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By Root 1001 0

the edges of mankind’s view of the future ever since. For philosophers, the demon was controversial because in reducing the prediction of the future to a mechanical exercise, it seemed to rob humanity of free will. As it turned out, though, they needn’t have worried too much. Starting with the second law of thermodynamics, and continuing through quantum mechanics and finally chaos theory, Laplace’s idea of a clockwork universe—and with it the concerns about free will—has been receding for more than century now. But that doesn’t mean the demon has gone away. In spite of the controversy over free will, there was something incredibly appealing about the notion that the laws of nature, applied to the appropriate data, could be used to predict the future. People of course had been making predictions about the future since the beginnings of civilization, but what was different about Laplace’s boast was that it wasn’t based on any claim to magical powers, or even special insight, that he possessed himself. Rather it depended only on the existence of scientific laws that in principle anyone could master. Thus prediction, once the realm of oracles and mystics, was brought within the objective, rational sphere of modern science.

In doing so, however, the demon obscured a critical difference between two different sorts of processes, which for the sake of argument I’ll call simple and complex.8 Simple systems are those for which a model can capture all or most of the variation in what we observe. The oscillations of pendulums and the orbits of satellites are therefore “simple” in this sense, even though it’s not necessarily a simple matter to be able to model and predict them. Somewhat paradoxically, in fact, the most complicated models in science—models that predict the trajectories of interplanetary space probes, or pinpoint the location of GPS devices—often describe relatively simple processes. The basic equations of motion governing the orbit of a communications satellite or the lift on an aircraft wing can be taught to a high-school physics student. But because the difference in performance between a good model and a slightly better one can be critical, the actual models used by engineers to build satellite GPS systems and 747s need to account for all sorts of tiny corrections, and so end up being far more complicated. When the NASA Mars Climate Orbiter burned up and disintegrated in the Martian atmosphere in 1999, for example, the mishap was traced to a simple programming error (imperial units were used instead of metric) that put the probe into an orbit of about 60km instead of 140km from Mars’s surface. When you consider that in order to get to Mars, the orbiter first had to traverse more than 50 million kilometers, the magnitude of the error seems trivial. Yet it was the difference between a triumphant success for NASA and an embarrassing failure.

Complex systems are another animal entirely. Nobody really agrees on what makes a complex system “complex” but it’s generally accepted that complexity arises out of many interdependent components interacting in nonlinear ways. The U.S. economy, for example, is the product of the individual actions of millions of people, as well as hundreds of thousands of firms, thousands of government agencies, and countless other external and internal factors, ranging from the weather in Texas to interest rates in China. Modeling the trajectory of the economy is therefore not like modeling the trajectory of a rocket. In complex systems, tiny disturbances in one part of the system can get amplified to produce large effects somewhere else—the “butterfly effect” from chaos theory that came up in the earlier discussion of cumulative advantage and unpredictability. When every tiny factor in a complex system can get potentially amplified in unpredictable ways, there is only so much that a model can predict. As a result, models of complex systems tend to be rather simple—not because simple models perform well, but because incremental improvements make little difference in the face of the massive errors