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> per Scott: that a complex domain cannot be accurately modeled or understood, that all attempts to do so are at best compromises and in error, and that any model will prove insufficient and unequal to the task.

Ahh, interesting. I believe that most modern scientists would reject that proposition, a priori. Millennia of scientific advancements have shown that complex systems can indeed be modeled. Recent advancements in mathematics have shown that self-correcting simulations are certainly possible (deep learning, for example). Science itself could be simply described as a “self-correcting model with built-in error checking”.



His argument is more nuanced, and ... includes examples.

I've only managed about 3 chapters of the book so far. It has proved quite illuminating.

There are examples of systems that do model well and those that don't. Depending on your physics, the 3, 2, 1, or null-body problems. The double pendulum. Etc.

And those are only at the very simplest level.

The fact that some systems can be effectively modelled doesn't mean that all can be. And in the case of complex systems (e.g., the German "rational forestry" method described early in Scott's book), initial success may preceed subsequent catastrophe.


> The fact that some systems can be effectively modelled doesn't mean that all can be.

Based on my understanding, the leading-edge of scientific research shows that “if the human mind can comprehend it, then there is a mathematical function for it”

Are you positing, then, that there simply are limits to what humans can understand? Or, rather, that some systems are just too complex to document properly?


1. No.

2. Yes, yes.



1. Halting problem, as a reductionist example.

2. I don't follow your point.

I do appreciate the examples/links.




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