In a breakout in the student colloquium at ISDC 2022, we discussed the difficulty of getting a paper accepted into the conference, where the content was substantially a discrete event or agent simulation. Readers may know that I’m not automatically a fan of discrete models. Discrete time stinks. However, I think “discreteness” itself is not the enemy – it’s just that the way people approach some discrete models is bad, and continuous is often a good way to start.
On the flip side, there are certainly cases in which it’s sensible to start with a more granular, detailed model. In fact there are cases in which nonlinearity makes correct aggregation impossible in principle. This may not require going all the way to a discrete, agent model, but I think there’s a compelling case for the existence of systems in which the only good model is not a classic continuous time, aggregate, continuous value model. In between, there are also cases in which it may be practical to aggregate, but you don’t know how to do it a priori. In such cases, it’s useful to compare aggregate models with underlying detailed models to see what the aggregation rules should be, and to know where they break down.
I guess this is a long way of saying that I favor a “big tent” interpretation of System Dynamics. We should be considering models broadly, with the goal of understanding complex systems irrespective of methodological limits. We should go where operational thinking takes us, even if it’s not continuous.
This doesn’t mean that everything is System Dynamics. I think there are lots of things that should generally be excluded. In particular, anything that lacks dynamics – at a minimum pure stock accumulation, but usually also feedback – doesn’t make the cut. While I think that good SD is almost always at the intersection of behavior and physics, we sometimes have nonbehavioral models at the conference, i.e. models that lack humans, and that’s OK because there are some interesting opportunities for cross-fertilization. But I would exclude models that address human phenomena, but with the kind of delusional behavioral model that you get when you assume perfect information, as in much of economics.
I think a more difficult question is, where should we draw the line between System Dynamics and model-free Systems Thinking? I think we do want some model-free work, because it’s the gateway drug, and often influential. But it’s also high risk, in the sense that it may involve drawing conclusions about behavior from complex maps, where we’ve known from the beginning that no one can intuitively solve a 10th order system. I think preserving the core of the SD genome, that conclusions should emerge from replicable, transparent, realistic simulations, is absolutely essential.
Bernoulli and Poisson are in a bar …