This image was created by Tatiana Ivanov

There are two major reasons for the slow adoption of crowdsourcing as a practical problem-solving tool.

The first is a widespread, often completely paralyzing uncertainty over which problem crowdsourcing can (or can’t) solve.

The second is the lack of trust in the intellectual power of the crowd, its ability to tackle complex technical or business problems. Everyone would seem to agree that the proverbial wisdom of crowds can be applied to a “simple” task, such as creating a corporate logo or coming up with a fancy name for yet another Frappuccino du jour. However, when it comes to answering a question that requires specialized knowledge, firms prefer to turn to experts.

“Crowds of Amateurs”

This preference obviously sits well with the experts themselves. They dislike the very idea that someone with no immediate experience in the field can solve a problem that they failed to. This sentiment was eloquently summarized in a 2010 article: “Our trust in the expert appears to be increasingly supplanted by a willingness to rely on the knowledge derived from crowds of amateurs.”

“Crowds of amateurs.” Harsh words, huh?

Pitting experts against crowds is plain silly. Experts represent an essential part of any problem-solving process; in fact, this process is impossible without experts. Only experts can identify and properly formulate problems that need to be solved. Only experts can assess the value of incoming solutions and select those that make sense. Only experts can successfully integrate external information into the knowledge available in-house. It’s only at this midpoint of a problem-solving campaign — at the stage of generating potential solutions to the problem — that crowds are usually superior to experts.

What Does It Means to Be an Expert (in Mice)?

Why are crowds usually more creative than experts? A 2019 neurobiological study may provide a clue. A research team from Cold Spring Harbor Laboratory has set out to analyze neuronal activity in the brains of mice forced to learn new decision-making skills.

As the mice progressed through learning new tricks, more and more neurons in their brains got activated. However, the neuron activity rapidly became very selective: individual neurons responded only when the mice made one choice and not another. This pattern became even stronger as the mice learned how to do a task better (that is, become “experts” in this task). Moreover, when the expertise was fully achieved, the mouse brain was ready for that decision even before the mouse began executing the task.

In other words, the “expert” mice knew how to solve the problem even before they started to solve it! In contrast, the neuronal activities in the brains of “non-expert” mice remain non-selective — meaning that the mice would approach the task with an “open mind.”

Were these findings directly applicable to humans, the implication would be that experts approach the problem with the patterns that are already pre-formed in their brains by prior experience. In contrast, amateurs may approach the problem from a completely different angle — and the more amateurs are involved in solving the problem, the more chance that a completely novel, unorthodox solution could be found.

Does Practice Make You Better at Solving Problems? It Should. But It Doesn’t

I’m usually among the first to argue that discoveries made in mice may have little relevance to humans. Examples of clinical trials in which a drug that was effective in mice would flop spectacularly in humans are so numerous that they could fill a warehouse as big as N.Y. Grand Station.

And yet, some indications that expertise may come at the expense of lost creativity in humans have emerged too.

A research team at Stanford conducted a study of how repetitions — that is, continued efforts over time — affected two types of creativity: divergent and convergent thinking. Divergent thinking is the kind of thinking process that involves branching off from what a person knows to come up with new ideas; it’s divergent thinking that is utilized in idea-generating sessions. In contrast, convergent thinking requires finding linkage between different existing concepts or ideas and connecting them to context; it’s convergent thinking that is usually associated with expertise.

What the researchers found was that regular brainstorming sessions didn’t improve the efficiency of divergent thinking: not only test subjects didn’t generate more unique ideas over time; the novelty of these ideas — a measurement of a degree to which test subjects’ inspiration departs from convention — actually decreased over time.

At the same time, test subjects charged with convergent creativity tasks were getting better and better over time, increasing their productivity while performing the task.

It appears that practice improves performance by reinforcing certain cognitive pathways in the brain, making them more accessible, but, at the same time, de-emphasizing other pathways, cutting them off in order to allocate an optimal amount of cognitive resources to the prioritized task. In other words, by training the brain to become more efficient and focused, the repetition also results in a less flexible brain.

(The authors of the Stanford study didn’t perform analysis of the neuronal activity in the brains of their text subject, but I suspect they would have revealed the same pattern of activation and selection that was shown in mice.)

The Last Word

I can only repeat: It’s plain silly to pit experts against crowds. Moreover, there is even no sense in discussing which tool, experts or crowds, is better. Both are different, complementary tools in the modern innovation toolbox, and each should be used at its proper time and place.

As a rule of thumb, when solving a problem similar to one that the firm faced in the past, the firm should engage experts. However, if the problem is novel and may require a fresh look at it, engaging crowds would be a better choice.

Isn’t it simple?