I explicitly explained that you can model an analog machine using a digital computer. When you make a topological map of a weather system (or a brain) or take a digital picture of a flower, you are generating a model. This is the subject of the articles you linked me.
No matter how accurate your digital model of a weather system, however, it will never produce rain. The byproduct of Turing machines (digital models) is strictly discrete.
- Thoughts are a byproduct of brains, just as rain is a byproduct of weather and torque is a byproduct of internal combustion engines.
- You could generate rain, torque (and maybe thoughts) in various contexts, of course. But not with Turing machines, whose only possible outputs are 1s and 0s.
You can model digital computers using analog computers. And the reverse is also possible. But digital systems are substrate-independent, whereas analog systems are substrate-dependent. They’re fundamentally inextricable from the stuff of which they’re made.
On the other hand, digital models aren’t made of stuff. They’re abstract. You can certainly instantiate a digital model within a physical substrate (silicon chips), the way you can print a picture of an engine on a piece of paper, but it won’t produce torque like an actual engine let alone rain like an actual weather system.
On a separate note, you reallllly need to acquaint yourself with Complexity Theory, if you actually believe our models will ever be anything other than decent estimates.
To learn more, please take a Theoretical Computer Science course.
Irreducibility isn’t a part of physics
Correct. It’s theoretical computer science. Again, analog systems are irreducible to digital ones by definition. They can only be modeled (functionally and crudely).
