Against Hayek’s market mysticism, complexity science points toward collective intelligence, not price signals.
As I write, steam is rising from my teacup. Illuminated by morning sunlight, it rises first in a straight column before wobbles and notches appear. These imperfections grow, forming eddies and vortices which spin up smaller whorls and ripples of steam, before dissipating into nothing. Here, in technical terms, the steam has transitioned from laminar to turbulent flow: from order to chaos. These, teacups and rising steam, are the stuff of classical mechanics, a straightforward application of Newton’s laws. Yet there’s a paradox, too: while the physics that undergirds them can be written in equations dating from the early nineteenth century, predicting the exact path of the steam is almost impossible. The problem with turbulence of this sort is that everything matters.
Physicists like to talk in terms of characteristic lengths: orbits on the scale of millions of kilometres, or nanometre-scale quantum systems. With turbulence, there is no characteristic length. Everything, from the smallest eddy to the largest vortex, affects everything else. Billowing steam is a complex problem, in both the everyday and technical sense of the term. With it, we are in the realm of complexity science, a field that explores how simple, interconnected components—in this case, molecules of water vapor—come together to create something entirely unexpected.
The study of complexity was made possible by computers. Prior to the digital revolution, the only practical way to model the world mathematically was to simplify it into a form that could be solved with a pen and paper, dropping less important terms until scientists were left with something tractable. A billiard ball bouncing around a pool table, for instance, can be described with simple equations because it is okay to ignore things like Jupiter’s gravitational field. The analogue tools of thermodynamics will suit you fine even if you have a trillion trillion billiard balls, so long as they are shooting about at random, as physicists imagine gas to. We can think of this case as unorganized complexity.
The turbulent steam from my teacup poses a much harder problem: it exhibits instead what the scientist Warren Weaver called, in 1947, “organized complexity”. If the unorganized complexity of a trillion trillion billiard balls admits pencil-friendly statistics, organized complexity cannot be simplified. Pen and paper tricks like dropping unimportant terms fail because, with my teacup, nothing can be ignored. Computers, with their enormous speed, are excellent at not ignoring things. They let organized complexity unfold on its own.
Since the first digital general-purpose computers of the 1940s and ’50s, complexity has become an organizing concept in the natural sciences. One of the primary uses of complexity concepts is in understanding weather dynamics. With weather patterns, even a small change in initial conditions—a seed of rising air, say, or the location of a crashing atmospheric wave—can lead to a radically different weather system. This sensitivity is what the meteorologist Edward Lorenz famously called the “butterfly effect”: with a flap of its wings, even a tiny creature could change the course of weather history.[1] Another field interested in organized complexity is ecology, where the dynamics of species populations is explored using similar maths as that which is used to model the atmosphere. And together, ecology, meteorology and the complex physics of the oceans are interlinked in the vast computational simulations used to forecast the impacts of climate change: simulations known as Earth System Models, capturing in silicon the coupled evolution of our planet’s biosphere, atmosphere, oceans and ice.
The resulting images of a future Earth, destabilized by climate change, join with images from other complex system fields—swirling fractal geometries, murmurations of starlings, maps of neurons—capturing the minds of the public and intellectuals alike. Where the science of the eighteenth century created an image of the world dominated by clocks and billiard balls, deterministic and described by the laws of mechanics, in the twenty-first, the world is a decentralized network of organized complexity. This popular vision of universal complexity diverges from its roots in partial differential equations: its appeal is not about improving weather forecasts. It’s more about a feeling of reverence, even mysticism. This is a mystical complexity that ventures beyond numerical methods, offering answers to ancient questions—solving the mind–body problem, for example, with a soul emerging out of networks of neurons—and speaking to a desire for holism and interconnection, allowing spirituality to enter into an otherwise thoroughly disenchanted world. It is here, at the boundary between complexity science and mystical complexity, that strange and conflicting politics emerge.