The real thing:
To answer directly the question of what models we really need:
I think we need many different models that APPEAR to be heading toward
sustainability as indicated by 1) reduction or elimination of the use
of non-renewable resources; 2) preservation or restoration of
ecosystem processes and structure, and 3) establishment or
restoration of healthy community relations. Since we cannot identify
sustainable systems with 100% certainty, then a plurality of
approaches provides a hedge against the possibility of putting all
our eggs into the wrong basket. As we learn more about sustainable
systems, we ought to be willing to shift eggs from one basket
to another. Furthermore, as we experiment with complex systems, we
ought to be open to learning things that we did not expect to learn.
Dick Richardson referred to his new method of research which
drew on W. E. Demming's work and seemed to be more flexible and open
to options than his previous research style. Although his
description of his new method was brief, it sounded similar to an
approach which Donald Schon writes about in his books on the
"Reflective Practitioner" dating from 1981. Schon, an applied
philosopher at MIT, critiques the notion of "technical rationality"
in which the "expert" deduces the most rational course of action based
upon universal principles and theories. This approach is based on the
positivist epistemology of knowledge, which works well for mathematics
and logic where all the rules and variables are clearly defined by the
community of researchers in those fields. In social and biological
systems, the variables and the rules of the game are not defined by
the researcher or practitioner; they are created by Nature or the
society at large. In dealing with these systems, we must rely more
heavily on successful past experiences than on pure theory. But if
we stick only to the tired and true, how is anything new created?
Schon proposes that the Reflective Practitioner proceeds in novel
situations in the form of a reflction in action: make a
plan, take some action, see what happens, reflect on the results,
revise the plan, take some action....
Similarly, Evelyn Fox Keller, physicist, historian and
philosopher of science, argues that the notion of "objectivity" in
science should be revised or expanded to a concept of "dynamic
objectivity" which seeks authentic and reliable information, but is
dynamic in the sense that it "actively draws on the commonality
between mind and nature as a resource for understanding. Dynamic
objectivity aims at a form of knowledge that grants to the world
around us an independent integrity, but does so in a way that remains
cognizant of, indeed relies on, our connectivity with that
world." (Reflections on Gender and Science, Yale Univ. Press, 1985, p
116-117)
To Keller, "the most striking exemplar of dynamic objectivity" is
Barbara McClintock, who was awarded the Nobel Prize in Genetics in
1983. McClintock's research approach relied upon cultivating a
"feeling for the organism." McClintock's success should demonstrate
that it is not necessary to for scientists to detach themselves from
their emotions in order to make important discoveries. McClintock's
story also demonstrates that the models accepted by the scientific
community can be demonstrated to be inadequate, but the process is
often long and painful. McClintock's discoveries were ignored by the
mainstream of genetics for nearly two decades because her findings
contradicted the accepted model (or dogma) of the time. (See: "A
Feeling for the Organism: the life and work of Barbara McClintock,"
by E.F. Keller, 1983, W.H. Freeman and Co.)
Wendell Berry also suggests that we should farm as if we were
carrying on a conversation with Nature in his essay "Nature as
Measure" found in his collection "What are People For?"(1990, North
Point Press). Berry argues that we should ask: What would Nature be
doing if no one was farming there? What would Nature permit us to do
here with least harm to our natural and human neighbors? What would
nature help us to do here?
Although Berry characterizes conventional agriculture as a
dictatorial monologue, I think this is not an entirely accurate
or useful metaphor. Conventional farmers and agribusiness must
necessarily engage in a give and take relationship with their
environment and with other species, even if they wouldn't
characterize this give and take as a conversation. I think
it might be more accurate characterize the difference between
sustainable and conventional agriculture as the difference
between two different converstations, where each with a different
sense of what is appropriate conversation. In the sustainable
agriculture conversation, we are not only concerned about
what we will GET, we are also concerned about whether our actions
promote the health of the environment and the society of which we are
part.
But what is health for a society or an ecosystem? I don't have
the answer to that question, but it seems to me that if we frame the
issue of sustainability in terms of health, it opens the door to a
wide range of human experience, including "feeling," since a large
part of health involves "feeling" well. Numerical indicators such as
body temperature and blood pressure might help explain why we don't
feel well, and might suggest what we should do in order to feel
better. Going back to my original statement, I think we need a
plurality of approaches: the numerical approach may have something to
offer, as well as the intuitive, feeling approach.
I think the numerical approach has been dominant in the
scientific tradition for much of this century, and many of those who
have invested their faith in this approach can be intolerant of other
approaches. I think an important paper was published in the Feb 4,
1994 issue of Science elucidating the limits of the numerical
modeling approach in the earth sciences. Although the paper did not
address the issue of sustainability, I think the analysis and
conclusions of the paper might help to undermine any excessive faith
that many scientists seem to place in numerical models.
The article is titled "Verification, Validation, and
Confirmation of Numerical Models in the Earth Sciences" by Oreskes,
Shrader-Frenchette and Belitz (p 641-646.) The abstract of the
article reads as follows:
"Validation and Verification of numerical models of natural systems is
impossible. This is because natural systems are never closed and
because model results are always non-unique. Models can be confirmed
by the demonstration of agreement between observation and prediction,
but confirmation is inherently partial. Complete confirmation is
logically precluded by the fallacy of affirming the consequent and by
incomplete access to natural phenomenon. Models can only be
evaluated in relative terms and their predictive value is always open
to question. The primary value of models is heuristic."
A similar article was published in Advances in Water Resources
volume 15 (1992) p 75-83 titled "Ground water models cannot be
validated" by Konikow and Bredehoef.
Although this may sound like a radical position, I think the
primary implication these papers has to do with the use of
terminology. They argue that terms "validation" and "verification"
imply that the model itself is "true" when it really is an
approximation of truth. Oreskes et al. are particularly concerned
about the misuse of these terms in describing models used in the
public policy arena.
There is also philosophically more subtle implication of these
papers: if models are not and can never be proven to be fully "true"
but can only be demonstrated to correspond to some degree to the
available measured observations, then models are partially works of
art. Oreskes et al. argue:
"A [numerical] model, like a novel, may resonate with nature, but it
is not the 'real' thing. Like a novel, the model may be convincing -
it may 'ring true' if it is consistent with our experience with the
natural world. But just as we may wonder how much the characters in
a novel are drawn from real life and how much is artifice, we might
ask the same of a model: How much is based on observation and
measurement of accessible phenomenon, how much based on informed
judgement, how much is convenience? Fundamentally, the reason for
modeling is a lack of full access, either in time or space, to the
phenomena of interest. In areas where public policy and public
safety are at stake, the burden is on the modeler to demonstrate
the degree of correspondence between the model and the material world
it seeks to represent and to delineate the limits of that
correspondence."
"Thus the primary value of models is heuristic: Models are
representations, useful for guiding further study, but not
susceptible to proof."
Comments and reactions are welcome.
Best regards,
Greg McIsaac