Dunbar tells the story of two labs that both ran into the same experimental problem: The proteins they were trying to measure were sticking to a filter, making it impossible to analyze the data. “One of the labs was full of people from different backgrounds,” Dunbar says. “They had biochemists and molecular biologists and geneticists and students in medical school.” The other lab, in contrast, was made up of E. coli experts. “They knew more about E. coli than anyone else, but that was what they knew,” he says. Dunbar watched how each of these labs dealt with their protein problem. The E. coli group took a brute-force approach, spending several weeks methodically testing various fixes. “It was extremely inefficient,” Dunbar says. “They eventually solved it, but they wasted a lot of valuable time.”

The diverse lab, in contrast, mulled the problem at a group meeting. None of the scientists were protein experts, so they began a wide-ranging discussion of possible solutions. At first, the conversation seemed rather useless. But then, as the chemists traded ideas with the biologists and the biologists bounced ideas off the med students, potential answers began to emerge. “After another 10 minutes of talking, the protein problem was solved,” Dunbar says. “They made it look easy.”

When Dunbar reviewed the transcripts of the meeting, he found that the intellectual mix generated a distinct type of interaction in which the scientists were forced to rely on metaphors and analogies to express themselves. (That’s because, unlike the E. coli group, the second lab lacked a specialized language that everyone could understand.) These abstractions proved essential for problem-solving, as they encouraged the scientists to reconsider their assumptions. Having to explain the problem to someone else forced them to think, if only for a moment, like an intellectual on the margins, filled with self-skepticism.

This is why other people are so helpful: They shock us out of our cognitive box. “I saw this happen all the time,” Dunbar says. “A scientist would be trying to describe their approach, and they’d be getting a little defensive, and then they’d get this quizzical look on their face. It was like they’d finally understood what was important.”

What turned out to be so important, of course, was the unexpected result, the experimental error that felt like a failure. The answer had been there all along — it was just obscured by the imperfect theory, rendered invisible by our small-minded brain. It’s not until we talk to a colleague or translate our idea into an analogy that we glimpse the meaning in our mistake.

A great write-up explaining *why* it's important to get a diversity of opinions to generate higher quality ideas. In less time too.