Thanks for the response :)

Fair enough. If we had figured out a way to model society at the level of abstraction in your example, we'd have psychohistory.

It might be worth a clarification that it's not the complexity of the model, but the chaotic nature of the underlying system that's makes it intractable. If you had just as many variables, but they were pool balls being hit, you'd be able to predict the outcome with high accuracy.

It's exogenous random variables and stochasticity in the effects themselves that create the chaos, not the complexity of the model itself. (in my understanding)

If you've tried modeling this with differential equations I'd be super curious to see! I've never made a CLD model before, only the DAG version.

Have you heard of "motivated reasoning"? Your "causal projection" sounds like a very similar idea.

Nicely written article, btw! I hadn't heard of Causal Loop Diagrams before, and will be adding that to my vocabulary :)

One critique: the crux of your argument seems to be that it's too hard to calculate counterfactual outcomes given complex CLDs. But isn't that what multi-input multi-output control systems do? Or, given the state of the world (or each of the variables in your graph at least) at some given time, couldn't you just let the model run from there?

A core part of Causal Inference is not just estimating the magnitude and polarity of the effects, but fitting a function to each effect. If you can quantify the effect each variable has on others, I don't see why you can't answer questions like "how much did the assassinations contribute to civil rights reform?".

Here's one from Microsoft! https://github.com/py-why/dowhy