Some technologies have remained the same for centuries; others seem to change every five minutes. But how are researchers to know where it’s worth investing time and money in looking for improvements?
MIT engineers think they may have the answer – concentrate on the simple stuff. And they’ve come up with a way of mathematically modeling complexity, breaking a system down into its individual components and then mapping all the interconnections between them.
“It gives you a way to think about how the structure of the technology affects the rate of improvement,” says Jessika Trancik, assistant professor of engineering systems at MIT.
The team was particularly interested in energy-related technologies, ranging from tiny transistors to huge coal-fired powerplants. They tracked how these technologies improve over time, either through reduced cost or better performance, and developed a model to compare this improvement to the complexity of the design and the degree of connectivity among its different components.
The authors say the approach could, for example, help policymakers deal better with climate change, by predicting which low-carbon technologies are likeliest to improve rapidly.
While overall design complexity was the biggest factor in slowing the rate of improvement, the researchers also found that certain patterns of interconnection can create bottlenecks, causing the pace of improvements to come in fits and starts rather than at a steady rate.
Trancik and her colleagues are now moving on to carry out an empirical analysis of many different technologies to gauge how effective the model is in practice.
For now, she suggests, it’s most useful for comparing two different technologies with similar components but different levels ofcomplexity. For example, it 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 Trancik’s team 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,” he says.
“The implications for firms and policymakers [are] that R&D should not only be spent on invention of new technologies, but also on simplifying existing technologies so that humans will learn faster how to improve these technologies.”