Crowdsourcing — posing a question or asking for help from a large group of people — has allowed many problems to be solved, like scan for new galaxies and climate modeling, that would be impossible for experts alone..

But what if the crowd was asked to decide what questions to ask in the first place?

University of Vermont researchers Josh Bongard and Paul Hines decided to explore  that question by seeing if volunteers who visited two different websites could pose, refine, and answer questions of each other that could predict the volunteers’ body weight and home electricity use.

Crowd-sourced predictive models

The resulting self-directed questions and answers by visitors to the websites led to computer models that effectively predict a user’s monthly electricity consumption and body mass index. The results, were published in “Crowdsourcing Predictors of Behavioral Outcomes” in IEEE Transactions: Systems, Man and Cybernetics.

“It’s proof of concept that a crowd actually can come up with good questions that lead to good hypotheses,” says Bongard, an expert on machine science.

In other words, the wisdom of the crowd can be harnessed to create a crowd-sourced predictive model that can determine which variables to study, the UVM project shows — and at the same time provide a pool of data by responding to the questions they ask of each other.

“Going forward, this approach may allow us to involve the public in deciding what it is that is interesting to study,” says Hines. “It’s potentially a new way to do science.”

And there are many reasons why this new approach might be helpful. In addition to forces that experts might simply not know about — “can we elicit unexpected predictors that an expert would not have come up with sitting in his office?” Hines asks — experts often have deeply held biases.

The UVM team primarily sees their new approach as potentially helping to accelerate the process of scientific discovery. The need for expert involvement — in shaping, say, what questions to ask on a survey or what variable to change to optimize an engineering design — “can become a bottleneck to new insights,” the scientists write.

The goal: “exponential rises” in the discovery of what causes behaviors and patterns — probably driven by the people who care about them the most. For example, “it might be smokers or people suffering from various diseases,” says Bongard. The team thinks this new approach to science could “mirror the exponential growth found in other online collaborative communities,” they write.

Case in point: expert comments on the KurzweilAI website are often helpful in developing story ideas. — Ed.