[OPE] Mistaking Mathematical Beauty for Economic Truth

You can formalize almost anything logically or mathematically if (i) you
know how it works, and can break it down into operational steps, and, if
(ii) there is constancy of meaning so that +1 does not become -1 in the same
conditions, or becomes 2.

If however the meaning (the significance) of something is not constant, but
originates out of the mediation of a contradiction, then develops into
something else, or even changes into its opposite, this is much more
difficult to understand computationally, especially with regard to a complex
totality which features interactions between many different variables.

The reason is, that you then have to introduce a very large number of
additional conditional assumptions, to depict the evolving meanings
computationally, to the point where theory becomes so complex, that it loses
its purpose (the purpose being to generalise comprehensibly from experience
in the most economical way possible, in such a way that it can usefully
orient behaviour).

A model, which is an isomorphism or an analogy, hopes to pick out certain
essential relationships in the subject being studied, in a way that it has a
lot of explanatory and predictive power. But obviously it is much more
difficult to fully explicate and consistently integrate all the assumptions
on which the model itself is based. In fact, we have the model precisely
because we cannot yet do this, in an all-inclusive way.

As I said, I think dialectical reason aims to portray the subject in a way
which "validates itself" because the further development of the argument
shows, through transformations of meaning, why its initial conditions are
appropriate, so that the subject becomes "self-explanatory".

>From my previous experience as research statistician working mainly on the
so-called "qualitative" (conceptualizing) side of statistical survey
research, I also can insert here a paragraph which I posted on wikipedia:

In the science of statistics, the collection of quantifiable data from
people involves a phenomenological step. Namely, in order to obtain that
data, survey questions must be designed to collect measurable responses
which are categorized in a logically sound and practical way, such that the
form in which the questions are asked does not bias the results. If this is
not done, data distortions due to question-wording effects (response error)
occur, and the data obtained may have no validity at all, because
observations are counted up which do not have the same meaning (it would be
like "adding up apples and pears"). A prerequisite of a good survey is that
all respondents are really able to give a definite and unambiguous answer to
the questions, and that they understand what is asked of them in the same
way. One could for example ask farmers "How much risk do you run on your
farm?" with a scale of response options ranging from e.g. "a lot of risk" to
"no risk". But this yields quantitatively meaningless data which is not
objective, since the interpretations of "how much risk" by farmers could
focus on e.g. on the number, size, frequency, severity or consequence of
risks, and each farmer will have his own idiosyncratic idea about that. All
farmers may suffer e.g. from a lack of rainfall, but some will personally
consider it a large risk, others a low risk and some not a risk at all.
Furthermore, in actually asking the questions of respondents and
subsequently coding the responses to numerical values, a technique must be
found to ensure that no misinterpretation occurs of a type that would lead
to errors. In other words, in designing the survey instrument, the
researcher must somehow find a satisfactory "bridge" of meaning between the
logical and practical requirements of the survey statistician, a statistical
classification scheme, the awareness of respondents and the processors of
the raw data. Finding this "bridge" involves an abstraction process which
necessarily goes beyond logical inference, theory and experiment and
involves an element of "art", because it must establish an appropriate
connection between the language used, the intersubjective interactions
between the surveyor and the respondent, and how respondents and those who
process the data construct the meaning of what is being asked of them. For
this cognitive process, it is impossible to provide a standard procedure
which will always work, only "rules of thumb"; it requires a "practical"
human insight (See Stanley Payne, The Art of Asking Questions. Princeton:
Pinceton University Press, 1980).
http://en.wikipedia.org/wiki/Phenomenology_(science)

This is merely to illustrate that mathematics cannot generate, out of
itself, all the conceptualizations, ontologies and categorizations used to
describe the world, insofar as these involve qualitative distinctions and
synthetic judgements which cannot be represented as quantities, at least not
until we have already assumed their validity, imported them, or adopted them
in quantifiable form. Simply put, quantitative procedure does not suffice to
establish and form qualitative categorizations, and this is really where
dialectical thought "begins", since somehow we then need methods to
relativise both qualitatively and quantitatively.

What I tried to highlight in what I said previously is, that the spontaneous
capacity of the human mind to create meaning and combine different trains of
reasoning, synthetically and simultaneously, carries the implication that
deduction and induction cannot fully describe what happens in the reasoning.
Similarly, when people say that the "calculative intellect" becomes
dehumanizing and alienating, they mean that by reducing everything to
quantities we have also annihilated part or all of the meaning which is
essential to understand something. The fetish of abstract thinking becomes
apparent when it fails to explain anything specific.

Autistic Marxists just want to use dialectics as a lever that catapults them
instantaneously to the "fount of wisdom" and the "grand overview of
everything", from which they can then "manage" the world as its boss, but in
reality, to depict a subjectmatter in a dialectical way, takes a lot of
scientific work and an enormous technical knowledge of the subjectmatter, so
that everything relevant is included and allocated in a non-arbitrary,
logically consistent way. So, the ability to represent a subjectmatter
dialectically actually presupposes a lot of learning to get to the point
where you can not only understand how ideas are really moving but also
explain why they move that way.

Jurriaan

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Received on Tue Sep 15 07:39:58 2009