It’s not even necessary to bring in perceptual control theory to apply this to humans. The quicker the feedback, the more accurately you can adjust your actions to produce the desired outcome. I’ll dig up the links later, but I believe the brain is optimized to perform over short causal distances, actions followed immediately by results. When that information is delayed, it creates the opportunity for uncertainty, so when it finally does come, the effects are severely reduced. Learning is hampered. That’s why unambiguous feedback is one of the requirements for entering a flow state. (Anecdotally, I can place much of the difficulty of my job squarely on the fact that we’re forced to operate with months-long feedback loops).

Lately it’s occurred to me that I can apply this heuristic to my thoughts themselves. As far as adjectives go, ‘tangled’ is generally a fairly good one for describing my thought processes. ‘Hazy’ is another one. Except for brief moments of clarity, most of them bear little relation to reality, instead being steeped in various biases and incomplete-understandings. 90% of the time I try to ignore what goes on in my brain.

But that sort of half-assery doesn’t fly when I’m writing. For some reason, seeing the words in front of me makes me that much more cognizant of them. When the words get put to the page, it’s immediately apparent whether they’re sensible or need a healthy dose of backspace. Writing, perhaps because I use different parts of my brain when I’m perceiving outside stimulus, foists clear (or at least, clear-er) thinking onto me.

My hope is that by writing often, and getting near-instant feedback to the sort of thoughts I’m having, that I can ‘train’ myself to think more clearly, to not fall prey to biases and mistakes of logic that (ideally) I would notice immediately.

Failing that, at the very least writing more will at least produce that many more hours of clear-thoughts in a day.

So, to both improve my understanding of the field and to try to break down what exactly you’re studying when you’re studying “systems”, I’m proceeding with unpacking and exploring some of the basic concepts of the study of systems. It’s a field full of cool-sounding ideas and impressive buzzwords, but how much meat is on those bones? Lets find out.

I’ll jump right in with a big one: complexity. Whenever you see the word ’system’, you can bet that the word ‘complex’ is either tacked on in front or lurking somewhere in the paragraphs below. Simple systems don’t merit study – they tend to fall into the category of already-figured-out. So-called ‘complex systems’ are a large part of why there’s able to be a ’systems’ discipline at all.

But what is complexity? Like many buzzwords, it’s gotten defined over and over again, in a variety of ways. Even in the comparatively more rigorous field of computer science, it seems that everyone and their mother has concocted some definition of complexity. Here’s a short list:

Computational Complexity – From ye old wikipedia, computational complexity “is a branch of the theory of computation in computer science that focuses on classifying computational problems according to their inherent difficulty.” It’s related to finding efficient algorithms for testing and solving problems of various kinds. P=NP is the classic, unsolved problem in computational complexity.

Kolmogorov Complexity – Kolmogorov Complexity is a description of how complex a piece of data or information is, making it closely related to computational complexity. Essentially, the Kolmogorov complexity of a particular sequence is the length of a computer program necessary to output that sequence. The longer the program, the more complex the sequence. Interestingly, under this definition (unlike many other definitions of complexity) a random sequence is the most complex you can get, since it’s impossible to compress. Kolmogorov Complexity is a description of the regularity of a piece of information (from perfectly regular to perfectly irregular) rather than what we would typically intuit to be its complexity. It also has the irritating property of being uncomputable, which hamstrings it’s usefulness somewhat.

Statistical Complexity – (or Effective Complexity). Bear with me as I google. Unlike Kolmogorov Complexity, Statistical complexity seems to be related to the structure present in a particular process – perfectly periodic and perfectly random processes possess no structure, and so are simple as opposed to complex. Statistical complexity is concerned with the degree of pattern or regularity in a process (though I confess what you might use to measure that escapes me). Like Kolmogorov Complexity though, the means of measurement of Statistical Complexity is the length of the computer program that outputs the regularities of a sequence.

The above are all closely related to, or subfields of, Information Theory – they’re about data compression and algorithmic performance. But none of these are what you tend to think of, or what systems engineers refer to, when they talk about something being complex. Let’s make that short list of definitions a little bit longer:

Biological Complexity – Now we’re headed towards familiar territory. Because biology isn’t developed to the point where it can completely describe the processes that produce a behavior, measures of biological complexity tend to based on form, function, or the genetic sequence that codes for an organism. These measures can be tricky, as it’s seldom obvious where to draw the line between different structures or behaviors. And though both are determined (at least initially) by genetic code, the fact that there’s a gap in our understanding between what the gene says and what the organism is and does means that currently it’s not the most useful definition, although in time it may end up being the most accurate.

