Technology and Combinatorial Evolution
published: April 3, 2017, recorded: March 2017, views: 1803
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Arthur's long-term fascination has been with constructing an economics that is more realistic. Standard economics has long been based on the idea of hyper-rational actors operating in a static equilibrium world. Now other approaches are coming to the fore and economics is in the midst of a radical remaking. In the late-1980s, Arthur and his group at the Santa Fe Institute began developing an alternative view that's come to be called "complexity economics." This economics assumes that the economy is not a perfecty balanced machine, but an evolving complex system. Actors in the economy do not necessarily face well-defined problems or use super-human rationality in making their decisions. They explore, try to make sense, and react and re-react to the outcomes they together create. Viewed this way, the economy is not in stasis, but always forming, always evolving, always "discovering" fresh novelty. Bubbles and crashes happen, markets can be "gamed" or exploited, and history and institutions matter. The result is a rigorous but realistic picture of the economy.
In the mid-1990s, Arthur became intrigued with questions about technology. Where do new technologies come from—and how exactly does invention work? What constitutes innovation and how is it achieved? Why are certain regions–Silicon Valley for example–hotbeds of innovation, while others languish? Does technology, like biological life, evolve from earlier forms? And how does the economy itself emerge from its technologies? The book Arthur published in 2009, The Nature of Technology: What it Is and How it Evolves, (below) attempts to answer these questions. It argues that all technologies share certain principles; these determine the character of technology and how novel technologies come into being —and hence how innovation works.
In the 1980s Arthur developed a theoretical framework for economics under increasing returns, in particular studying the dynamics of lock-in to one of many possible outcomes under the influence of small, random events. (Several of his papers are collected in his 1994 book Increasing Returns and Path Dependence in the Economy. ) High tech operates under increasing returns, and so here network effects and lock-in are important.
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