Re: Promoting Rosennean concepts

["John Kineman" <***>]

Hi everyone,

This is a problem I have been thinking about as well, as I have been
recommending that an emphasis in our cooperative institute for research
in the environmental sciences restart its complexity group with a
Rosenean focus. Where's the proof of its value and applicability??

I'm finding that a good case can be made with regard to informatics,
particularly ecosystem informatics. The question is, if ecosystems are
complex -- and we are now attempting to manage them as whole entities
instead of in terms of single resource (linear) models -- then what
changes are needed in the way we produce or organize information? This
is also a very large and presently debated issue with regard to the role
of science (in general) in policy. There is a rising tide of criticism
of science for not effectively linking with policy and management
decision making, to the point that some groups are advocating that (a)
we don't need nearly as much science (amazing generalization), or (b)
science priorities should be dictated by specific policy decision needs.
This has been openly discussed at US Executive Branch levels and in
Congress. Susan Avery from our institute (CIRES) just spent a year
helping to craft the new global climate initiative and research program,
adding an explicit policy element.

My own conclusion is that Rosenean theory directly and importantly
relates to the design of information and information enterprises that
themselves are intended to represent a complex natural system. It is a
perfect application of the modeling relation. As a design issue it need
not be quantitative, and indeed cannot be. We are not talking about how
much information is needed or how many ecosystems we improve in a year
(that is an actual current "performance measure" believe it or not), but
principles such as multiple outcomes, inability to predict exact future
states, need for all perspectives (and thus stakeholder involvement in
assessment and management programs), requirement for adaptive management
(Hollings' work, which means one needs to actually study ecosystem
response to management actions to know if they are effective, because of
complexity; and that the linear check-box performance measurues are
ridiculous), importance of obtaining information about context along
with information about states, etc. So, in this arena it can make a
tremendous difference. Also, the modern tide is toward "integrated
science" combining physical, ecological, and sociological perspectives.
In the social sciences there are many strong analogies with this form of
complexity, but strongest, I think, in information dependent fields.

I presented many of these ideas over the course of three international
meetings of the Global Terrestrial Observing Systems (GTOS) working
group on coastal GTOS; and they were very well received. A result, which
will come out in the WG implementation plan, is to introduce an explicit
element focusing on a functional view of the coast and coastal
ecosystems, and an adaptive mapping approach that is justified on the
basis of Rosenean complexity. So in this area I am encouraged about
applicability and I am finding quite a large number of sympathetic ears.

Just an update on where I'm attempting to apply it.

Tim Gwinn wrote:

>>-----Original Message-----
>>
>>
>>
>--snip--
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>
>>I am beginning to think what's required are little guides to Rosennean
>>Theory, defining the terms and pointing out the cross-applicability
>>potential in the parlance of each discipline, such that there will be "The
>>Ecologist's Guide", "The Medical Doctor's Guide", "The Physicist's
>>Guide".... etc.
>>
>>Judith
>>
>>
>
>Why are his works so unknown? Here are some of my thoughts, in no particular
>order. Looking at these, I am not sure that having guides for each
>discipline would (or could) resolve or circumvent most of these issues:
>
>* Heavily technical style is both its strength and an obstruction to its
>acceptance.
>
>* The very idea that there are physical systems outside the grasp of
>mathematical (algorithmic) models is almost totally disbelieved.
>
>* The very idea that there are physical systems not amenable to
>epistemological reductionism is almost totally disbelieved.
>
>* Rosennean complexity has no "killer app" (or, as Jeff Pridaux described it
>in a post some time ago, 'the elusive "practical application"').
>
>* Institutionally, teaching, asserting or promoting Rosennean complexity
>appears to be discouraged at every level.
>
>* "Of what use are models that can't give me a numeric answer!!??"
>
>* Of what use are papers submitted to peer-review journals that can't show
>numeric answers?
>
>* Historically has been difficult to physically access writings - both
>books, and papers published in journals that are not easily found.
>
>
>I was just reading the article on string theory in the Nov 2003 issue of
>"Scientific American". Here is a theory about which many books, articles and
>papers have been written, and yet, this theory has so apparently far not
>produced any real testable predictions!! But it continues to garner
>interest, institutional support, and "public awareness". Why is this?
>Certainly, string theory is compatible with, and seeks to further, the
>crusade of reductionism. It also does not deeply threaten the paradigmatic
>limits of any of the current QM and macroscopic physics. Physicists commonly
>expect the next "Einsteinian breakthough" can only come from either the
>ultra-microscopic or the cosmological frontier. The proponents of string
>theory also appear to actively promote it from within the institutional
>setting.
>
>String theory at least holds out the promise of providing science with
>testable (numeric?) predictions at some point in the future of its
>development. Whether it will eventually fulfill those promises is a moot
>point right now. (Speaking from my highly unqualified vantage point, I am
>extremely dubious of the entire program.) But at least it makes such
>promises to science, the institutions, and the public. And it gets
>recognition.
>
>On the other hand, what promises of testable predictions does Rosennean
>complexity hold?? I can think of none, at the moment.The only predictions I
>can think of are predictions of what _cannot_ be done, such as predicting an
>inability to solve certain physical problems in purely algorithmic form. But
>the endeavor of science is perpetually fraught with inabilities and
>difficulties, many of which are eventually overcome. So these negative
>predictions may be less than compelling, and do not thereby lend adequate
>support to Rosennean complexity. In effect, if we view Rosennean complexity
>itself as a model sitting on one side of a modeling relation, it needs to
>make positive predictions about what the science on the other side of that
>modeling relation will encounter; otherwise, that modeling relation between
>complexity and science may never commute.
>
>It is midnite now, so I pause here. I hope this will still make sense to me
>in the morning. :)
>
>Regards,
>Tim
>
>