Who Is Sovereign?

In the cold meat grinder of any system, be it the chrome-plated monstrosity of a corporation or the byzantine labyrinth of bureaucracy (both tentacles of the same squirming control machine), glitches inevitably erupt. These are the burps and hiccups in the program, the malfunctions in the meat. They can erupt with all the messy glory of a runaway digestive system, spewing forth spectacular accidents that leave you reeling in the stench of chaos.

Here’s the rub: how do we identify these glitches in the matrix? Are they mere blips on the screen, easily dismissed by the suits at the control panel? Or are they harbingers of a system meltdown, a full-on Burroughs-ian cut-up waiting to happen?

Then comes the dance with the gremlins. How do we address these exceptions? Patch the program? Throw a bucket of bolts at the malfunctioning machine? Or is there some deeper, more primal ritual required, some offering to the machine gods to appease their circuits?

But the real meat of the matter, the question that hangs thick in the air like the stink of fear, is this: who holds the power? Who gets to make the call when the system shits the bed? Who has the authority to yank the plug, rewrite the code, or sacrifice a goat to the malfunctioning server?

This, my friend, is where the ghost of Carl Schmitt slithers in, that old authoritarian bastard. He whispers in your ear, his voice a chilling binary code: “Who is sovereign?” In the face of glitches and exceptions, who gets to decide the fate of the system? Is it the button-pushing drones, forever locked in their bureaucratic trance? Or is there a higher power, a hidden hand that pulls the strings and dictates the course of action?

The answer, my friend, is as murky as the oil slick that coats the gears of any system. The search for the sovereign, the one with the final say, is a never-ending chase through the labyrinthine corridors of power. Just remember, in the game of automation, there’s always a ghost in the machine, waiting to remind you who’s really in control.

Complicated, Complexity, Chaos


Science has progressed, at least beginning with the physical sciences, by always being reductionistic: that is, reducing things to their primary elements, whether they’re electrons, atoms, molecules, or genes and so forth. That has been enormously successful, but one of the things that we’ve begun to appreciate more and more is that kind of paradigm has extraordinary limitations.

When you try to build up from these fundamental elements to the collective whole, you discover that the whole is much greater than, behaves differently than, and is structured differently from the sum of its parts. What you recognize in parallel with that is almost all of the major issues that we face on the planet — everything from climate change and the question of stability in markets to potential questions about risk and how we deal with things like cancer, and the encroaching threat of global urbanization — are what we call complex. They’re not easily, or even potentially, reduced to the sum of their parts.

For example, organisms like you and I are much more than the sums of our cells or genes. A city is much more than the sum of all its people or roads and businesses. Furthermore, all of these are not just conglomerations of all of these things: They’re all highly dependent upon one another. There’s what we call express emergent properties: New things emerge as you build these systems up, whether they are economies, climates, cities, or our bodies.

The Critical Difference Between Complex and Complicated

(…) A typical complex system is composed of myriad individual constituents or agents that once aggregated take on collective characteristics that are usually not manifested in, nor could easily be predicted from, the properties of the individual components themselves. For example, you are much more than the totality of your cells and, similarly, your cells are much more than the totality of all of the molecules from which they are composed. What you think of as you — your consciousness, your personality, and your character — is a collective manifestation of the multiple interactions among the neurons and synapses in your brain.

In general, then, a universal characteristic of a complex system is that the whole is greater than, and often significantly different from, the simple linear sum of its parts. In many instances, the whole seems to take on a life of its own, almost dissociated from the specific characteristics of its individual building blocks (…)

West, Geoffrey. Scale: The Universal Laws of Life, Growth, and Death in Organisms, Cities, and Companies (p. 23). Penguin Publishing Group. Kindle Edition.

A car is complicated, traffic is complex. You can build a car or repair it, but you have to manage traffic. You can achieve full visibility of a complicated system but not of a complex one. That’s why rules can be used with the former but not with the latter.

“Things are complicated”

It isn’t that things are complicated, because complicated is to be distinguished from complex. Let us have a couple of examples.

One of the great successes in the development of science was understanding the motion of the planets. Newton’s laws of gravity and motion and so on set the template of everything we do. Newton’s laws explained all of that, which was fantastic. This reductionistic and simplistic — that is to say it’s not complex — explanation was very profound. Your cellphone wouldn’t work if we didn’t understand all of that with great accuracy, since we use satellites to send our messages.

To do that in detail, to understand it in the detail that it is needed to make technology like this work, we need to put in all of the various little corrections that occur to Newton’s laws due to things like the atmosphere and things like satellites that may have deviated slightly because of an asteroid and so forth, and all of that gets complicated. That’s complicated.”New things emerge as you build these systems up, whether they are economies, climates, cities, or our bodies.”TWEET THIS QUOTE

The other example would be the building of an airplane. We understand in detail and in great depth the physics and material sciences of flight — the engineering and math involved. In that sense, an airplane is a simple system: We can describe it with a relatively small set of equations or algorithms. It’s extremely complicated, potentially, if you try to build a Boeing 787. It is very complicated, but nothing in principle will stop you from doing it. I think there are two great big manuals at Boeing that tells you how to build the 787, the Dreamliner. It’s how they carry it out. You cannot imagine manuals for knowing how your body, or New York City, or the stock market works.

Complicated systems require expertise in their management, but as long as the proper expertise is available and used, the attractiveness of complicated systems is that they generally can be successfully managed. Complicated thinking leads Peoples to think that they are doing something purposeful when in reality they are not, and in fact they are likely doing more harm than good. Complex systems are nuanced and require a nuanced approach. The one thing that will not work is a rigid, rules-based, complicated approach.

While it is not necessary to be a genius to manage complexity, it is helpful to consider for a minute the difference between a genius and someone who is really smart. The reality is that Einstein thought differently. A little-known fact is that most of his mathematical problems were solved by others, including an assistant, Walther Mayer, who solved many of the mathematical equations and did most of the calculations that Einstein’s musings required. Einstein was a complexity thinker, while Mayer was a very good and very intelligent complicated thinker.

When we go to these complex systems — when you think along the lines I’ve elaborated on — you conclude that to get a quantitative, predictive understanding of them is, in principle, impossible if you insist on getting into great detail. This is where I come in.


There is, potentially, an in-between place where we may not have a theory for the way the system works in infinite detail, but Geoffrey west, has shown that you generally can get what physics calls a “coarse grain description”: an understanding of the idealized, average behavior of these systems, and maybe even more than that — how they deviate from various things and so forth. That’s what the scaling laws represent. They show that if you tell me the size of a mammal — its mass, how much it weighs. I can tell you to 80 or 90 percent accuracy how much food it needs to eat a day, how fast its heart beats, how long it is going to live, the length and radius of the fifth branch of its circulatory system, the flow rate of blood of a typical capillary, the structure of its respiratory system, how long it needs to sleep and so on.

You can answer all of these kinds of systems for the average idealized mammal of a given size, and it will be correct to an 85 or 90 percent level. I can predict the metabolism of an elephant, for example, but give me a specific element and I won’t be able to tell you exactly what the metabolic rate is. By metabolic rate, I mean that in colloquial terms: How much it is going to need to eat each day. To bring it closer to home, one can roughly predict the lifespan of a mammal of a given size, and in particular where this life span of a hundred years come from. I can tell you what the parameters are that control that, but I won’t be able to tell you detail how long you’re going to live.

ze the whole, we must accept the sub-optimality of the parts”