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

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      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

      

      In early 2005, Jeff Severts, a vice president at Best Buy, decided to try something different. Severts had recently attended a talk by James Surowiecki, whose bestseller The Wisdom of Crowds claims that, under the right circumstances, groups of nonexperts can be remarkably insightful. In some cases, Surowiecki argues, they can be even more intelligent than the most intelligent people in their ranks. Severts wondered if he might be able to tap into such braininess at Best Buy. As an experiment, in late January 2005 he sent e-mails to several hundred employees throughout the company, asking them to predict sales of gift cards in February. He got 192 replies. In early March, he compared the average of these estimates to actual sales for the month. The collective estimate turned out to be 99.5 percent accurate—almost 5 percent better than the figure produced by the team responsible for sales forecasts.

      “I was surprised at how eerily accurate the crowd’s estimates were,” Severts says.

      In his book about smart crowds, Surowiecki cites similar examples of otherwise ordinary people making extraordinary decisions. Take the quiz show Who Wants to Be a Millionaire? Contestants stumped by a question are given the option of telephoning an expert friend for advice or of polling the studio audience, whose votes are averaged by a computer. “Everything we know about intelligence suggests that the smart individual would offer the most help,” Surowiecki writes. “And in fact the ‘experts’ did okay, offering the right answer—under pressure—almost 65 percent of the time. But they paled in comparison to the audiences. Those random crowds of people with nothing better to do on a weekday afternoon than sit in a TV studio picked the right answer 91 percent of the time.”

      Although Surowiecki readily admits that such stories by themselves don’t amount to scientific proof, they do raise a good question: If hundreds of bees can make reliable decisions together, why should it be so surprising that groups of people can too? “Most of us, whether as voters or investors or consumers or managers, believe that valuable knowledge is concentrated in a very few hands (or, rather, in a very few heads). We assume that the key to solving problems or making good decisions is finding that one right person who will have the answer,” Surowiecki writes. But often that’s a big mistake. “We should stop hunting and ask the crowd (which, of course, includes the geniuses as well as everyone else) instead. Chances are, it knows.”

      Severts was so impressed by his first few efforts to harness collective wisdom at Best Buy that he and his team began experimenting with something called prediction markets, which represent a more sophisticated way of gathering forecasts about company performance from employees. In a prediction market, an employee uses play money to bid on the outcome of a question, such as “Will our first store in China open on time?” A correct bid pays $100, an incorrect bid pays nothing. If the current price of a share in the market for a bid that yes, the store will open on time, is $80, for example, that means the entire group believes there’s an 80 percent chance that that will happen. If an employee is more optimistic, believing there’s a 95 percent chance, he might take the bet, seeing an opportunity to earn $15 per share. In the case of the new store, which had been scheduled to open in Shanghai in December 2006, the prediction market took a dive, falling from $80 a share to $50 eight weeks before the opening date—even though official company forecasts at the time were still positive. In the end, the store opened a month late.

      “That first drop was an early warning signal,” Severts says. “Some piece of new information came into the market that caused the traders to radically change their expectations.” What that new information might have been about, Severts never found out. But to him it didn’t really matter. The prediction market had proved its ability to overcome the many barriers to effective communication in a large company. If anyone was listening, the alarm bells were ringing loud and clear.

      As this story suggests, there may be several good reasons for companies to pay attention to prediction markets, which are good at pulling together information that may be widely scattered throughout a corporation. For one thing, they’re likely to provide unbiased outlooks. Since bids are placed anonymously, markets may reflect the true opinions of employees, rather than what their bosses want them to say. For another thing, they tend to be relatively accurate, since the incentives for bidders to be correct—from T-shirts to cash prizes—encourage them to get it right, using whatever unique resources they might have.

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