Fireflies flash in perfect synchrony — here Pteroptyx malaccae in a mangrove apple tree in Malaysia.
There are other medical and technological applications. The laser, one of the most practical gadgets of our time, depends on light waves in sync, atoms pulsing in unison, all emitting light of the same color and moving in phase, with all the troughs and crests of the light waves perfectly lined up. The light in a laser is no different from the light coming out of these bulbs overhead in that the atoms are not really that different; it's the coordination of the atoms that's different. The choreography is the difference, not the dancers.
We see sync all around us. The global positioning system that's been used for satellite-guided weaponry in Iraq consists of 24 satellites, each of which has an on-board atomic clock, very well synchronized to a master super clock at NIST, the National Institute of Standards and Technology in Boulder, Colorado. It's only because of this perfect synchronization, down to a billionth of a second, that we can hit a license plate with a missile. It's a grim example, but then at the same time we see life itself depending on synchrony. Sperm cells beat their tails in unison as they swim toward an egg, communicating through pressure fluctuations in the fluid. I could go on and on. What's so breathtaking about the phenomenon of synchrony is that it occurs at every scale of nature, from the sub-atomic to the cosmic. It's what may have killed the dinosaurs. I could explain why it was gravitational synchrony that caused certain asteroids to be flung out of the asteroid belt and ultimately to strike our planet, probably extinguishing the dinosaurs and many other creatures. It's one of the most pervasive phenomena in nature, but at the same time one of the most mysterious from a theoretical perspective.
We are used to thinking about entropy, the tendency of complex systems to get more and more disordered, as the dominant force. People always ask me, "Doesn't synchrony violate that? Isn't it against the laws of nature that systems can become spontaneously more ordered?" Of course you can't violate the law of entropy, but there is no contradiction. The point is that the law of entropy applies to a certain class of so-called isolated, or closed systems, where there's no influx of energy from the environment. But that's not what we're talking about when we discuss living things or the earth. Where systems are far from thermodynamic equilibrium all bets are off, and we see astonishing feats of self-organization, synchrony just being the simplest such example.
The same laws that give rise to entropy and the tendency of systems to become more and more disordered are the same laws that will account for synchrony. It's just that we don't have a clear enough understanding of the thermodynamics of systems very far from equilibrium to see the connection—but we're getting there. We're learning a lot about spontaneously synchronizing systems, at least in physics. We know how a laser works, and there's nothing that violates entropy about that. For living examples, like heart cells, we have a rough idea about how electrical currents are passed back and forth. But synchrony also touches on some of the deepest mysteries of our time, like consciousness. There's some thought, at least according to some neuroscientists, that what distinguishes consciousness from other forms of brain activity is the synchronized firing of the cells involved at specific frequencies close to 40 cycles a second.
What I like about this whole area of thinking about spontaneous order, or self-organization, is that I really do believe that many of the major unsolved problems of science today have this same character. Architecturally they involve millions of players, whether they're neurons, heart cells, or players in an economy. They're all interacting, influencing each other through complex networks and via complicated interactions, and out of this you sometimes see amazingly organized states. Stuart Kauffman calls this "order for free." Many of us are really trying to climb that same mountain from different perspectives. Kauffman and others see it as a matter of understanding evolution better. When he or Gell-Mann speaks of complex adaptive systems the emphasis seems to be on the word adaptive, suggesting that the key to understanding the mystery is to learn more about natural selection. However, you didn't hear me really talk about evolution at all. I feel like they're barking up a tree that's deeper into the forest. There's a right place to start, and the simplest place to start is to think about problems where evolution plays no part.
I want to think about purely physical systems that are complex in their own right and how, just from the laws of physics, we get these self-organized patterns. It feels to me like I would want to understand that first, before I add the further complication of evolution. We know that's important, but that's starting at the wrong place.
Recently, I keep finding myself wanting to learn more about cancer and what it is about the network of cells or the network of chemical reactions that goes awry in a cancerous cell. There are certainly some cases where a single gene may be screwed up, but I don't believe that all cancers will be explained that way. It's been 35 years since Nixon declared war on cancer and we haven't really understood it. Understanding oncogenes is a great start, but that can't be it. Again, it's about choreographies of proteins and genes and the missteps, not just of single dancers, but of the way that they're moving together. Cancer is somehow a dynamical disease that we won't understand through pure biological reductionist thinking. It's going to take a combination of reductionism to give us the data, and new complex systems theory, super computers, and math. I would like to be part of that.
Biologists often emphasize the part that computers will play, and it's true that computers will be indispensable, but there's a third leg, which is good theoretical ideas. It won't be enough to have big computers and great data. You need ideas and I think that those ideas will be expressed in the language of complex systems mathematics. Although that phrase, complex systems, has been talked about a lot, I hope people out there appreciate what a feeble state it's in, theoretically speaking. We really don't understand much about it. We have a lot of computer simulations that show stunning phenomena, but where's the understanding? Where's the insight? There are very few cases that we understand, and so that brings me back to synchrony. I like that example of synchrony as a case of spontaneous order because that's one of the few cases that we can understand mathematically. If we want to solve these problems, we've got to do the math problems we can do, and we need the simplest phenomena first, and synchrony is among them. It's going to be a long slog to really understand these problems.
Another thought, though, is that we may not need understanding. It could be that understanding is overrated. Perhaps insight is something that's been good for three or four hundred years since Isaac Newton, but it is not the ultimate end. The ultimate end is really just control of these diseases, and avoiding horrible ecological scenarios. If we could get there, even without knowing what we're doing, that would maybe be good enough. Computers might understand it, but we don't have to. There could be a real story here about the overrating of understanding.
In broad strokes, there were hundreds of years after Aristotle when we didn't really understand a whole lot. Once Kepler, Copernicus, and Newton began explaining what they saw through math, there was a great era of understanding, through certain classes of math problems that could be solved. All the mathematics that let us understand laws of physics—Maxwell's equations, thermodynamics, on through quantum theory—all involve a certain class of math problems that we know how to solve completely and thoroughly: that is, linear problems. It's only in the past few decades that we've been banging our heads on the non-linear ones. Of those, we understand just the smallest ones using only three or four variables—that's chaos theory. As soon as you have hundreds, or millions, or billions of variables—like in the brain—we don't understand those problems at all. That's what complex systems is supposed to be about, but we're not even close to understanding them. We can simulate them in a computer, but that's not really that different from just watching. We still don't understand.