A new study by researchers at MIT and other institutions shows that it may be possible to predict which technologies are likeliest to advance rapidly, and therefore may be worth more investment in research and resources.
The researchers found that the greater a technology’s complexity, the more slowly it changes and improves over time. They devised a way of mathematically modeling complexity, breaking a system down into its individual components and then mapping all the interconnections between these components.
The method is most useful for comparing two different technologies “whose components are similar, but whose design complexity is different.” For example, the analysis could be used to compare different approaches to next-generation solar photovoltaic cells, she says. The method can also be applied to processes, such as improving the design of supply chains or infrastructure systems. “It can be applied at many different scales,” she says.
Koen Frenken, professor of economics of innovation and technological change at Eindhoven University of Technology in the Netherlands, says this paper “provides a long-awaited theory” for the well-known phenomenon of learning curves. “It has remained a puzzle why the rates at which humans learn differ so markedly among technologies. This paper provides an explanation by looking at the complexity of technology, using a clever way to model design complexity.”
Ultimately, the kind of analysis developed in this paper could become part of the design process — allowing engineers to “design for rapid innovation,” Trancik says, by using these principles to determine “how you set up the architecture of your system.”
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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