to order and something new emerges: a pattern, a decision, a structure, or a change in direction. This whole chapter, in fact, has been about the kinds of strategies that emerge from self-organized behavior. And what these strategies all have in common is that they represent a way to cope with the unpredictable.
Consider life in an ant colony, where survival means competing not only against other colonies but also against an ever-changing environment. Will there be enough food today? Where will it be found? How will the weather affect the nest? The colony meets such challenges through self-organized behavior, and what emerges is a pattern of activity that allocates the colony’s resources to meet its immediate needs.
Air Liquide, for its part, had its own list of unknowns. Which customers would need deliveries today? What types of gas would they need? Which production facilities could make those gases at the least cost? What would the price of electricity be at those facilities? How could the company deliver those gases most economically? By emulating an ant colony’s distributed problem-solving approach, the company’s optimizer tool provided a day-to-day plan to cope with an endless string of variables.
Like many businesses today, Air Liquide was looking for a way to cope with the perpetual novelty of its environment. The company didn’t expect a guarantee, that it would win every competition it got into, just an opportunity to stay in the game until it could adapt to the latest changes. What it needed, in other words, was a strategy to gain a degree of control over the uncontrollable—which was what Samuel’s checkers player also seemed to promise.
That was quite different, in an important way, from what Deborah Gordon’s ant colonies were trying to do. Instead of attempting to outsmart the desert environment, the ants, in a sense, were matching its complexity with their own. If Colony 550 were to play a game of checkers, each piece on the board would move by itself, acting on local information, with nobody waiting for instructions. The game would be a swirl of motion as pieces moved forward, jumped over one another, became kings, or got taken as prisoners in patterns of interactions that might be difficult to perceive at first glance. But if checkers were as important to ants as foraging, the colony, without doubt, would be a flexible and resilient competitor.
This tension between minimizing uncertainty, on the one hand, and experimenting to keep up with change, on the other, is something we’ll see time and again throughout this book. And what’s surprising about the behavior evolved by bees, birds, and fish, among other species, is the adroit way that groups of such animals manage to have it both ways—to manage complexity and to partake of it at the same time.
“If I was in charge of designing the software for a company like Air Liquide, I’d probably be stressed about doing a really great job,” Gordon says. “But the ants aren’t doing that.” Their system’s too loose and undisciplined. Information coming in is too spotty, and their responses are too unpredictable. “The amazing thing to me is how, every way you look at it, the ants’ system is so messy, and yet somehow it works,” she says.
Maybe there’s a deeper lesson here, Gordon suggested. “Instead of trying to keep fine-tuning a system so it will work better and better, maybe what we really ought to be looking for is a rigorous way of saying, okay, that’s good enough.” Maybe a smart way to face the unpredictable, whether you’re running a business or playing a game of checkers, is to look for that balance between strategic goals and random experimentation. Ant colonies, after all, manage to thrive at the edge between efficiency and utter chaos, she says. “The question is, how do they find that edge? Because if we could find that edge too, we could save ourselves a lot of trouble.”
2 HONEYBEES Making Smart Decisions
Appledore Island is a tough place for honeybees.
Anchored in the Atlantic off the coast of southern Maine, the rocky, wind-blown island is barely a half-mile long, with hardly any trees, which the bees need for nest building. In fact, you might describe the island as a kind of bee Alcatraz, which makes it an ideal place to observe their behavior under controlled conditions.
A few summers ago, biologists Thomas Seeley of Cornell University and Kirk Visscher of the University of California at Riverside ferried a half-dozen colonies of honeybees to Appledore, which is home to the Shoals Marine Laboratory run by Cornell. For nearly a decade, Seeley and Visscher have been studying a fascinating example of what they call “animal democracy.” How do several thousand honeybees, they want to know, put aside their differences to reach a decision as a group?
The focus of their research has been honeybee “house hunting.” In late spring or early summer, as a large hive outgrows its nest, the group normally divides. The queen and roughly half of the bees fly off in a swarm to create a new colony, leaving behind a daughter queen in the old nest. There may be fifteen thousand bees in the swarm, which typically clusters on a tree branch, while several hundred scout bees search the neighborhood for new real estate. Although the queen’s presence is important to bees in the swarm, she plays no role in picking a new nest site. That task is delegated to the scouts, who do their jobs without direction from a leader.
When a scout buzzes off into the countryside, she’s looking for just the right dwelling place (I say she, because worker honeybees are all females). It must be well off the ground, with a small entrance hole facing south and enough room inside to allow the colony to grow. If she finds such a spot—a hollow in a tree would be perfect—she returns to the swarm and reports her discovery by doing a waggle dance. This dance, which resembles the one forager bees do when they locate a new patch of flowers, contains a code telling others how to find the site. Some of the scouts that see her dance will then go examine the site for themselves, and, if they agree with her assessment, they’ll return to the swarm and dance in support of the site, too.
This is no trivial question for the bees. As long as the swarm is clinging to the branch, it remains exposed to weather, predators, and other hazards. But once the swarm selects a new home, it won’t move again until next spring. So it has to get it right the first time. If the group selects poorly, the entire colony could perish.
One by one, scouts that have been exploring the neighborhood return to the cluster with news about different locations. Soon there’s a steady stream of bees flying between the cluster and a dozen or more potential nest sites, as more and more scouts get involved in the selection process. Eventually, after enough scouts have inspected enough sites, it becomes clear that traffic at one site is much greater than that at any other, and a decision is reached. The bees in the main cluster warm up their wings and fly off together to the chosen site—which almost always turns out to be the best one.
Facing a life-or-death situation, in other words, a honeybee swarm engages in a complex decision-making process involving multiple, simultaneous interactions between hundreds of individuals with no leadership at all—exactly the kind of chaotic, unpredictable enterprise that, if attempted by people under stress, would almost certainly lead to disaster. Yet the bees almost always make the right choice.
How do they do it?
The Five-Box Test
One spring day in 1949, a young zoologist named Martin Lindauer was observing a swarm of bees near the Zoological Institute in Munich, Germany, when he noticed something odd. Some of the bees, he realized, were doing waggle dances. Ordinarily that meant they were foragers that had found a nice patch of flowers nearby, and they were telling other bees where to go find it. But these dancers weren’t carrying any pollen or nectar, so Lindauer didn’t think they were foragers. What were they up to?
Lindauer’s
