Economics is a stately subject, prim and respectable, one that’s altered little since its modern foundations were laid in Victorian times. Now it is changing rapidly, thanks to the work of a small group of researchers over the last two decades in New Mexico.
The story started in 1987, when two Nobel prize winners, economist Kenneth Arrow and physicist Philip Anderson, brought together 10 economists and 10 scientists for a now-famous conference at the new Santa Fe Institute. The purpose was to see how economics could benefit from physics, computer science and biology.
That meeting gave birth to the institute’s first research program, The Economy as an Evolving Complex System, and I was picked to lead it. And that program, in turn, has gone on to lay down a new and different way to look at the economy.
Conventional economics, in 1987 and still today, sees the economy as something like a perfect machine, a system whose players make perfectly rational decisions in perfectly spelled out situations, with all forces matched in equilibrium. This highly ordered approach was necessary to get analytical results, but the results didn’t always look very authentic.
By the late 1980s and early ’90s, we had new mathematical tools, new ideas from cognitive science and computational power at our disposal. The time was right to look at the economy more realistically.
Instead of seeing people in the economy as perfect solvers of perfect problems, our program decided to allow that they might not know what situation they were in and would have to make sense of it. Instead of assuming people were perfectly rational, we allowed there were limits to how smart they were. Instead of assuming the economy was a mechanistic system operating at equilibrium, we saw it as an ecology — of actions, strategies and beliefs competing for survival — perpetually changing as new behaviors were discovered.
Instead of reducing all situations to a simple set of equations, we decided to study them by creating “artificial worlds” — miniature economies within the computer — where the many players would be represented by little computer programs that could explore, respond to the situation they created and get smarter over time.
The result was an approach that saw economic issues as playing out in a system that was realistic, organic and always evolving.
Did this new approach make a difference? It did. Our artificial-worlds-in-the-computer approach, along with the work of others, in time became agent-based modeling, now a much-used method in all the social sciences.
Our emphasis on nonequilibrium allowed economics to rigorously study how patterns form and evolve. Standard equilibrium economics is excellent at studying static outcomes, how the various incentives of various players translate into a consistent pattern. But it is woefully bad at figuring out how patterns form and change over time, and this is what our approach could supply.
We were also able to explain phenomena standard economics couldn’t easily explain, such as periods of quiescence and volatility in stock markets. In conventional economics, all behavior is in equilibrium, so calmness always rules. In our approach, some investors might make small adjustments in their predictions, which might perturb the overall pattern so that other investors might have to change their predictions to readapt. Cascades of mutual adjustment could ripple through the system. The result is periods of tranquility, followed randomly by periods of spontaneously generated perturbation — quiescence and volatility. Exactly like the real stock market.
Similarly, standard economics can’t easily explain bubbles and crashes. At equilibrium, such disturbances are irrational and by definition ruled out. In our approach, some market investors might spontaneously forecast upward price movement and buy in, causing a small price rise. This might confirm the upward predictions of other investors, and they buy in. The result is a bubble, or on the way down, a crash. Again, events trigger other events, causing random temporary changes in the economy.
None of this meant that our methods were easily accepted into economics, and the field’s mainstream didn’t exactly throw open its doors to us. This changed in 2008 and 2009, after the economic meltdown. Indeed, that meltdown itself was a perfect example of what we were talking about. The collapse of Lehmann Brothers triggered further collapses that triggered still further collapses and eventually brought about a severe recession. The result, observed The Economist magazine wryly, was not just the collapse of the financial system, but the collapse of standard economics. Something different was needed.
Our approach, which has come to be called Complexity Economics, suddenly looked a great deal more relevant. It wasn’t quite a new set of methods, it wasn’t a new set of theories and it certainly wasn’t a new ideology. Really, it was a different way to see the economy. It viewed the economy not as static, perfect, pure and rational, but as messy, alive, organic and always constructing itself. It saw the economy as never at rest, but as always discovering, always in perpetual novelty.
In 1996, a historian of economic thought, David Colander, put forward an allegory in which economists a century ago stood at the base of two mountains whose peaks were hidden in the clouds. They wanted to climb the higher peak and had to choose one of the two. They chose the mountain of well-definedness and mathematical order, only to see when they had worked their way up and finally got above the clouds that the other mountain, the one of process and organicism, was far higher.
Many other economists besides our New Mexican group have started to climb that other mountain in the last few years. We have much to discover.
W. Brian Arthur is an external professor at the Santa Fe Institute and a visiting researcher in the Intelligent Systems Lab at the Palo Alto Research Center in California. He has served on the Santa Fe Institute’s Science Board and board of trustees. Formerly at Stanford, he is the recipient of the inaugural Lagrange Prize in Complexity Science and the International Schumpeter Prize in Economics.
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