systems

Some other commenters here seem to think this article is (or should have been?) an introduction to systems thinking, or an argument for using it. The article is explicitly framed as some advice, based in experience, on avoiding common pitfalls with using explicit models in systems thinking. As such I think it is a nice read. It is also full of links to other interesting work by the author, not least of which is a tool to assist in system modeling (https://github.com/lethain/systems), and links to other similar tools I was unfamiliar with (https://insightmaker.com), which will make for some fine weekend reading and tinkering.

This is certainly not. As a data architect these are things I think about all the time, and this is a pretty good conversation starter. The name drops to Donella Meadows and Ruth Malan alone validate this post's existence, required reading in my mind. The main challenge to this approach is static visuals just don't quite capture interactions and flow the way he wants; even in Meadows' Systems Thinking, once you move to some of the more complex visuals, you can feel your inner "mental picture" struggling (which is the point! Systems thinking is a wicked problem!) A tool I've thought about once in a while would be like LOOPY ( https://ncase.me/loopy/ ) but you could add actual load and performance benchmarks, test cases and simple conditional logic on all the edges to demonstrate failures, security gates, cache hit/miss, etc. Combined with