[0d9eooo]

Related:

https://arxiv.org/abs/0708.1362

At some point the complexity of a system becomes great enough that it's impossible to simulate it perfectly.

The interesting question for me lately is how and under what circumstances can you ignore the complexity for some purpose.

For example, underlying what is represented in that poster is some set of physical processes that are ignored in that poster. The system at that physical level of description would be even more complex, probably too complex to represent on a poster. So why the level of analysis of the poster? Similarlly, at some point it's easier to talk about eating and fatigue than it is the citric acid cycle. How and why do you move from one level of analysis to another? Some of it probably depends on what is being explained, but some of it might not.

[fulafel]

Interesting looking paper. From the abstract it seems that the claim is about about perfectly replicating a future state of the simulated system? Simulation with a less ambitious fidelity level still seems useful to understand these kind of systems.

[di4na]

Yes, that is called a model.

Usually people that study complexity will say that all models are wrong but some are useful.

In software, we usually prefer the term abstraction.

The trick is to remember that our model are wrong. This gap between "system-as-modeled" and "system-as-reality" can be inconsequential in a lot of time, but it is also a place in which failure mode can rise. It is important to always keep exploring it and change the model depending on situations.