
Every good regulator of a system must be a model of that system (1970) [pdf] - ismail
http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf
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carrolldunham
This paper is so popular all over the web but nobody has ever managed to
follow the proof of its 'theorem'. This guy is a math professor and quantum
physicist and he was unable to extract any sense from it:
[https://johncarlosbaez.wordpress.com/2016/01/27/the-good-
reg...](https://johncarlosbaez.wordpress.com/2016/01/27/the-good-regulator-
theorem/) He points out that the proof uses terms that aren't even defined.
The fact that the authors of the paper are a psychiatrist and, I think a
humanities academic, doesn't fill me with confidence that the 'theorem' and
its 'proof' are actually those things. It's disquieting to see so many people
taking the paper's claim and running with it because it 'resonates'. I know
there is also an 'internal model principle' paper which is technically correct
but that applies to a specific set of linear systems under specific
conditions.

~~~
tlb
I felt like I followed the proof. The claim is actually very narrow: that to
be a _perfect_ regulator of a system you need an exact model. Obviously, lots
of devices in our world can regulate things reasonably well with simple
models.

For instance, a standard thermostat keeps your house at 20 +/\- 2 degrees C.
In order to keep it at 20.00000000 exactly, it might need to model every air
molecule bouncing around.

~~~
marcosdumay
That's a problem, because we don't actually use "perfect" regulators for any
system. I'm very willing to believe those things don't even exist.

So, it may be a correct proof, but it's a useless one.

~~~
staticautomatic
The implication being what, exactly?

~~~
marcosdumay
Well, except for the fact that the article is trivially wrong, and that there
is no reason to trust its conclusions, I don't think there are any.

But you didn't need my comment for that, the fact that there is an entire
engineering discipline thriving on doing exactly what the article claims to be
impossible should suffice.

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sytelus
This is very interesting in more modern fields of reinforcement learning where
debates rages between "model-free" and "model-based" approaches. I'd no idea
that there was actual any proofs around this. However, I have to say the
theorem goes against my intuition. If you want to control a heating system,
you don't really need to build the model of heater which could be very complex
requiring the knowledge of how it works internally. Instead, you observe the
temperature for given action and successfully control it without ever knowing
how your heater actually works. You do build a model but its model of
observations and actions, not the heater itself. In fact, for vast majority of
things we control such as bicycle or fan or oven, people rarely knows the
model of these very complex systems (in terms physical principals and
internals).

~~~
BoiledCabbage
> You do build a model but its model of observations and actions, not the
> heater itself. In fact, for vast majority of things we control such as
> bicycle or fan or oven, people rarely knows the model of these very complex
> systems (in terms physical principals and internals).

I think you're mistaking the term model for a replica. You don't need a
replica, but you do need a model for your relevant tasks.

If the heater needs 30 mins to warm up before it starts pumping heat, you'll
need to model that. If it overheats after running for 3hrs straight you'll
need to model that too. You don't need every molecule of the physical item
replicated but you do need to model all of the relevant behaviors.

~~~
dTal
This sounds a little circular. If a "model" is defined as a representation of
a system of sufficient fidelity to control it, then of course all controllers
contain models. Deciding which behaviors are "relevant" would appear to be the
key issue here.

~~~
mannykannot
You have a point, but I think it is useful to have a unifying general
principle to use in analyzing the design of regulators, and having a
theoretical basis makes it harder to dismiss.

I can think of a couple of places where it could have avoided accidents (one
tragic and one expensive.) The 1974 DC10 crash in Paris was supposed to be
impossible because the airplane could not be pressurized unless the cargo door
was properly latched, but the mechanism depended on the position of the handle
rather than the latching pins. At Three Mile Island, the operators were
trained to use the pressurizer water level as a primary indicator of the state
of the system, and turned off the emergency cooling feed as a consequence.

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ur-whale
It's funny, because I initially thought the title was about regulators as in
"government regulators", not about control theory.

Even more interesting is the fact that this idea from control theory in fact
completely transposes to government regulation, as in: it'd be _really_ good
if regulators had the faintest idea what they're doing (aka in a perfect
world, they would have a somewhat decent model of the system they're trying to
regulate).

Except ... that's very probably not the case ... (I'm looking at you
economists).

~~~
jplayer01
> Except ... that's very probably not the case ... (I'm looking at you
> economists).

