What does that mean and how is it helpful?
This isn't a math lesson but one example may be useful, so here it is:
When physicists or engineers analyze electromagnetic waves radiating from an antenna, something unfamiliar appears: there are three different worlds, that tell different stories. The worlds appear initially to be in total conflict with each other, yet they can't be.
Very near the antenna, much closer than one wavelength of the signal, the radiated power appears to follow one law - it may clearly fall off as (1/r) where r is the radial distance away.
The measurements and math are quite clear and easy to do. The answer is clear.
Very far from the antenna, many wavelengths away, the radiated power clearly can be measured to fall off as (1/(r*r*r)) or 1 over r-cubed. The measurements and math are easy to do. The answer is clear. (but different from the one above).
And, in between, the equations are a mess, measurements are much harder to make sense of, and a third world applies, possibly the field varies as the inverse square of the distance.
What's the point? The point is that all three observers and measurements are "right", from their point of view.
These really do seem to have a lot of resemblance to the ways of knowing the world that are described by science, business, and religion respectively. Science tends to be very accurate and short-range, specializing is studying phenomena that can be studied "in isolation" - the core of the "scientific method". Religion tends to be very far-range, specializing in dealing with the biggest picture one can get - everything, with all the parts connected together, over all space and time.
And business tends to occupy the very messy place in the middle, often despised by both sides.
From science's viewpoint, business thinking is too messy and imprecise. It is "unclean." It deals with too much at once.
From religion's viewpoint, business is too down-to-earth and pragmatic and short-sighted. It is unclean. It deals with too little. Like "science" it is viewed as neglecting the very important human and non-quantitative factors that are critical.
So, this is the world into which "system thinking" and "system dynamics" really comes to play, trying to cross the gap from the "science" world where things can be studied separately, into the business world, where it seems everything happens at once and nothing can be known with certainty. It is a world where action is more important than study, where feedback from motion has more wisdom in it than any amount of analysis from a static point of view.
It is a world perhaps like the one birds occupy, where static vision may not be very good, but high-speed motion vision is astoundingly good, and they can fly through a tree of twigs at 40 miles per hour without hitting something. It's a different way of seeing.
And, one of the unspoken and perhaps unrealized terrors of systems thinking is that, by legitimizing the concept of looking "upwards", some legitimacy spills over into the concept of "religious studies" of the world - the perceived arch-enemy of science.
Like a rock-climber on a cliff face, the question is how to include a little more within one's grasp, without holding so little now that one slides off the cliff to one's death below. What's always worked before is the commandment and teaching to narrow one's view an focus, to cut down scope, to consider less. As one moves towards the intermediate field, the complexity rises as does opportunity for error.
This model does raise the new idea that the complexity doesn't rise forever - in fact, the complexity goes up, but then goes back down again, and, at a high enough level, the complexity gets back down to a manageable level. In physics, this happens in thermodynamics, when you stop looking at molecules and start looking at "a gas". The area inbetween is a mess. Single molecules are relatively easy to study, as are large-scale gases.
So, one issue for systems dynamics is how much more to include in the boundaries. It may be that "more is less", and that adding some additional factors make the model harder, but adding more and refactoring to a larger scale may make that complexity, legitimately, go away again.
No comments:
Post a Comment