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Kostis question about variable structure in SD models touches some
important points about methodology. Much of what I was going to say was
anticipated by Toms comments, which I wholeheartedly agree with, but
some elaboration might be useful.

First, a more academic point: it is very easy to get confused about
what is meant by "structure". As far as I can see, theres a hierarchy
of structure:

At the highest (shall we call it fundamental) level, structure is
whatever is invariant during the simulation. For an SD model, that is
clearly the entire model equation structure. For a genetic-algorithm
model (and similar techniques--GA for short), it would be the "rules of
the survivival game", such as selection procedures among competing
"genes" and, not least, the way the coding between the genes and their
phenotypes (the expression of that gene in the actions / properties of
the organism).

At the next level is what George R. calls active structure, i.e. the
dominant feedback mechanisms at any point in time during a simulation.
In a GA model, that might be the current ensemble of genotypes and
phenotypes and (if the model includes an endogenous environment) their
interaction in the ecological system.

Depending on the particular situation, I could imagine several layers in
the hierarchy. For instance, Khalids switches that activate latent
structure or Waynes logical superstructure lie somewhere between the
two levels just described.

Another example that came up in my dissertation work was how to measure
learning in a set of market experiments with human subjects. (Bent
Bakkens thesis talks extensively about these issues.) A simplistic
measure of learning as improvement in performance is inadequate because
performance may improve simply as a dynamic consequence of a fixed
decision or judgment procedure. If, for instance, the dynamic task
facing the subject is much like a simple search for an optimum price,
with a fairly stable and dynamically simple environment, performance can
improve over time for a wide variety of simple error-correction or
hill-climbing procedures. But real learning would imply a change in
the mental model of the subject, i.e., a change in the decision/judgment
rule itself. In my case, it was possible to incorporate and measure
this as a change in the coefficients or weights in a single rule. I
would say my learning measure falls somewhere between the fundamental
level and GPRs notion of active structure.

All this said, I think these considerations are not nearly as important
as a more basic methodological point:

Tom points out that in principle SD models could be used to set up a
universal computer and hence do anything a computer can do, adding right
away that this is of course ludicrous. Yes indeed. The "in-principle
principle" has done much to dull the mind. When you assess a method
like SD, you have to look, not at what it could do in principle but how
it tends to shape the kinds of problems you look at and the kinds of
hypotheses / theories you form.

An analogy from economics might serve: Analytical economists tend to
assume that consumer preferences are stable and exogenous. "In
principle" one could incorporate theories of endogenous preferences, but
first, it takes a lot of the power out of statements about welfare,
efficiency, etc. and second, it is cumbersome to treat analytically. As
a result, you rarely (never?) see mathematical theories in economics
that incorporate the idea that preferences can be directly manipulated
by e.g. marketing. (On the economic-history and political-economy side,
there are of course plenty of examples, e.g. Torstein Veblen, but thats
a different story.)

SD has a tendency to view problems (patterns of behavior) as created
endogenously by feedback loops. Have you ever tried, for instance, to
look at a random walk. Try it! I bet youll see cycles. There is
nothing wrong with that. SD has a set of biases and propensities for
theory building that adds a unique perspective, and it has many
particular strengths, not least the ability to communicate and explain
the model results. The key, I believe, is to be wise about the
strengths and weaknesses of the inherent biases of the approach. Thats
why Im also skeptical about grand schemes of integrating SD with other
modeling techniques (artificial intelligence, GAs etc.) to create a
bigger better SD. In principle this can be done, but in the
process, I think youd lose the methodology.

Still, I dont want to be too black and white about it. For instance,
the de Vries - Janssen study that Tom cites sounds fascinating.
Interfacing with other methods is a good idea because it helps reveal
limitations and qualify the results, not because a combined grand method
is better or truer.

Back again to the issue of endogenous structure, in the context of
evolutionary models:

John Sterman and Mike Radzicki wrote a paper for an Amer. Econ. Assoc.
conference a couple of years ago where they argued that SD was well
suited to address questions of evolutionary economics, using a simple
model of technological lock-in as a demonstration. The thrust of the
paper, as I recall it, is that the SD perspective provides a simple and
clear tool for understanding evolutionary processes. In contrast to the
black-box nature of GA models, SD can explain the outcomes in a way
that makes it more appropriate for policy making. Indeed, some GA-style
models (e.g., some of Nelson and Winters early models of innovation and
immitation among firms) can be distilled into a simpler feedback
explanation.

OK. But I would have added a word about the limitations of our
approach. By an a priori specification of the kinds of decision rules
(structures) that can be activated, our models (by definition?) cannot
be truly evolutionary. And the element of surprise and discovery, while
probably there, is much less than in GA models. It may be that in
princple a richer diversity of structure is possible in SD, but to
incorporate it, you first have to be convinced it matters and why.
Where do you get that inkling from? In my view, you can better use SD
ex post to distill and explain evolutionary outcomes. And recognize
that this is not always possible: The technological lock-in example is
simple enough that you can do it, but we have a hard time explaining
emergent properties of the system that depend upon the diversity of
populations.

--
Christian E. Kampmann
cek@chaos.fys.dtu.dk
Postdoctoral Fellow
Dept. of Physics, Bldg. 309
Tech. Univ. of Denmark (DTU)
2800 Lyngby
DENMARK
Phone: (+45) 45 25 31 05
Fax: (+45) 45 93 16 69
WWW: http://chaos.fys.dtu.dk/~cek (under construction)