On Fri, 3 Oct 2014 10:38:52 -0400, "Jim Wilkins"
wrote:

"Ed Huntress" wrote in message
.. .
On Fri, 3 Oct 2014 08:29:57 -0400, "Jim Wilkins"
wrote:

"Martin Eastburn" wrote in message
...
On 10/2/2014 12:11 PM, Jim Wilkins wrote:
"Ed Huntress" wrote in message
...

...
National debt is one of the strangest aspects of the whole
thing.
There is no way to sort it out by common sense because common
sense
has proven to be wrong, time after time. Common sense is based
on
experience and we have little experience with the amounts of
debt
that
we're talking about here. It's like the old story about personal
debt:
If you owe the bank $10,000, you have a problem. If you owe the
bank
$10 million, the bank has a problem....
--
Ed Huntress

Do you agree with this?
http://en.wikipedia.org/wiki/Modern_Monetary_Theory

It seems a logical and well written explanation to me, though I
don't
know enough to find flaws in its speculations.

-jsw


hocus pocus is a lot of it - this is a massively changed document
that is in flux as I type.
(I'm an editor / corrector on one of wiki's pages. )
Look at the page and then see the TALK button on top - flip open
that page. Discussions that are still on-going beating it up and
trying to
get it changed. Eco college page. Then look at the view
history -
wow a lot of changes - and whoa - a lot by one ?? Back on the
main
page, there are a lot of good (quality unknown) end-notes.

Tricky - might be a page of a doctoral student who is taking
inputs
from professors, students, you, me, self proclaimed...

It is only one of many theories. See the bottom of the page -
hyperlinks.

Martin Eastburn

I do realize that Economics has no more solid theoretical basis than
Climatology and that both arrogantly demand the respect they envy in
the hard sciences, which their results do not justify.

The strength of economics is in its empirical studies, not in
theory.
As for the hard sciences: when physics figures out "dark energy" and
integrates general relativity with quantum mechanics, they, too,
will
actually have a sound theoretical basis. g

"Empirical studies" were the state of metallurgy when statistical
analysis showed that the urine of red-headed boys or fern-fed goats
were best for hardening steel.

That wasn't "statistical analysis." That was anecdotal old-wives'
tales.

Smiths could sometimes make excellent
swords by following procedures but didn't understand why they worked.

Now we understand the effects alloying ingredients have on the rate of
recrystallization and can control hardness with cooling rate, or
design an alloy to have predicted properties. Successful extrapolation
is the ultimate confirmation of a theory, and the reason we still use
both Relativity and Quantum Mechanics although we know they disagree
at the extremes.

You're talking about engineering more than science. No amount of
deductive theorizing or undirected observation would produce Special
Relativity. It depended, first, on a great insight; and then was
proven by highly-directed observation based on that insight.

That's common in the modern history of science. Less so in
engineering.


Your fringe counterexamples apply to places and scales we can't reach
to collect data. I realize we may not have the final answers, only
ones that adequately explain all experimental observations in
practical physics and engineering on planet Earth.

Climatology can't reconstruct the known past, I don't know that
Economics even dares to try. I silenced an Elliot Wave proponent by
challenging him to reconstruct the last 10 years with it. He couldn't
snow me with NASA signal processing theory that I understood better
than he did.

The Elliot Wave theory is a form of religion used primarily by
financial people. After 75 years or so, it remains controversial. Like
any religion, it has its casuists and apologists.


The deviation between mainstream climate models run backwards is
around three times larger than the warming from observed data.
Projected hurricane tracks are called "spaghetti models" because our
understanding of energy flow in the atmosphere is inadequate to
accurately predict two days into the future.
http://stormfacts.net/models.htm

That's not climate models. That's weather models.


The difference between a hard and a soft science is that a hard
science has a fundamental understanding of underlying cause and effect
and doesn't rely on statistics beyond correcting for random noise.

That's one definition, but it doesn't say much about *why* one has an
understanding of those causes and effects and the other is limited in
that regard. Examining that issue is far more revealing about sciences
that we practice today.


Biology is a good current example of how a field of science matures.
We know for example how DNA codes for proteins but not all its other
regulatory functions. It's maturing as we watch.

-jsw

This, of course, is part of a long-running discussion between the
physical sciences and the social sciences, with a couple of examples
that are -- in terms of their characteristics as science -- in
between.

The physical sciences are strictly deterministic (don't start with me
about Schrodinger's cat, please g). Advances in those sciences comes
primarily from finding out what the determinants are, and how they
relate. Once those are revealed, the relationships prove to be mostly
extremely simple and consistent.

Economics may be deterministic but, at the fundamental level, it may
be impossible ever to uncover all of the determinants. Or there may be
a randomness for which it is mathematically, scientifically,
improssible to uncover the determinants. In other words, there may be
no intellectual distinction between randomness and the finest-grained
determination. In the case of economics, that's largely because it
depends on the actions of people -- often hundreds of millions of them
-- and neither biology nor psychology is anywhere near determining the
causes of behavior behind any single one of those people. That is, at
the level of weather prediction: predicting an individual, local
event.

So mainstream economics today proceeds mostly along two paths:
statistical modelling, and the recent field called "Behavioral
Economics." The former is the basis of econometrics. Depending on what
results are desired and who wants them, it may be practiced in an
attempt to build theories, or it may be looking for patterns in which
nobody gives a damn about why. My son just finished such a project for
a car manufacturer. They didn't want theories; they wanted patterns
that would produce a better result in their pricing. The model says he
just made over $5 million for them. It's probably true; those types of
models typically have such effects. This past summer, his teamed saved
us taxpayers an estimated $2 Billion by modelling and improving
aspects of the supply chain for a well-known piece of military
hardwre. They weren't looking for a theory. They were looking for
money.

Among practicing, commercial economists and economic analysts, that's
what most of them do today. It's more like engineering than science.
It requires powerful insights to boil millions of variables down to
something that's manageable with analytic tools.

The other economics field that's hot, mostly in academia, is
Behavioral Economics. They're applying the knowledge of psychology and
social pyschology to human economic behavior.

The two may come together, like the hope for Quantum Mechanics and
General Relativity, to produce comprehensive theories. But they still
will produce probablistic, statistical results. That's science, just
as much as a numerical result that's accurate to ten decimal places.
Because "science" is the method and the discipline, not the specific
result.

Now, about those "in-betweens," such as biology and climatology.
They're sciences, too. Biology, like economics, may never be strictly
deterministic. At the finest-grain level, mutations may prove to be
random in the extreme, or indistinguishable from randomness. There
will be statistical values for their likelihood, and that may be the
end of the line.

Climatology is somewhat in the same boat. There's no denying that it's
likely deterministic, but the variables and the data may be, again,
indistinguishable from randomness. Progress will come from more
accurate insights into the determinants that can be managed with
models that can be run and employed in the real world, and that can
produce useful, statistical results.

One final comment on the value of such results. When we think of
statistics, and the discomfort we engineering-inclined people feel by
not knowing things absolutely, consider this: When we have a
statistical result that predicts a 48% likelihood of something, at a
90% confidence level and a confidence interval of +/-5, our result is
not very useful. But a 75% likelihood with a level of 90% and an
interval of +/- 10 is actionable. Bet the farm. Economic modelling is
solid to the degree it predicts results in the latter category. At
that point, it's good as gold. That is, if it's done intelligently.

--
Ed Huntress