Innovation (here defined as a lasting novelty or invention) might be understood as a search process in a space of combinatorial possibilities. This week at SFI, a hand-picked group of engineers, biologists, economists, and innovation experts are exploring how to transform this notion into formal – even predictive – models.
The working group is sponsored by the Arizona State University-SFI Center for Biosocial Complex Systems and is funded by the National Science Foundation.
It builds on an SFI workshop last fall that explored the drivers of invention and the challenges of building a theory of innovation, including how to identify and measure novelty.
“With universal problems, such as innovation, we need a broad interdisciplinary discussion to grasp the fundamental nature of the problem so that we can (hopefully) arrive at deep theoretical insights,” says SFI External Professor Manfred Laubichler, who co-organized the meeting with SFI collaborator Jose Lobo. “And we need those [insights] to have real-world impacts.”
A few participants have taken steps, using data on patented inventions or cell metabolic pathways to build empirical search spaces. The hope is that a general framework for innovation might arise from similarities the participants and others observe in their separate search spaces.
Lobo points to Darwin’s Theory of Evolution as a potential framework. If we can begin to build a similarly general Theory of Innovation, he says, “it will help us explain how things emerge and thrive.”
Some think the very nature of innovation obviates prediction. Still, certain features of companies and societies make them more inventive and innovative than others, Lobo says. Whether such a theory is possible will be much discussed, but “all agree, in the absence of a good theory, we won’t be able to make predictions,” he says.
More about the working group here.