[phoe-krk]

To quote the original article: "As a model of a complex system becomes more complete, it becomes less understandable. Alternatively, as a model grows more realistic, it also becomes just as difficult to understand as the real-world processes it represents"

If your model is simple enough to represent the original system accurately, then the original system is not complex.

[dearrifling]

If your model is simple enough to represent the original system accurately, then the original system is not complex.

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.

[AllegedAlec]

> If your model is simple enough to represent the original system accurately, then the original system is not complex.

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.

[phoe-krk]

> My model can be complex enough to represent the original system

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.

[AstralStorm]

The paradox invokes an absolute. How do you even know the system is "too complex"? Maybe it is too complex for humans but not for a galaxy brain?

[phoe-krk]

See the comment at https://news.ycombinator.com/item?id=20019426 that refers another quote:

> 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.