Complexity: The Emerging Science at the Edge of Order and Chaos

by M. Mitchell Waldrop

why is there something rather than nothing? The universe started out from the formless miasma of the Big Bang. And ever since then it's been governed by an inexorable tendency toward disorder, dissolution, and decay, as described by the second law of thermodynamics. Yet the universe has also managed to bring forth structure on every scale:

the very richness of these interactions allows the system as a whole to undergo spontaneous self-organization.

How does spontaneous self organization differ from the invisible hand?

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.

Adaptivity-key reason progressivism fails. The left refuses to adapt out of fear while the opposition is constantly adapting.

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 were 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. The edge of chaos is where centuries of slavery and segregation suddenly give way to the civil rights movement of the 1950s and 1960s; where seventy years of Soviet communism suddenly give way to political turmoil and ferment; where eons of evolutionary stability suddenly give way to wholesale species transformation. The edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive, and alive.

Quote- Explain- Yes, and - Yes, but

linear, reductionist thinking

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March 17, 1987,

Arthur had convinced himself that increasing returns pointed the way to the future for economics, a future in which he and his colleagues would work alongside the physicists and the biologists to understand the messiness, the upheaval, and the spontaneous self-organization of the world.

The answer, as Prigogine and others realized back in the 1960s, lies in that innocuous-sounding phrase, "Left to themselves . . ." In the real world, atoms and molecules are almost never left to themselves, not completely; they are almost always exposed to a certain amount of energy and material flowing in from the outside. And if that flow of energy and material is strong enough, then the steady degradation demanded by the second law can be partially reversed. Over a limited region, in fact, a system can spontaneously organize itself into a whole series of complex structures. The most familiar example is probably a pot of soup sitting on the stovetop. If the gas is off, then nothing happens. Just as the second law predicts, the soup will sit there at room temperature, in equilibrium with its surroundings. If the gas is turned on with a very tiny flame, then still nothing much happens. The system is no longer in equilibrium—heat energy is rising up through the soup from the bottom of the pot—but the difference isn't large enough to really disturb anything. But now turn the flame up just a little bit higher, moving the system just a little farther from equilibrium. Suddenly, the increased flux of heat energy turns the soup unstable. Tiny, random motions of the soup molecules no longer average out to zero; some of the motions start to grow. Portions of the fluid begin to rise. Other portions begin to fall. Very quickly, the soup begins to organize its motions on a large scal...

self-organizing system

In mathematical terms, Prigogine's central point was that self-organization depends upon self-reinforcement: a tendency for small effects to become magnified when conditions are right, instead of dying away. It was precisely the same message that had been implicit in Jacob and Monod's work on DNA. And suddenly, says Arthur, "I recognized it as what in engineering we would have called positive feedback."

Positive feedback

In social change movements harnessing positive feedback is essential. Incremental change will always be effectively counteracted.

negative feedback diminishing returns is what underlies the whole neoclassical vision of harmony, stability, and equilibrium in the economy.

the actual living economy out there," he says. "It's path-dependent, it's complicated, it's evolving, it's open, and it's organic."

White feminism locked us in to goals which are consistent with reliance on neoclassical economics. White feminism reinforces neoliberalism, “them that has gets”. It is easy to reverse whatever incremental gains white women made precisely because the goals and tactics reinforced a racist sexist system.

Look at a software product like Microsoft's Windows, he says. The company spent $50 million in research and development to get the first copy out the door. The second copy cost it—what, $10 in materials? It's the same story in electronics, computers, pharmaceuticals, even aerospace. (Cost for the first B2 bomber: $21 billion. Cost per copy: $500 million.) High technology could almost be defined as "congealed knowledge," says Arthur. "The marginal cost is next to zilch, which means that every copy you produce makes the product cheaper and cheaper." More than that, every copy offers a chance for learning: getting the yield up on microprocessor chips, and so on. So there's a tremendous reward for increasing production—in short, the system is governed by increasing returns.

See Paul Mason on • The long term mismatch between market systems and an economy based on information

Now compare that with standard bulk commodities such as grain, fertilizer, or cement, where most of the know-how was acquired generations ago. Today the real costs are for labor, land, and raw materials, areas where diminishing returns can set in easily. (Producing more grain, for example, may require that farmers start to open up less productive land.) So these tend to be stable, mature industries that are described reasonably well by standard neoclassical economics.

They were successful because increasing returns make high-tech markets unstable, lucrative, and possible to corner—

Back in 1891, the great English economist Alfred Marshall actually devoted quite a bit of space to the increasing returns in his Principles of Economics—the book in which he also introduced the concept of diminishing returns.

increasing returns could lead to multiple possible outcomes

Maria Augusztinovics.

"nonlinear dynamics."

the whole really can be greater than the sum of its parts.

But most startling of all was the nonlinear phenomenon known as chaos. In the everyday world of human affairs, no one is surprised to learn that a tiny event over here can have an enormous effect over there. For want of a nail, the shoe was lost, et cetera. But when the physicists started paying serious attention to nonlinear systems in their own domain, they began to realize just how profound a principle this really was.

Under the right circumstances, the slightest uncertainty can grow until the system's future becomes utterly unpredictable—or, in a word, chaotic.

Why no one could predict Trump’s election. Yet, the left is still trying to understand this through linear analysis-was it race or was it class.

