### Systemantics

If you haven't read _Systemantics_ by John Gall, I highly recommend it. Here is a review.

http://www.uia.org/problems/systfail.htm

Here is a publisher.

http://generalsystemantics.com/

It covers fundamentals of dealing with systems, particularly large systems. It's written in the style of C Northcote Parkinson, with grandiose language. Here are some valuable things from it:

http://www.uia.org/problems/systfail.htm

Here is a publisher.

http://generalsystemantics.com/

It covers fundamentals of dealing with systems, particularly large systems. It's written in the style of C Northcote Parkinson, with grandiose language. Here are some valuable things from it:

**.**

A complex system that works is invariably found to have evolved from a simple system that worksA complex system that works is invariably found to have evolved from a simple system that works

**A complex system designed from scratch never works and cannot be patched up to make it work; you have to start over, beginning with a working simple system.**

This fits my experience, does it fit yours?

## 2 Comments:

Thanks for the pointer. I find the work very interesting.

Atanu Dey

www.deeshaa.org

Fits it dead-on. It is also likely that it fits natural systems as well. For example, in 1972, Robert May found that in order for a randomly assembled network describing a dynamical system to possess local stability, it had to be simple, ie. small and have few connections (Nature 238:413).

This was a challenging idea to ecologists, who felt intuitively that natural systems were stable, but who also knew that they were very complex. Interestingly, it had been generally accepted at the time that systems were stable

becauseof their complexity (MacArthur 1955, Ecology 36:533).Some argued that perhaps local stability was the wrong measure of what was generally meant by 'stability'. Others said that the intuition that ecosystems were stable was simply teleology. May retorted:

theoretical work should not try to prove any general theorem that "complexity implies stability", but instead should focus on elucidating the very special sorts of complexity, the singular strategies, which may promote such mathematically atypical stability.So people like Stuart Pimm spent a long time finding out what common attributes stable model ecosystems had, yet there just didn't seem to be a way around May's law when systems were assembled randomly.The "singular strategy" was not to do with identifying the special structures, but the special method of obtaining those structures. They found the very thing that you suggest.

When model ecosystems are built from small, stable ecosystems, with incremental additions and substractions of species according to their dynamics, one can obtain very large, dynamically stable systems (e.g. Taylor 1988, J. Theor. Biol. 135:569). And this is true not just for local stability, but more complicated measures of stability like permanence (e.g. Law & Morton 1996, Ecology 77:762).

Now the interesting question is: how much is a model ecosystem like a real ecosystem, a society, or a nation?

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