Think of computational technologies don't change in bursts. That's what we were trained to think from an equilibrium point of view. They change, i may use the term fractly. And so far, technologies themselves for the structure of the economy Its changing at all levels all the time. All sorts of small changes, incremental and almost fractile, used metaphorically,. So the economy is always changing. Social structures change. History itself, changes what we can do. Changes we cannow fly across the pacific or the atlantic in just a matter of hours. Economic possibilities change. Economics is wonderful at these questions of allocation of time or effort, or allocation of budgets. We've had
Economies in the modern world are incredibly complex systems. But when we sit down to think about them in quantitative ways, it’s natural to keep things simple at first. We look for reliable relations between small numbers of variables, seek equilibrium configurations, and so forth. But those approaches don’t always work in complex systems, and sometimes we have to use methods that are specifically adapted to the challenges of complexity. That’s the perspective of W. Brian Arthur, a pioneer in the field of complexity economics, according to which economies are typically not in equilibrium, not made of homogeneous agents, and are being constantly updated. We talk about the basic ideas of complexity economics, how it differs from more standard approaches, and what it teaches us about the operation of real economies.
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W. Brian Arthur received his Ph.D. in operations research from the University of California, Berkeley. He is currently an External Faculty Member at the Santa Fe Institute, IBM Faculty Fellow, and Visiting Researcher in the Intelligent Systems Lab at PARC. He was formerly the Morrison Professor of Economics and Population Studies and Professor of Biology at Stanford. He is known for developing the theory of increasing returns in economics. Among his awards are a Guggenheim Fellowship, the Schumpeter Prize in economics, and the Lagrange Prize for complexity.
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