The Evolution of Technology
Van Leeuwenhoek Lecture on BioScience.
Drinks after the lecture
Doyne Farmer studied Physics at Stanford University and obtained his PhD (Physics) at the University of California.
At present he is Director of the Institute for New Economic Thinking at the Oxford Martin School and professor of Complexity Economics at this Institute and also professor of Mathematics at the Mathematical Institute (Oxford University). Moreover he is external professor at the Santa Fe Institute (USA).
From 2010 – 2012 he was a Distinguished Fellow at the Potsdam Institute of Climate Change.
His interests are: Complex systems and economics with applications to systemic risk, sustainability and technological innovation. He received many awards and fellowships (J. Robert Oppenheimer Fellowship, Los Alamos National Laboratory Fellow Prize, Alexander von Humboldt Award).
Amongst his recent grants, those from the National Science Foundation (USA), Institute for New Economic Thinking, Alfred P. Sloane Foundation, European Commission FP7 CRISIS and GROWTHCOM, U.S. Dept. of Energy, Solar Energy Technologies Office.
Technological progress is the ultimate driver of economic growth, and forecasting technological progress is one of the pivotal issues for climate mitigation. While there is a rich anecdotal literature for technological change, there is still no overarching theory. Technology evolves under descent with variation and selection, but under very different rules than in biology. The data available to study technology are also very different; On the one hand we have historical examples giving the performance of a few specific technologies over spans of centuries, on the other hand, the collection of information is much less systemic than it is for fossils. There is no good taxonomy, so in a sense the study of technological evolution is pre-Linnean. This may be due to the complexities of horizontal information transfer, which plays an even bigger role for technology than it does for bacteria. There are nonetheless empirical laws for predicting the performance of technologies, such as Moore’s law and Wright’s law, that can be used to make quantitative distributional forecasts and address questions such as “What is the likelihood that solar energy will be cheaper than nuclear power 20 years from now?” I will discuss the essential role of the network properties of technology, and show how 220 years of US patent data can be used as a “fossil record” to identify technological eras. Finally I will discuss new approaches for understanding technological progress that blend ideas from biology and economics.