My model can be complex enough to represent the original system and still be useful. I can iterate on design and make measurements without building the real thing.

No, this is just plain wrong, sorry.

I can describe a population fairly well using one or two ODEs. That doesn't mean that a population of individuals is simple. It's incredibly complex. However, at the meso-scale of just looking at population size and growth rate, my model is accurate (enough).

A model doesn't describe a system at all levels. It simulates the behaviours, whether explicit in the system or emergent from other properties, in order to describe its behaviour at a certain level.

This does not mean that your model is not complex. It means that the system is simple.

At least, it is simple enough to be represented fully in a model. I think this paradox refers mostly to systems that are too complex to be represented in any model at full accuracy, and must therefore be simplified.

> I think the George Box aphorism linked at the bottom ("All models are wrong [but some are useful]") is closer to the right way to think about this.

Also, see the original article:

> Bonini's Paradox, named after Stanford business professor Charles Bonini, explains the difficulty in constructing models or simulations that fully capture the workings of complex systems (such as the human brain).

A model is "too complex" by this definition if there can be no complete and accurate virtual representation of it, it can't be wrong by definition. If you emulate a CHIP-8, for instance, you are able to accurately represent the full state of the system inside the emulator; therefore, the emulator is capable of full accuracy. It's impossible to do the same with a human brain, as quoted in the article.