Smart Swarm: Using Animal Behaviour to Organise Our World. Don Tapscott

research on the site being advertised, and if they liked what they saw, they could buy into the site by dancing for it themselves. If they didn’t like it, they didn’t have to. The more bees that joined in, the greater the likelihood the site would be selected.

      But how did the process work, exactly? What were the mechanisms that enabled the bees to choose so accurately?

      To find out, Seeley and Visscher conducted a series of experiments. After preparing a swarm for house hunting, they placed five plywood nest boxes an equal distance from the bees on Appledore Island—four representing mediocre choices for a new nest and one that was excellent. What made the fifth box better than the rest was that it offered the bees an ideal amount of living space—about forty quarts, compared to fifteen quarts for the others, which was not enough to store honey, raise brood, and meet the other needs of an expanding colony. To track the bees during the decision-making process, Seeley and Visscher labeled all four thousand individuals in each swarm with tiny numbered disks on their thoraxes and dabs of paint on their abdomens, a tedious process that involved chilling batches of twenty bees at a time to render them docile enough to be handled. But it was worth it in the end, because, when they looked at video tapes of the swarms later, they could tell which bees had visited which nest boxes and which ones had danced for which boxes at the main cluster. The shape of the decision-making process emerged.

      The key, it turned out, was the brilliant way the bees exploited their diversity of knowledge—the second major principle of a smart swarm. Just as Deborah Gordon’s ant colonies used self-organization to adjust to changes in the environment, so the honeybees used diversity of knowledge to make good decisions. By diversity of knowledge, in this case, I mean a broad sampling of the swarm’s options. The more choices, the better. By sending out hundreds of scouts at the same time, each swarm collected a wealth of information about the neighborhood and the nest boxes, and it did so in a distributed and decentralized way. None of the bees tried to visit all five of the boxes to rate which one was the best. Nor did they submit their findings to some executive committee for a final decision, as workers in a corporation might do. Instead, these hundreds of scouts each provided unique information about the various sites to the group as a whole in what Seeley and Visscher described as a “friendly competition of ideas.”

      Equally important, every scout evaluated nest sites for herself. If a scout was impressed by another scout’s dance, she might fly to the box being advertised and conduct her own inspection, which could last as long as an hour. But she would never blindly follow another scout’s opinion by dancing for a site she hadn’t visited. That would open the door to untested information being spread like a rumor. Or, to use the stock-broker analogy, a bee wouldn’t invest in a company just because its stock was on the rise. She’d check out its fundamentals first.

      Meanwhile, as the scout bees continued their search, the swarm was busily ranking each option. This was determined by the number of bees visiting each site. The more visitors, the more “votes” for the site. Though the best nest box wasn’t discovered first by the scouts, it quickly attracted the attention of numerous bees. Scouts returning from the excellent box had no trouble convincing others to check it out, largely because they danced for it so vigorously—performing as many as a hundred dance circuits each, compared to only a dozen or so danced by bees for lesser sites. A dance of that length could take five minutes, compared with thirty seconds for a shorter dance, so it was much more likely to be noticed by scouts walking around on the surface of the cluster. And once the number of bees advertising the best box increased, support for it shot up, as interest in the mediocre sites faded away.

      “This careful tuning of dance strength by the scouts created a powerful positive feedback,” Seeley explained, “which caused support for the best site to snowball exponentially.” This was a crucial mechanism, because it meant that even small differences in the quality of nests were exaggerated—their “signals” were amplified—making it much more likely that support for the best site would surge ahead.

      As more and more bees gathered at the first-rate box, fewer and fewer lingered at the others. That was because scouts returning from boxes for the second or third time were dancing fewer circuits for them each time, whether they’d visited the excellent box or the mediocre ones. Scouts that had visited poor sites quit dancing first. Seeley and Visscher described this mechanism as the dance “decay rate.” It meant that support for less attractive boxes would dwindle automatically—even as the number of bees collecting at the superior box kept growing—in a decision-making process that lasted from two to five hours during the test. In technical terms, this represented a balancing, or negative feedback, preventing the swarm from choosing too fast and making a mistake. These were the factors steering the bees’ problem-solving machine—exponential recruitment on the accelerator, dance decay rate on the brakes.

      Meanwhile, something critical was happening at the nest boxes. As soon as the number of bees visible near the entrance to the best box reached fifteen or so, Seeley and Visscher noticed a new behavior among the scouts. Those returning from the box started plowing through bees in the main cluster, producing a special signal called “worker piping.”

      “It sounds like nnneeeep, nnneeeep! Like a race car revving up its engine,” Seeley says. “It’s a signal that a decision has been reached and it’s time for the rest of the swarm to warm up their wing muscles and prepare to fly.” Scouts from the excellent box, in other words, were announcing that a quorum had been reached. Enough bees had “voted” for the most attractive box by gathering there at the same time. A new home had been chosen.

      The number fifteen, it turns out, was the threshold level for the quorum. Although this number might seem arbitrary at first glance, it turns out to be anything but that. Like the dance decay rate, the threshold level represents a finely tuned mechanism of emergence. To gather that many bees at the entranceway simultaneously, it takes as many as 150 scouts traveling back and forth between the box and the main swarm cluster, which means that a majority of the bees taking part in the selection process have committed themselves to the site.

      Once the quorum was reached, the final step was for scouts to lead the rest of the group to the chosen site. Most of the swarm, some 95 to 97 percent, had been resting during the whole decision-making process, conserving their energy for the work ahead. Now, as the scouts scrambled through the crowd, they stopped from time to time to press their thoraxes against other bees to vibrate their wing muscles, as if to say, warm up, warm up, get ready to fly. A final signal, called the buzz run, in which the scouts bulldoze through sleepy workers and buzz their wings dramatically, triggered the takeoff. At that point, the whole swarm flew away to its new home—which, to nobody’s surprise, turned out to be the best nest box.

      The swarm chose successfully, in short, because it made the most of its diversity of knowledge. By tapping into the unique information collected by hundreds of scouts, it maximized its chances of finding the best solution. By setting the threshold level high enough to produce a good decision, it minimized its chances of making a big mistake. And it did both in a timely manner under great pressure to be accurate.

      The swarm worked so efficiently, in fact, you might be tempted to imagine it as a complicated Swiss watch, with hundreds of tiny parts, each one smoothly performing its function. Yet the reality is much more interesting. To watch a swarm in the midst of deliberation is to witness a chaotic scene not unlike the floor of a commodities market, with dozens of brokers shouting out orders at the same time. Bees coming and going. Scouts dancing this way or that. Uncommitted bees milling around. The way they make decisions looks very messy, which is also very beelike. Natural selection has fashioned a system that is not only tailor-made for their extraordinary talents for cooperation and communication but also forgiving of their tendency to be unpredictable. It is from this controlled messiness that the wisdom of the hive emerges.

      Seek a diversity of knowledge. Encourage a friendly competition of ideas. Use an effective mechanism to narrow your choices. These are the lessons of the swarm’s success. They also happen be the same rules that enable certain groups of people to make smart decisions together—from antiterrorism teams to engineers in aircraft factories—through a surprising phenomenon that has come to be known as the “wisdom of crowds.”

      The Wisdom of Crowds