Working the System

“Systems Biology Calls for New Way of Training Doctors” – sidebar headline in Chemical and Engineering News

The first feedback control device was probably the float valve, used in ancient water clocks. I’m discounting biological and other feedback systems, obviously. Those are usually called homeostasis.

Prior to the 20th century, there aren’t a lot of examples of feedback devices. Watt’s governor is the one most commonly cited, and its 18th century origin was close to concurrent with the steam release valve, which is also a feedback device. The governor is also of note because it is an example of proportional control. It didn’t just shut the steam on and off; it throttled the steam by varying the size of an aperture.

On/Off control is the sort that you get with a thermostat. When the temperature drops, the furnace kicks on at full force, then it stops when the temperature rises. That produces a limit cycle because the process is non-linear. If the furnace heating were proportional to the difference between the room temperature and the thermostat’s set point, then you’d have proportional control. That also produces a cycle, but the cycle is sinusoidal, and the process is termed linear, because of the type of equation that describes it.

The 19th century invention of a torpedo control system by Robert Whitehead was probably the first mechanical invention that addressed the oscillation problem. The first torpedo designs used a simple hydrostatic valve to adjust the control fins, but this caused “porpoising,” an up-and-down motion that sometimes put the torpedo above the surface of the water. Whitehead realized that something was needed to damp out the fluctuations, so he devised a pendulum that crudely measured the torpedo’s angle and modified the control in the direction to reduce that angle. This added a rate-of-change term (aka, a derivative) to the control equation, and reduced the depth fluctuations of the torpedo from 40 ft. to less than 6.

The problem with a proportional controller with damping is that the system often settles to a point of stable error, because the small error signal is damped out by the derivative signal. The solution to that is to add what is called the integral term, so a small error signal is integrated over time, and thus builds to a large enough signal to move the settling point.

The first example of a full PID (proportional-integral-derivative) controller comes in 1922, when N. Minorsky devised an automatic controller for the steering of ships. The mathematical characterization of control systems was also advanced enough by then to properly analyze such systems.

The “feedback loop” as it came to be called, seemed to offer some benefit to another, more general problem, of the sort that a wide variety of scientists and others were facing, that of the reductionist trap. When someone says, “We’re nothing but a bunch of atoms that think we’re alive,” that’s voicing the reductionist trap. A bunch of atoms we certainly are, but it doesn’t seem accurate to say that we’re nothing but a bunch of atoms. There are, after all, a lot of bunches of atoms around, but none of them behave just like me. I rather doubt that any of them think they are me, either.

Another way of addressing the problem is to use phrases like “emergent phenomena,” which is a fancy way of saying that the whole is more than the sum of its parts. Since a feedback loop is also more than the sum of its parts, and since homeostasis (feedback, remember) is a general characteristic of living organisms, there came a general belief that feedback analysis might offer some insights into biology, or psychology, or sociology.

Thus was born the Cybernetics Movement, which included some folks like A. H. Maslow, whom I mentioned in a recent essay, as well as Margaret Mead and Gregory Bateson, plus some heavy hitters like Claude Shannon, John von Neumann, and Norbert Weiner, whose 1950 book, The Human Use of Human Beings: Cybernetics and Society became a best-seller. I’ll mention in passing that Claude Shannon had just pretty much invented information theory, which, aside from revolutionizing electronic communications, also became part of the cybernetics movement.

Later, Cybernetics became General Systems Theory, which was not exactly a cult and not exactly a movement. But it did have some Believers, and I was probably one of them. The systems guys were of the belief that systems theory could be applied to, if not everything, an awfully big part of everything, and that it could and would revolutionize everything it was applied to.

In my own case, as I’ve previously written, I was attracted to the idea of simulation modeling of large scale biological, environmental, and social systems. I started off doing lake ecology, then slid over to atmospheric chemistry with barely a hiccup, because the methods of analysis were so similar. So that part of the program worked pretty well, at least from my viewpoint. However, I hit the downside of it all pretty quickly.

The downside was first, that while the tools of analysis were top notch, to use them in real world situations, you need a lot of data, and the methods of data collection weren’t really up to it. I hit that first in lake ecosystem modeling, where data from sunlight, nutrients, and plankton were pretty good, but the data we had for fish populations were horrible. And, oddly enough, the fish were important. After that experience, atmospheric science was wonderful; there was so much data available.

The second drawback was the real killer: analysis isn’t enough. In order to “change the world” you have to change the world. You can have the right answer, but if people aren’t willing to use it, what good is it? And, if your way of doing things is different in any way from what people are already doing, what they are, in fact, trained to do, you’re not going to make much headway.

It doesn’t help to blame the other guy, either. Everyone thinks their job is hard and everyone else’s is easy. No, what they are is different. Getting the correct engineering analysis isn’t the same as getting the right policy analysis, and neither of them make getting the policy adopted that much easier.

The worst of it was with the physicians. They go through hell getting their medical education. If you want medicine to change, you’re going to have to wait for an entirely new cohort. Worse, because medical education is also controlled by those same people, you’re actually talking about many generations. I watched more than one systems engineer bash his head into that brick wall, over and over again.

The quote at the beginning of this essay is from July, 2006. It could just as easily have been from 1976. Or 1956 Maybe it will happen, but I’m not holding my breath.