Let the Algorithms Learn

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Don't try to teach them at all. Let the algritms learn. They're much better at it than we are at teaching them. Do i remember correctly that somewhere you mentioned that there was a kind of phase transition, a as a function of how much experimentation you allowed the algarithms to do. If they were only allowed to do a little bit, they never found a better solution. But if you let all the algaritms experiment a lot, a whole bunch of new things could happen. Yes. This beautiful work done by robert lucas in the late 19 seventies became a kind of standard in economics. Looks quite realistic.

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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