
Every Good Regulator of a System Must Be a Model of That System (1970) [pdf] - tischler
http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf
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PaulHoule
see [https://www.amazon.com/Have-Fun-at-Work-
Livingston/dp/093706...](https://www.amazon.com/Have-Fun-at-Work-
Livingston/dp/0937063053) and [https://www.amazon.com/Friends-High-Places-W-
Livingston/dp/0...](https://www.amazon.com/Friends-High-Places-W-
Livingston/dp/0937063061) for practical applications of Ashby thinking.

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kaycebasques
It’d be nice to list the names of the books, rather than just links. I’m
curious about your suggestions, but don’t necessarily want to visit Amazon.

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doppelganger27
The books are "Have Fun at Work" and "Friends in High Places," both by William
L. Livingston

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richard_todd
I don’t see how the last sentence (the brain must model the environment)
follows from the admission on the previous page that a regulator can skip the
model by taking on unnecessary complexity. It seems there is a built-in
assumption that the brain has no unnecessary complexity, if I am following
correctly. I wouldn’t be so sure about that! (Although, I should add, the idea
that the brain models its environment sounds intuitively true beyond
question... I’m just trying to follow the arguments put forth in the paper
itself.)

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amatic
>> modelling might in fact be a necessary part of regulation.

That seems false. PID is used all over, no models. Very simple, very
effective.

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sobellian
The fact that PID loops work for many systems is a consequence of the dynamics
of those systems, for example that the integral of velocity is position and
the derivative of velocity is acceleration. A vast array of phenomena can be
modeled as 2nd order linear ODEs.

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carlmr
Which just means that for many things your brain doesn't need a new model,
just to parametrize one of the most common models.

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sytelus
This is interesting in context of so-called model-free and model-based
reinforcement learning.

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jey
I tacitly assumed that "model-free" really meant "model has been marginalized
out", i.e. instead of treating it as a function to be estimated/learned
through regression. Is that not the case?

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aristus
It feels like people keep rediscovering the implications of Turing-completness
in different domains.

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jessriedel
I don't think anyone doubts that being able to regulate a Turing machine would
require a system that is Turing complete. But this seems very far from saying
that regulating any system requires a complete (isomorphic) model it. Or did
you mean something else?

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aristus
A surprisingly large number of things that we normally don't think of as
computers are Turing-complete. It's a very low bar of complexity. And
regulating requires modeling requires simulation. That's what Turing's result
says: to know whether a program halts you must run it.

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jessriedel
A surprisingly large number of things can be modeled as Hamiltonian systems --
in fact, all things -- but this does not imply that Liouville's theorem can be
usefully applied universally (or even frequently). The reason is that the
subset of variables we actually have access to and care about are not
Hamiltonian.

Likewise, observing that some microscopic piece of a system has operations
that can be mapped on to a Turing machine does not mean that the output of the
Turing machine controls the variables we care about.

Additionally, we prove constraints about the outputs of particular software
(executed on Turing machines) all the time. Noting that some piece of a system
is isomorphic to a Turing machine does not actually mean it will be fed
arbitrary instructions.

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liquidwax
LOL. The second paragraph in the abstract starts with "m this paper a theorem
is presented […]"

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organsnyder
Probably an OCR error.

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adrianmonk
Especially likely since the Introduction section contains has "i(lea" instead
of "idea".

