numbers, there are very few examples where you have a central agent,” says Vijay Kumar, a professor of mechanical engineering at the University of Pennsylvania. “Everything is very distributed: They don’t all talk to each other. They act on local information. And they’re all anonymous. I don’t care who moves the chair, as long as somebody moves the chair. To go from one robot to multiple robots, you need all three of those ideas.”

Within five years, Kumar hopes to put a networked team of robotic vehicles in the field. One purpose might be as first responders. “Let’s say there’s a 911 call,” he says. “The fire alarm goes off. You don’t want humans to respond. You want machines to respond, to tell you what’s happening. Before you send firemen into a burning building, why not send in a group of robots?”

Taking this idea one step further, Marco Dorigo’s group in Brussels is leading a European effort to create a “swarmanoid,” a group of cooperating robots with complementary abilities: “foot-bots” to transport things on the ground, “hand-bots” to climb walls and manipulate objects, and “eye-bots” to fly around, providing information to the other units.

The military is eager to acquire similar capabilities. On January 20, 2004, researchers released a swarm of 66 pint-size robots into an empty office building at Fort A. P. Hill, a training center near Fredericksburg, Virginia. The mission: Find targets hidden in the building.

Zipping down the main hallway, the foot-long red robots pivoted
this way and that on their three wheels, resembling nothing so much as large insects. Eight sonars on each unit helped them avoid collisions with walls and other robots. As they spread out, entering one room after another, each robot searched for objects of interest with a small, Web-style camera. When one robot encountered another, it used wireless network gear to exchange information. (“Hey, I’ve already explored that part of the building. Look somewhere else.”)

In the back of one room, a robot spotted something suspicious: a pink ball in an open closet (the swarm had been trained to look for anything pink). The robot froze, sending an image to its human supervisor. Soon, several more robots arrived to form a perimeter around the pink intruder. Within a half hour, all six of the hidden objects had been found. The research team conducting the experiment declared the run a success. Then they started a new test.

The demonstration was part of the Centibots Project, an investigation to see if as many as a hundred robots could collaborate on a mission. If they could, teams of robots might someday be sent into a hostile village to flush out terrorists or locate prisoners; into an earthquake-damaged building to find victims; onto chemical-spill sites to examine hazardous waste; or along borders to watch for intruders. Military agencies such as DARPA (Defense Advanced Research Projects Agency) have funded a number of robotics programs using collaborative flocks of helicopters and fixed-wing aircraft, schools of torpedo-shaped underwater gliders, and herds of unmanned ground vehicles. But at the time, this was the largest swarm of robots ever tested.

“When we started Centibots, we were all thinking, this is a crazy idea, it’s impossible to do,” says Régis Vincent, a researcher at SRI International in Menlo Park, California. “Now we’re looking to see if we can do it with a thousand robots.”

In nature, of course, animals travel in even larger numbers. That’s
because, as members of a big group, whether it’s a flock, school, or herd, individuals increase their chances of detecting predators, finding food, locating a mate, or following a migration route. For these animals, coordinating their movements with one another can be a matter of life or death.

“It’s much harder for a predator to avoid being spotted by a thousand fish than it is to avoid being spotted by one,” says Daniel Grünbaum, a biologist at the University of Washington. “News that a predator is approaching spreads quickly through a school because fish sense from their neighbors that something’s going on.”

When a predator strikes a school of fish, the group is capable of scattering in patterns that make it almost impossible to track any individual. It might explode in a flash, create a kind of moving bubble around the predator, or fracture into multiple blobs, before coming back together and swimming away.

Animals on land do much the same, as Karsten Heuer, a wildlife biologist, observed in 2003, when he and his wife, Leanne Allison, followed the vast Porcupine caribou herd
(Rangifer tarandus granti)
for five months. Traveling more than a thousand miles with the animals, they documented the migration from winter range in Canada’s northern Yukon Territory to calving grounds in Alaska’s Arctic National Wildlife Refuge.

“It’s difficult to describe in words, but when the herd was on the move it looked very much like a cloud shadow passing over the landscape, or a mass of dominoes toppling over at the same time and changing direction,” Karsten says. “It was as though every animal knew what its neighbor was going to do, and the neighbor beside that and beside that. There was no anticipation or reaction. No cause and effect. It just was.”

One day, as the herd funneled through a gully at the tree line, Karsten and Leanne spotted a wolf creeping up. The herd responded with a classic swarm defense.