I'm not sure I agree. From what I've seen, it's usually politicians that
ignore unintended consequences and incentives that lead to poor outcomes. Not
sure what that has to do with economists ... they don't run the country.

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carapace
One of the main results of Cybernetics. You can get a PDF of Ashby's book
"Introduction to Cybernetics" from here, as well as a lot of other
information:

[http://pespmc1.vub.ac.be/ASHBBOOK.html\](http://pespmc1.vub.ac.be/ASHBBOOK.html\\)

(edit: Ha! I just realized it's the same Principia Cybernetica Web!)

------
_glass
In the management sciences this is one argument for diversity. If you have a
complex environment, then your team has to mirror that complexity. But the
whole thing based on system theory, so the basic assumption is that there is
no direct access to the internal parts of the system, that is difficult if one
assume that they are human actors.

~~~
9q9
Why would "diversity" help modelling the complexity of the environment? Would
a chess-playing program get better if it also contained a go-engine, checker-
engine, scissor-paper-rocks rules and tiddly-winks?

~~~
TeMPOraL
I think what they mean is that your market is the system, and your team is the
controller, so if the market is diverse, your team should be diverse too.

I think it's still cargo-cult control theory. You can't just make your team as
diverse as your market and expect results to magically improve.

~~~
9q9
I agree that that's what they mean.

What I'm not seeing is how the "requisite variety" that control theory talks
about, relates to "diversity" in modern usage (which for practical purposes is
coeval with the _political_ demand of hiring fewer members of certain
demographics).

Let's use a less loaded analogy, intellectual diversity: the Manhattan
Project, which needed to adapt to a most complex and adversarial environment
(world war), hired a lot of STEM scientists, in particular mathematicians,
physicists and chemists, and mostly from a few top universities, in other
words, a highly homogeneous group that were educated on the same few core
subjects (linear algebra, real and complex anaysis, Newtonian and quantum
physics, special and general relativity). I do _not_ believe that the
Manhattan Project would have been better by hiring fewer STEM graduates, and
more psychologists, historians, yoga teachers and so forth. How would modern
management science convince me that I'm wrong?

The heart of the issue is the conflation of "diversity" (a _political_
concept) with control theory's "requisite variety". The two are related, but
not the same.

I partly agree that it's cargo-culting, but that's not the whole story: the
Melanesians after whom Cargo Cult is modelled, would probably not have tried
to fire / doxx / cancel anyone who wasn't on board with the belief that
building of an airplane runway will by itself bring desirable Western goods.

~~~
jkqwzsoo
You’re missing the part where they say that diversity is needed because the
target audience is diverse. The goal of the Manhattan project was to deliver a
working nuclear weapon. Consequently its hiring practices were in line with
this goal. Consequently your example of yoga instructors and psychologists
(I’m sure there were psychologists consulting for the Manhattan project,
however), is something of a strawman.

What was being discussed was the idea that a company or organization that
wants to appeal to the populace at large should have a workforce
representative of that population at large. I think that’s debatable, but you
can imagine that having someone on staff who can say “this is offensive (to my
group of people)” can be useful to an organization. In this case, “I find this
offensive” would not be a political or cultural message, but an actionable
piece of data that says “For our goal of appealing to the populace at large,
this statement/product may have lower, or negative, utility in appealing to
this part of the population we’d like to like us/like to sell widgets to.”

~~~
_glass
correct. the Manhattan project's complexity is generated by a natural, to be
distinct from a social problem. and it is not just about offence, but also the
inherent complexity of subsidiaries, that can be controlled by having a
diverse workforce in the headquarters, that has cultural or organizational
knowledge.

------
longtom
Previous discussion:
[https://news.ycombinator.com/item?id=16545537](https://news.ycombinator.com/item?id=16545537)

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crawfordcomeaux
If we wish to be good regulators of the environment, we must be a model of the
environment.

What does this mean?

~~~
nabla9
You must figure out the causal structure of the system you are trying to
regulate and incorporate it into the regulator. Using some generic and simple
model to observe variables will not do.

When the system becomes very complex this involves simulation. Think the game
of chess as a system you try to regulate towards win. The only working
solution is to simulate gameplay, dong sequences of moves and evaluating their
outcomes (search). Chess computer replicates the system it regulates.