This everything-else-is-chemistry nonsense breaks apart on the twin shoals of scale and complexity, he explains. Take water, for example. There's nothing very complicated about a water molecule: it's just one big oxygen atom with two little hydrogen atoms stuck to it like Mickey Mouse ears. Its behavior is governed by well-understood equations of atomic physics. But now put a few zillion of those molecules together in the same pot. Suddenly you've got a substance that shimmers and gurgles and sloshes. Those zillions of molecules have collectively acquired a property, liquidity, that none of them possesses alone. In fact, unless you know precisely where and how to look for it, there's nothing in those well-understood equations of atomic physics that even hints at such a property. The liquidity is "emergent."

Emergent properties

emergent properties

"phase transition,"

By no coincidence, it was also about this time that the new, unified science acquired a name: the sciences of complexity. "It seemed a much better canopy for everything we were doing than any other phrase we were using, including 'emerging syntheses,' " says Cowan. "It embraced everything I was interested in, and probably everything that anyone else at the institute was interested in."

Darwinian natural selection the whole story. Darwin didn't know about self-organization—matter's incessant attempts to organize itself into ever more complex structures, even in the face of the incessant forces of dissolution described by the second law of thermodynamics. Nor did Darwin know that the forces of order and self-organization apply to the creation of living systems just as surely as they do to the formation of snowflakes or the appearance of convection cells in a simmering pot of soup.

biology. He was thunderstruck. "Here was this absolutely stunning phenomenology," he says. "Here you start with a fertilized egg, and the damn thing unfolds, and it gives rise to an ordered newborn and adult." Somehow, that single egg cell manages to divide and differentiate into nerve cells and muscle cells and liver cells—hundreds of different kinds. And it does so with the most astonishing precision. The strange thing isn't that birth defects happen, as tragic as they are; the strange thing is that most babies are born perfect and whole. "This still stands as one of the most beautiful mysteries in biology," says Kauffman. "Well, I became absolutely enthralled with the problem of cellular differentiation, and set straight away to thinking hard about it."

any cell contains a number of "regulatory" genes that act as switches and can turn one another on and off.

In a real cell, he realized, a great many regulatory genes could be active at the same time. So instead of executing its instructions step by step by step, the way human-built computers do, the genomic computer must be executing most or all of its genetic instructions simultaneously, in parallel.

And if that was the case, he reasoned, then what mattered was not whether this regulatory gene activated that regulatory gene in some precisely defined sequence. What mattered was whether the genome as a whole could settle down into a stable, self-consistent pattern of active genes.

Kauffman was also troubled by people's tacit assumption that detail was everything. The biomolecular details were obviously important, he knew. But if the genome really had to be organized and fine-tuned to exquisite perfection before it could work at all, then how could it have arisen through the random trial and error of evolution? That would be like shuffling an honest deck of cards and then dealing yourself a bridge hand of thirteen spades: possible, but not very likely. "It just didn't feel right," he says. "You don't want to ask that much of either God or selection.

The precise genetic details of any given organism would be a product of random mutations and natural selection working just as Darwin had described them. But the organization of life itself, the order, would be deeper and more fundamental. It would arise purely from the structure of the network, not the details.

"So I immediately started thinking about what would happen if you just took thousands of genes and hooked them together at random—what would they do?" Now here was a problem he knew how to think about: he had studied neural circuitry ad nauseam at Oxford. Real genes were pretty complicated, of course. But Jacob and Monod had shown that the regulatory genes, at least, were essentially just switches. And the essence of a switch is that it flips back and forth between two states: active or inactive.

he spent his spare time sitting on the rooftop of his apartment in Oakland, obsessively drawing little diagrams of his regulatory genes hooked up in wiring diagrams, and trying to understand how they turned each other on and off.

What I was interested in were the natural laws of complex systems. Whence cometh the order?

He quickly convinced himself that if the network became as densely tangled as a plate of spaghetti, so that every gene was controlled by lots of other genes, then the system would just thrash around chaotically.

Kauffman likewise convinced himself that if each gene were controlled by at most one other gene, so that the network was very sparsely connected, then its behavior would be too simple.

Jacob and Monod had already demonstrated that real genes tended to be controlled by several other genes. (Today, the number is known to be typically two to ten.)

So Kauffman started to concentrate on networks in between, where the connections were sparse, but not too sparse. To keep things simple, in fact, he looked at networks with precisely two inputs per gene.

He already knew that densely connected networks were hypersensitive in the extreme: if you went in and flipped the state of any one gene from say, on to off, then you would trigger a whole avalanche of changes that would cascade back and forth through the network indefinitely. That's why d...

I had found something that no one would have intuited then." Instead of wandering through a space of one million trillion trillion states, his two-input network had quickly moved to an infinitesimal corner of that space and stayed there. "It settled down and oscillated through a cycle of five or six or seven or, more typically it turned out, about ten states. That's an amazing amount of order!

sparsely connected networks

Well, he thought, one obvious prediction of his model was that real genetic networks would have to be sparsely connected; densely connected networks seemed incapable of settling down into stable cycles.

Kauffman, meanwhile, was both intrigued and perplexed by Arthur's increasing-returns ideas. "I had a hard time understanding why this was new," he says. "Biologists have been dealing with positive feedback for years." It took him a long time to comprehend just how static and changeless the neoclassical world view really is.

So a laser printer is possible when you have computer technology, laser technology, and a Xerox reproducing technology. But it is also only possible because people need fancy, high-speed printing." In short, technologies form a highly interconnected web—or in Kauffman's language, a network. Furthermore, these technological webs are highly dynamic and unstable. They can grow in a fashion that is almost organic, as when laser printers give rise to desktop publishing software, and desktop publishing opens up a new niche for graphics programs.