Physical Complexity – Posited as a measure in the paper where I stole most of my definitions from, physical complexity is a measure of how much information about the environment is contained in a particular sequence. For example, the more information about the environment a gene stores, the more adaptive behaviors it is capable of specifying, and the more complex it becomes. “If A then B” is much less complex than “If A and C and E given G H and I previously, then B F and Q”, although I wonder if the physical complexity of gene sequences might level off once they’re capable of producing organs (such as minds) that can store information about the environment in a different fashion. More precisely defined, physical complexity is a measure of the shared Kolmogorov Complexity of a sequence and its environment.

These definitions, though relating to the physical world, are still rooted in information theory – genes coding for an organism is no different than a computer program outputting a string of data. Moving away from these definitions brings us closer to what we traditionally think of as complexity. Unfortunately, this serves only to illustrate the uselessness of those intuitions:

From the wikipedia article on “complex systems”:

A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties (behavior among the possible properties) not obvious from the properties of the individual parts.

From the wikipedia article on “complex adaptive systems”:

A CAS behaves/evolves according to three key principles: order is emergent as opposed to predetermined (c.f. Neural Networks), the system’s history is irreversible, and the system’s future is often unpredictable.

From a MITRE newsletter:

“Complexity is a fuzzy concept,” says Lashon Booker, principal engineer for the Center for Washington Command, Control, and Communications (WC3). “There are prototypical examples of complex systems, such as economies and social systems; however, trying to draw boundaries and determining that everything inside is complex and everything out isn’t, I think, misses the point. Complex systems typically have some characteristic properties, but the extent to which a particular system exhibits any given property can vary.”

This is what often gets referred to when discussing ‘complexity’, and it’s the least precise definition yet. Read about ‘Complex Systems’, ‘Complexity Theory’, or other miscellanea with the word ‘Complex’ in it and you’ll see a vague definition like the one above, and a list of the properties that supposedly characterize a complex system. These include:

-emergence
-non-linearity
-self-reference
-fuzzy boundaries
-an open (as opposed to closed) system

These topics all have something in common: they’re all poorly understood, almost as if someone drew a line around a group of topics we haven’t figured out and called it a field of study. Some pattern seemingly ‘emerges’ out of nowhere? Complexity! Feedback loops causing unpredictable behavior? Complexity! Non-linear or ‘chaotic’ behavior that can’t be foreseen? Complexity! Don’t know where something starts or stops? Complexity! Self-reference or deep, recursive hierarchies doing something weird? Complexity! You can’t talk about any of them without using the phrase “don’t know”. It’s a field defined by our ignorance.

Take traffic, for example. Traffic/road systems have many of the properties of supposed complex systems – many small agents, fuzzy boundaries, an open system, etc. But they’re seldom talked about as an example of complex systems because we’re not ignorant of them – traffic pattern analysis and traffic engineering are well-defined disciplines. It’s only when we fail at understanding something that we apply the complexity label to it.

And I strongly suspect that these ‘mysterious’ behaviors of complex systems are really problems of computation or information theory in disguise. For example, one of the few useful concepts to come from the field of cybernetics is Ashby’s Law of Requisite Variety, which states: “The larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate.” This seems little more than a restatement of the definition of Physical Complexity. And the only coherent definition of ‘emergence’ that I’ve come across has been an information-theoretical one.

So when you’re talking about complexity, you’re talking about one of two things:

1) A computer program outputting a sequence of data

2) Something you don’t understand

Systems engineering is about engineering a process, about creating something that does something. In principle, any process can be viewed as a computation (though I’ll bet many systems scientists would love to invoke the magic of ‘holism’ to explain why that isn’t the case), bringing us squarely into the realm of Information Theory. In short, complexity, fuzzy topic it may be, is over-defined. Problems of complexity are problems of computation or information theory in disguise, and any attempt so study or solve them will have to either look to those disciplines, or forgo a mathematical answer and use a heuristic, intuitionist approach. But pontificating about the magic and mysteriousness of complexity, as systems people seem to enjoy doing, is unnecessary. You can stop writing papers about “What is Complexity?” – we don’t need any more.

I’ve missed you.

Anyway.

A heuristic that I’ve seen pop up, in several different arenas, is the principle of doing more with less. To be successful, really successful, it’s necessary to strip things down to the absolute minimum. Find the essence of what you’re trying to do, and, to the extent thats possible, focus on it to the exclusion of everything else*. If you can make that raw essence exceptional, everything else will be easy.

Fine, I can roll with that. My problem is that it’s a deceptively difficult trick to pull off in practice.

