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(... and/or the abstraction of nature in
science!)
While we are discussing mathematical logic and the nature of
systems which can be completely formalized (hence, are "computable"), I thought
we might as well throw a little more bio-diesel on the
fire...
From page 213 of "Anticipatory Systems", Robert Rosen
wrote:
"Let us suppose we are given only a single meter, which
may interact with the states of some natural system N. As far as the meter is
concerned, the only quality of N visible to it is the one embodied in the
observable it measures. As far as that meter is concerned, no other qualities of
N exist; and indeed N behaves for that meter as if it possessed no other
qualities. Instead of seeing the full range of interactive capabilities which N
can manifest, then, the meter sees only one.
It is in this sense that we shall say that any observation
procedure, applied to a natural system N, generates an abstraction. For instead
of telling us about N, the observation procedure can by definition tell us only
about a single quality of N; it must necessarily forget, or neglect, all the
other qualities which N may manifest. It allows us to see only a projection of N
along a single interactive dimension. This kind of forgetting, or neglect, of
existent qualities is the essence of abstraction.
In popular parlance, "abstraction" has a pejorative
connotation. The antonym of "abstract" is "concrete"; thus in the popular view
(which is shared by many empirical scientists) abstraction is a property of
theory, and one engaged in the direct observation of nature cannot be accused of
performing abstractions. Above all, one who observes nature directly must always
be solidly anchored in concrete reality...
We see, however, that the facts are quite otherwise.
"
Robert Rosen often said "there is nothing more abstract
than a measurement." (Raw data, without the referents, is meaningless.) He
viewed theory as the way to actually make empirical, i.e.; direct
observation/experimental science "less abstract". (The theoretical
underpinnings are what suggest where to look, what to observe, what sorts
of experiments to design in order to get useful data that pertains to the
questions we have. In other words; information.) He believed
that any science which proceeds from, or is based on, an unsound
theoretical basis will be of questionable value: It cannot fail to be
incomplete and/or inapplicable, at least part of the time, even in the
best-case scenario. My father asserted that this is precisely the situation
in science, currently. Science today is based on the notion that all
systems in the universe (indeed the universe, itself) are "just like a machine."
This assumption is further extrapolated into a presumption that the ways we can
study machines and the things we learn from such a study are going to be both
relevant and applicable to all of material reality. So, for example, when
there are foundational questions to be answered about the epistemology of any
given system, the way science studies the system is by taking it apart and
studying all the subsystems, then taking the subsystems apart and studying all
the components, and ultimately taking the components apart and
studying all the raw ingredients.
This turns out to be useful when working with certain kinds of
systems, but less and less useful with other kinds. Among the kinds of systems
it works very well with are machines, which is not a surprise, of course.
However, no amount of studying of the ingredients and components of a living
system will ever be able to answer the question; "Why are living organisms
alive?" My father spent his life's work finding out the answer to that question
as well as uncovering the reasons why science currently has so much trouble
learning fundamental things about biological systems. As it turns out,
it's NOT that biological systems are "too strange and bizarre" compared to
everything else in the universe-- on the contrary; Einstein was right that there
is a common thread running through the fabric of all systems, all phenomena. He
was also right that it has to do with the power that can be contributed to a
system by a relation.
All systems have relations as ingredients, in a sense; as
parts. But not all relations will have the same impact, even when the
relation involves interaction between the same two components. The sun and the
Earth have a set of distinct relations which are responsible for the nature of
the temperature conditions we experience on our planet. If our planet were in a
different set of relations-- say, similar to Mercury's-- the effects
would be entirely different. The common thread that runs through all systems in
the universe is based on this fact; that all causality is relational. That is;
it is the interactions between "things"-- as specified by the
particular relations involved-- which drives causality.
That conclusion was reached after a great deal of investigation
into the history and the foundations of science, searching for the reasons why
certain aspects of biological systems were proving to be unapproachable using
the scientific tools which existed "on the shelf," as it were. His
diagnosis was that several incomplete and/or frankly erroneous assumptions
and conclusions were inadvertently built into the foundations of science
and were never really scrutinized or re-analyzed adequately, since.
The most damaging of them: the Cartesian "machine metaphor" as a
conceptual model in science for all natural systems. About this
particular foundational idea, my father wrote: "The machine metaphor isn't just
a little bit wrong, it is entirely wrong, and must be
discarded".
The changes to science which are required (in order for
science to learn about, understand, and solve problems/make accurate
predictions with regards to complex systems) are not just "new
capability" that can be added onto what already exists. There must also be an
excision of certain conceptualizations of natural systems which
are currently mortared into the foundations themselves. I can attest
to the fact that, in all of my research on the internet into various
different theoretical descriptions of "complexity" (all of which
differ markedly from the Rosennean one), I have not seen any serious
scrutiny of the foundational concepts that underlie all of science (and,
therefore, become part of the definition for what most people view as being
"scientific").
Furthermore, if there are not some foundational changes made, then
any new "scientific" capability that we generate is likely to proceed from,
and/or incorporate, at least a few vestigial aspects of the same flawed
conceptualizations. The primary problems that science was concerned
with in previous centuries, up to about the middle of the 20th
Century, were nearly always much simpler than the issues we are facing
today. Examples include: Aspects of our solar system's mechanics, industrial
applications, mechanical engineering, architecture, civil engineering,
chemistry... those were/are areas where the relational nature of causality can
(with relative safety) be either ignored or treated entirely as a
series of variables in notation. However, times are changing.
As humanity is increasingly being called upon to manage resources
and solve problems in the planetary biosphere-- and with human physiology, human
social systems, and the human conscious mind, etc.-- we will be dealing almost
entirely with complex systems.
Judith
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