Here’s a video on the fundamentals of communication and control systems. It’s rather dry and abstract, but it may prove useful as a point of reference.
Many of our readers will not know what “The Laplace Domain” could possibly be. In short, every time the lecturer writes (s) he is just reminding us that what looks like multiplication is actually something much more complicated, which some rather abstruse math converts into a multiplication. When we’re done, we’ll convert it back. In this video, it doesn’t matter much. (By the way, the trick only works in linear systems, which is why engineers find climate science overwhelming. I know I did.)
This is the basic idea of much of engineering, and the core of the mathematics behind the discipline once known as “cybernetics”. We have a system which can do lots of things. There is one behavior which we want. So we measure the behavior we have and represent the behavior we want, and use the difference to adjust the system. This is the “closed loop”.
Closed loop systems are uncannily more powerful than open loop systems. Capitalism triumphed over soviet communism because individual actors in capitalist systems can compare their own situation to a situation they want and act on the difference, while in Soviet communism everything was centralized via ponderous and crudely measured five year plans.
But closed loop systems work only if we have a good model of what we want and a good measure of what is happening. If the system has a behavior mode which is not measured or controlled, it will wander off that way and break. Sometimes this happens in uncontrolled vibrations. Sometimes the system just drives itself out of spec. You must measure what you want to control.
The economy as it is structured has no measure of sustainability – to the contrary the future is systematically discounted. It is consequently not surprising that the system is going off the rails in exactly that way.
The Laplace domain is an extension of the Fourier domain. If you’re familiar with the Fourier transform, you may be able to follow the follow-up lecture. This moves back to the “time domain” where we live, and shows briefly how feedback can grossly alter the behavior of a system.
By the way, it would be great if someone could do this kind of thing with real production values, wouldn’t it?
Here’s a comparable lecture. Honestly if I had my life to live over I think I’d do my undergrad in Bangalore. I just love the Hindu accent. And if you persist all the way to minute 34, you will understand the nature of the very basic step (please pronounce this in Hindi-inflected English) that I suspect economists somehow always neglect. Every time you use a model, you must constantly be aware of its limits, is it not?