Sure, it sounds easy to just trim away everything superfluous from your raw passions… until you’ve tried to do it, and find yourself left with… *counts*…a list of seven or eight things that you still think you need to focus on. Several months ago I wrote that I found myself being driven by three masters. They seem to have multiplied since then. It’s not something I can come close to satisfying, much less sustaining.

Of course, this just means that I haven’t yet found the essence of what I’m trying to do (which is it’s own disappointment, given that it’s been bouncing around in my head for months at this point). What I have is seven or eight lenses onto something more basic beneath, something I haven’t yet completely grasped. That needs to be my next step – until I do, my actions will be muddled, pulling me in seven or eight different directions instead of where I want to be going.

*Bonus points: this bears a striking resemblance to my favorite definition of mathematics, “the minimum environment to preserve ideas”

Beyond that, I feel in a bit of a rut. The same schedule day in and day out is bad enough when it’s you’re job, but when your supposed ‘leisure’ time is something you start to dread, that’s a good indication that a change is in order.

But this isn’t an excuse to give up on whatever goals I had set for myself – the prize still has eyes upon it. And forcing myself to write something every day has been valuable – at the very least it shows I’m capable of producing something. Something, however, is no longer enough. I’m closer to it, but a change of direction is needed to go any further. I’m aiming towards good, great, and beyond, and I think I’m close enough to it where I can reach it with a burst of self-motivation (something that has been lacking as of late). I’m hoping a format change will provide that.

So starting January 1st, I’ll be taking a slightly different tack with this blog. It probably won’t be TOO much of a change – my interests haven’t suddenly shifted – but I’ll be approaching them from a different angle. More focused, definitely. More personal, perhaps. More passion, absolutely.

-Evidence suggests that consciousness is integral to the resolution of conflicting motor commands (think how intensely aware you are when you have to hold on to a burning plate, or when you’re pulling off a band-aid, or any other time you have to fight a reflex)

-Consciousness seems to be tied in with executive function (they’re both located near and around the frontal cortex), which is also important to the regulation of conflicting motor commands.

-Various parts of the brain have been mapped specifically to restraint. The ACC is often associated with ‘conflict monitoring’, and the DLPFC is associated with sorting out conflicting information. When we’re asleep, the DLPFC is shut down, which is why we cannot notice the ridiculous dreamworld generated for us while we sleep. Another brain area, the DFMC, shows activation when an action is inhibited.

-Frontal lobe damage results in a loss of impulse control, and children (who have less developed frontal lobes) tend to be notoriously impulsive.

-The frontal cortex is likely the last brain area to have evolved (as it is the last brain area to grow in babies). It was likely added on to an already adequately functioning brain, one that was simply following basic emotional scripts. Conscious thought is a slow process, and in times of stress we rely on the quick judgments of our emotional system instead.

-The infamous Benjamin Libet experiments show we’re not conscious of an action until we’ve already decided to do it.

All the evidence points to consciousness serving an inhibitory function, essentially neurological veto power. This is interesting enough by itself, but what really intrigues me is how this function might relate to how the brain learns (specifically, how it approximates Bayesian updating and/or hypothesis and test).

The money idea behind interchangeable parts is standardization. And the benefit of standardization is that it removes thought and judgment from the process. Instead of figuring out how to repair a rifle, you just remove the broken part and put in an identical one. Instead of having one assembled by a knowledgeable craftsman, you can give the basic instructions and the pile of parts to unskill laborers. Instead of having each piece carved by hand, you can have hundreds pumped out by dumb machines. Standardization takes intelligence out of the process of creation and moves it back a level, into deciding how the process of creation should work. Once the design of a rifle is completed, and the proper castings are forged, everything that remains is a dumb process, provided things run smoothly. You know longer need to understand how a gun works to be able to repair or assemble one.

So perhaps it’s more accurate to say that rather than intelligence being removed, standardized parts come with intelligence preinstalled.

Because of this, any constant feature of your environment will eventually be ignored. It’s this ability that lets us adapt to new situations, no matter how bad (or good) they might be. That horrible job doesn’t seem so horrible after a few weeks at it, and that solid gold house doesn’t seem so amazing after the 100th time you wake up inside it. We get used to things, for better or for worse – pleasure and pain fade over time. What doesn’t fade is desire – our reward circuitry is (I think) decoupled from this, providing a constant need to pursue more no matter how much we already have. This might be why it’s so easy to be greedy – we’re designed by evolution to pay attention to what we don’t have, not what we do.

In real life, however, kicking up some dust is a great way to win, or at least to avoid losing. And just like in the movies, it’s a dirty trick that only villains use.

