If you take a more Darwinian point of view the dynamics of the universe are such that as the universe evolved in time, complex systems arose out of the natural dynamics of the universe. – Seth Lloyd
Systems Dynamics is one flavour of systems thinking that tries to understand the behaviour of complex systems as they change over time. I don’t have much experience of this space but came across quite a nice application of it when I presented a paper at a conference in 2019. Dennis Sherwood from The Silver Bullet Machine Manufacturing Company Limited described climate change using a systems model and it made quite a lot of sense. The most interesting part was realising that the issue we have with carbon in the atmosphere is akin to having a leaking boat. If we do nothing, we sink. If we plug the leak, we float. But another option is to bale water out faster than it’s coming in – and Sherwood’s argument is that carbon capture can do that, if we remove more carbon from the atmosphere than we create, then we can limit rises in temperature.
Anyway, the thing about systems dynamics is that it’s complex. The image that start this post shows you the kinds of things you need to know. For example, you need to know what happens when things interact, what kind of complex behaviour can result. You need to understand how feedback works and how things move through a system in stocks and flows. For example, if you buy a toy a day then that looks like a constant line, one toy every day when you think about flow. When you think about stocks, however, you end up with a rising line, as the total number increases every day. Which is why when the kids got to 10 years old there are at least 3,650 pieces of unwanted plastic that I didn’t have when we started.
Another important element to understand is how time delays work – you make a change and it takes time to work through. Moving too fast is a problem. Then there are non-linearities, not everything is causal and it’s hard to model some kinds of changes. And then you have people and the models they carry around in their heads or on paper.
All this skills are necessary if you want to create good quality models and it turns out that they are not innate – we don’t have a natural ability to think in this way. Not even smart people, the paper suggests, find this obvious or easy. One suggestion is that it’s just too hard and we aren’t clever enough to deal with the challenges. Or maybe it’s because the people doing the tests weren’t being paid or didn’t have enough time. Maybe it’s the test itself, it was done in a lab while in the real world we seem ok at making these kinds of decisions. But the paper says that perhaps the problems are even more basic than that. We’re poor at doing this because we don’t know why we’re doing it.
I liked this sentence, “Nevertheless, there is only a weak relationship between education and performance. For a large fraction of the subjects, training and experience with calculus and mathematics did not translate into an intuitive appreciation of accumulations, of stocks and flows.” Modelling does not build intuition – working on real-world problems does. This may not be a situation that’s unique to this field – it feels like a problem that applies to much of education. We are left wondering what the point of it all is.