“As soon as the wolf got within a certain distance of the caribou, the herd’s alertness just skyrocketed,” Karsten says. “Now there was no movement. Every animal just stopped, completely vigilant and watching.” A hundred yards closer, and the wolf crossed another threshold. “The nearest caribou turned and ran, and that response moved like a wave through the entire herd until they were all running. Reaction times shifted into another realm. Animals closest to the wolf at the back end of the herd looked like a blanket unraveling and tattering, which, from the wolf’s perspective, must have been extremely confusing.” The wolf chased one caribou after another, losing ground with each change of target. In the end, the herd escaped over the ridge, and the wolf was left panting and gulping snow.

For each caribou, the stakes couldn’t have been higher, yet the herd’s evasive maneuvers displayed not panic but precision. (Imagine the chaos if a hungry wolf were released into a crowd of people.) Every caribou knew when it was time to run and in which direction to go, even if it didn’t know exactly why. No leader was responsible for coordinating the rest of the herd. Instead, each animal was following simple rules evolved over thousands of years of wolf attacks.

That’s the wonderful appeal of swarm intelligence. Whether we’re talking about ants, bees, pigeons, or caribou, the ingredients of smart group behavior—decentralized control, response to local cues, simple rules of thumb—add up to a shrewd strategy to cope with complexity.

“We don’t even know yet what else we can do with this,” says Eric Bonabeau, a complexity theorist and the chief scientist at Icosystem Corporation in Cambridge, Massachusetts. “We’re not used to solving decentralized problems in a decentralized way. We can’t control an emergent phenomenon like traffic by putting stop signs and lights everywhere. But the idea of shaping traffic as a
self-organizing system, that’s very exciting.”

Social and political groups have already adopted crude swarm tactics. During mass protests eight years ago in Seattle, antiglobalization activists used mobile communications devices to spread news quickly about police movements, turning an otherwise unruly crowd into a “smart mob” that was able to disperse and re-form like a school of fish.

The biggest changes may be on the Internet. Consider the way Google uses group smarts to find what you’re looking for. When you type in a search query, Google surveys billions of Web pages on its index servers to identify the most relevant ones. It then ranks them by the number of pages that link to them, counting links as votes (the most popular sites get weighted votes, because they’re more likely to be reliable). The pages that receive the most votes are listed first in the search results. In this way, Google says, it “uses the collective intelligence of the Web to determine a page’s importance.”

Wikipedia, a free collaborative encyclopedia, has also proved to be a big success, with millions of articles in more than 200 languages about everything under the sun, each of which can be contributed by anyone or edited by anyone. “It’s now possible for huge numbers of people to think together in ways we never imagined a few decades ago,” says Thomas Malone of MIT’s new Center for Collective Intelligence. “No single person knows everything that’s needed to deal with problems we face as a society, such as health care or climate change, but collectively we know far more than we’ve been able to tap so far.”

Such thoughts underline an important truth about collective intelligence: Crowds tend to be wise only if individual members act responsibly and make their own decisions. A group won’t be smart if its members imitate one another, slavishly follow fads, or wait for someone to tell them what to do. When a group is being
intelligent, whether it’s made up of ants or attorneys, it relies on its members to do their own part. For those of us who sometimes wonder if it’s really worth recycling that extra bottle to lighten our impact on the planet, the bottom line is that our actions matter, even if we don’t see how.

Think about a honeybee as she walks around inside the hive. If a cold wind hits the hive, she’ll shiver to generate heat and, in the process, help to warm the nearby brood. She has no idea that hundreds of workers in other parts of the hive are doing the same thing at the same time to the benefit of the next generation.

“A honeybee never sees the big picture any more than you or I do,” says Thomas Seeley, the bee expert. “None of us knows what society as a whole needs, but we look around and say, oh, they need someone to volunteer at school, or mow the church lawn, or help in a political campaign.”

If you’re looking for a role model in a world of complexity, you could do worse than to imitate a bee.

Virginia Morell
is the author of the critically acclaimed
Ancestral Passions: The Leakey Family and the Quest for Humankind’s Beginnings
. A longtime contributor to
National Geographic
and correspondent for the journal
Science
, Morell writes frequently on animal behavior. Her forthcoming
Animal Wise: The Thoughts and Emotions of Our Fellow Creatures
will be published in 2013.

Mary Roach
is the author of the
New York Times
best sellers
Packing for Mars, Stiff, Bonk
, and
Spook. Packing for Mars
is a
New York Times
Editor’s Choice and was chosen as the San Francisco 2011 One City, One Book selection.
Stiff
has been translated into 22 languages, and
Spook
was a
New York Times
Notable Book. In addition to
National Geographic
, Roach has written for
Wired, New Scientist
, the
New York Times Book Review
, and
Outside
. She is a member of the Mars Institute’s Advisory Board, the guest editor of the 2011
Best American Science and Nature Writing
, and a winner of the American Engineering Societies’ Engineering Journalism Award, in a category for which, let’s be honest, she was the sole entrant. More at
www.maryroach.net
.