I am, of course, talking about arguments. Again. Specifically, I’m talking about kicking up dust by making things more complex than they need to be. Once any counter arguments become impossible to predict, impossible to check or impossible to know, you don’t have to worry about discarding your assumption – since you can’t prove it wrong, it must be right!

Example:
Woman: You should quit smoking, it’s bad for you.
Man: The studies that show that were all performed in the sixties with an extremely flawed methodology – they may have even been forged.
Woman: What? Why would you think that?
Man: You can’t be sure they WEREN’T forged, and the issue has become so politically charged that any current studies are suspect. People think it’s bad, and so they’ll try to interpret the evidence to show that it’s bad. The same thing happened with the concept of nuclear winter.
Woman: Yes, but…
Man: And there’s plenty of examples of conventional wisdom about medicine being way off base – look at all the hoopla about low fat diets. The human body is amazingly complex and we’re really not very good at teasing cause and effect relationships out of it.
Woman: Well…
Man: Besides, anything’s bad for you if you have too much of it, and its not clear how much you have to smoke before any alleged health problems start to show up. The science surrounding smoking is murky at best.
Woman: I want a divorce.

~fin

This sort of “it’s complicated, therefore it’s probably wrong, therefore I’m probably right” chicanery can be done for almost anything that can’t be directly verified. Much like arguing against basic principles, fog like this is difficult to penetrate. And also like arguing against basic principles, using this strategy makes you a dick.

This is made worse by the fact that a state of ignorance is a state that I loath, regardless of how accurate it may be. “I want to be right” I think, and I proceed to take whatever steps my self-preservation instincts deem necessary. This includes (but is not limited too): overstating my case, deliberately obfuscating my point, favoring terseness over clarity, carefully avoiding counter-arguments, and other behaviors that I’m likely not even aware of. This is in addition to all the built-in biases that humans come pre-installed with to be ensured of their own correctness. This blog is one long struggle (flecked with the occasional victory) against these tendencies.

These habits are somewhat soothed by the sprinkling of caveats I try to include in whatever I write. Lots of “seems to be”, lots of “appears”, lots of “as far as I knows” and “to the best of my knowledges.” But these do an inadequate job of masking the certainty that by all rights shouldn’t be there in the first place.

The most frustrating part is that it’s not hard to do this once you focus on it. Sure, I may not be able to trace the exact path a belief takes through my cortex, but I’m reflective enough to know how much I know about something. But I have to actively consider it, and ignore my desire to be a constant source of pithiness, insight, and wisdom. And in general, I don’t, save for rare moments of lucidity (this one).

Since this isn’t good enough for me, let these few paragraphs signal my commitment to stating clearly and concisely what it is I know – no more and no less.

Cyclones are caused by a variety of atmospheric phenomena that result in stable areas of rotating fluid. The smallest cyclones are tornados and waterspouts, which exist on a scale of 2 km (if you include the atmospheric formations that generate them). The largest known cyclone (technically, an anticyclone) is Jupiter’s Great Red Spot, maxxing out at 40,000 km in diameter. 4 orders of magnitude seperate them.

The same goes for galaxies. The smallest galaxy yet found is Leo T, a dwarf galaxy only 600 light-years in diameter. On the other end of the spectrum is IC1101, a cD type galaxy over 6 million light years across – 4 orders of magnitude larger.

Stars, luminous balls of plasma powered by nuclear fusion, also have scale limits. The lower end is around 75 Jupiter-masses, the minimum amount needed to generate nuclear fusion. OGLE-TR-122b, the smallest yet discovered, is around 100 jupiter masses, 166,800 km in diameter. The largest is VY Canis Majoris, at 3,614,000,000 km in diameter. Once again, the difference is 4 orders of magnitude.

Replicators, on the other hand, stubbornly push the boundaries imposed by the laws of physics. The smallest organism is the Mycoplasma genitalium bacteria, at a mere 200 nanometers in diameter, or 1/5,000,000th of a meter. The largest organism is the Giant Sequoia, which can grow nearly 100 meters in height. 8 orders of magnitude seperate the largest and the smallest, an enormous window to operate in. (You can in fact go larger or smaller by pushing the boundaries of what you classify as an ‘organism’.)

There are, of course, enormous selection effects at work here – it’s much easier to measure phenomena on Earth than phenomena anywhere else in the universe, so it will naturally be catalogued much more thoroughly. Nevertheless, the basic machinery of life (ATP energy source, DNA encoding for proteins, cellular structure) has adapted itself to an enormous space of possibility, one larger than almost* any other complex phenomena yet discovered.

*The only exception I can think of is black holes, which can grow without limit due to gravitational effects dominating at large scales.