Joel Chan and his mentor Christian Schunn of Pitt's Learning Research and Development Center, has said that to solve a problem the idea should not be taken randomly by asking different people but rather people who have closely relate to the problem one is trying to solve.
Chan and Schunn, have collected data through OpenIDEO, a web-based crowdsourced innovation platform intended to help people address a wide range of social and environmental problems like human rights violations and job growth for youth.
The process took up to 10 weeks and occupied more than 350 participants and thousands of ideas. Creativity studies had many participants solve 'toy' problems or observe few participants solving real problems in their study they had both, lending greater strength to their conclusions.
The team has collected its data at the conclusion of the OpenIDEO process. They then entered it into an algorithm to determine whether an idea was near to or far from the posted problem. This algorithm was first vetted against human judgments and proved to be quite good at determining idea distance. Then, the outcomes of the model proved adept at predicting the OpenIDEO experts' shortlist and found that the vast majority of ideas that made the list were closely related to the posted problem, Schunn says.
Chan has said that instead of seeing a bigger effect of far inspirations they saw that ideas built on source ideas more closely related to the problem tended to be selected more often and the same pattern across 12 very different problems-ranging from preventing human rights violations to fostering greater connectedness in urban communities to improving employment prospects for young people.