Mitch Waldrop’s Complexity: The Emerging Science at the Edge of Order and Chaos, published in 1992, is one of the best books I’ve read in recent times. It describes the science behind complex adaptive systems and the work academics from numerous disciplines have done on the concept of emergence. I also think it contains a kernel of insight – and a possible scientific explanation – on why top-down control of economies is a bad idea.

Complexity and emergence

But first, what are complex adaptive systems? The following passages from Waldrop’s book is a neat summary of what they are:

“For example, every one of these questions refers to a system that is complex, in the sense that a great many independent agents are interacting with each other in a great many ways. Think of the quadrillions of chemically reacting proteins, lipids, and nucleic acids that make up a living cell, or the billions of interconnected neurons that make up the brain, or the millions of mutually interdependent individuals who make up a human society.

In every case, moreover, the very richness of these interactions allows the system as a whole to undergo spontaneous self-organization. Thus, people trying to satisfy their material needs unconsciously organize themselves into an economy through myriad individual acts of buying and selling; it happens without anyone being in charge or consciously planning it. The genes in a developing embryo organize themselves in one way to make a liver cell and in another way to make a muscle cell… In every case’ groups of agents seeking mutual accommodation and self-consistency somehow manage to transcend themselves, acquiring collective properties such as life, thought, and purpose that they might never have possessed individually.

Furthermore, these complex, self-organizing systems are adaptive, in that they don’t just passively respond to events the way a rock might roll around in an earthquake. They actively try to turn whatever happens to their advantage. Thus, the human brain constantly organizes and reroganizes its billions of neural connections so as to learn from experience (sometimes, anyway)… the marketplace responds to changing tastes and lifestyles, immigration, technological developments, shifts in the price of raw materials, and a host of other factors. 

Finally, every one of these complex, self-organizing, adaptive systems possesses a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes, which are merely complicated. Complex systems are more spontaneous, more disorderly, more alive than that. At the same time, however, their peculiar dynamism is also a far cry from the weirdly unpredictable gyrations known as chaos. In the past two decades, chaos theory has shaken science to its foundations with the realization that very simple dynamical rules can give rise to extraordinarily intricate behavior; witness the endlessly detailed beauty of fractals, or the foaming turbulence of a river. And yet chaos by itself doesn’t explain the structure, the coherence, the self-organizing cohesiveness of complex systems.

Instead, all these complex systems have somehow acquired the ability to bring order and chaos into a special kind of balance. This balance point – often called the edge of chaos – is where the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either. The edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo, and where even the most entrenched old guard will eventually be overthrown.”

Put simply, a complex adaptive system comprises many agents, each of which may be following only simple rules. But through the interactions between the agents, sophisticated outcomes spontaneously “emerge”, even when the agents were not instructed to produce these outcomes. This phenomenon is known as emergence. Waldrop’s book has passages that help shed more light on emergence, and also has an illuminating example of how an emergent behaviour takes shape:

“These agents might be molecules or neurons or species or consumers or even corporations. But whatever their nature, the agents were constantly organizing and reorganizing themselves into larger structures through the clash of mutual accommodation and mutual rivalry. Thus, molecules would form cells, neurons would form brains, species would form ecosystems, consumers and corporations would form economies, and so on. At each level, new emergent structures would form and engage in new emergent behaviors. Complexity, in other words, was really a science of emergence… 

…Cells make tissues, tissues make organs, organs make organisms, organisms make ecosystems – on and on. Indeed, thought Holland, that’s what this business of “emergence” was all about: building blocks at one level combining into new building blocks at a higher level. It seemed to be one of the fundamental organizing principles of the world. It certainly seemed to appear in every complex, adaptive system that you looked at…

…Arthur was fascinated by the thing. Reynolds had billed the program as an attempt to capture the essence of flocking behavior in birds, or herding behavior in sheep, or schooling behavior in fish. And as far as Arthur could tell, he had succeeded beautifully. Reynolds’ basic idea was to place a large collection of autonomous, birdlike agents—“boids”—into an onscreen environment full of walls and obstacles. Each boid followed three simple rules of behavior: 

1. It tried to maintain a minimum distance from other objects in the environment, including other boids.

2. It tried to match velocities with boids in its neighborhood.

3. It tried to move toward the perceived center of mass of boids in its neighborhood.

What was striking about these rules was that none of them said, “Form a flock.” Quite the opposite: the rules were entirely local, referring only to what an individual boid could see and do in its own vicinity. If a flock was going to form at all, it would have to do so from the bottom up, as an emergent phenomenon. And yet flocks did form, every time. Reynolds could start his simulation with boids scattered around the computer screen completely at random, and they would spontaneously collect themselves into a flock that could fly around obstacles in a very fluid and natural manner. Sometimes the flock would even break into subflocks that flowed around both sides of an obstacle, rejoining on the other side as if the boids had planned it all along. In one of the runs, in fact, a boid accidentally hit a pole, fluttered around for a moment as though stunned and lost—then darted forward to rejoin the flock as it moved on.”

Emergence in the economy

In the first series of excerpts I shared from Waldrop’s book, it was hinted that an economy is a complex adaptive system. But this is not always true. Emergence is unlikely to happen in an economy with a very simple make-up. On the other hand, emergence is likely to occur in an economy whose depth and variety of economic activity within has increased over time. Here’s a relevant passage from Waldrop’s book:

“In fact, he argued, once you get beyond a certain threshold of complexity you can expect a kind of phase transition analogous to the ones he had found in his autocatalytic sets. Below that level of complexity you would find countries dependent upon just a few major industries, and their economies would tend to be fragile and stagnant. In that case, it wouldn’t matter how much investment got poured into the country. “If all you do is produce bananas, nothing will happen except that you produce more bananas.” But if a country ever managed to diversify and increase its complexity above the critical point, then you would expect it to undergo an explosive increase in growth and innovation-what some economists have called an “economic takeoff.””

This brings me to the topic behind the title and introduction of this article: Why top-down control of economies is a bad idea. An important aspect of emergence is that specific emergent phenomena in any particular complex adaptive system are inherently unpredictable. This applies to economies too. Given everything above, I think it stands to reason that any government that aims to exert top-down control over an economy that has grown in complexity would likely do a poor job. How can you control something well if you’re unable to predict its behaviour?

Note: An earlier version of this article was published at The Good Investors, a personal blog run by our friends.

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Disclosure: Ser Jing does not own shares in any of the companies mentioned.