Re: Modeling vs. Simulation [from "fundamental problems in physics"]

[John Kineman <***>]

Tim, Howard,

Sorry to raise this, but it seems to me that the definition of 
simulation is too broad. Correspondance of behavior of a formal system 
(model, simulation, description, etc.) with behavior of the natural 
system, as indicated by observables, describes all of modeling in my 
opinion, i.e., the modeling relationship. We cannot "see" the underlying 
causality structure, so it must be inferred from behahvior and the 
modeler's insight(?).  The only difference between what present day 
scientists would call a simulation and a model would seem to be that we 
call it a model if we believe its sub-models represent real processes in 
nature. We evaluate various model structures based on beliefs about how 
nature is structured projected from these sub-model experiences; but the 
final test is through correspondence between formal and natural behavior 
- there is nothing else that can be tested.

Plant dispersal models, for example, can be based on process dynamics, 
i.e., structuring the model based on prior knowledge about water and 
nutrient uptake rates, growth rates corresponding to these processes, 
climate effects on the rates, etc. Simulations of general behavior can 
be done statistically, based on generalized statistical correlations 
with environmental variables. For example one can produce a statistical 
estimate of net annual primary production based on average rainfall, 
nutrients, temperature, etc.  Or one can attempt to model exactly what 
each species is doing physiologically in terms of time-dependent 
equations and wire all that together to get a production estimate. The 
first approach can be iterated on varioius time steps to re-introduce 
the dynamics an that produces a "simulation." The rate-based approach is 
generally not described as a simulation, because it employs sub-models 
of what everyone thinks are "actual" causes or rate-based controls on 
productivity. The word "simulation" is used when one hasn't attempted to 
emply all that is known about underlying causes. It is thus considered 
only to be characteristic of what can happen, but not "accurate" enough 
to give detailed predictions. Hence, simulation seems to be most related 
to characterizing the type of behavior, vs. attempting to predict as 
much detail as we can about it.  Simulations are good for producing 
"scenarios" of possible future system behavior - i.e., a 
characterization of what could be. Whereas process models are sought 
after to make more accurate predictions of future states, such as 
quantities, flows, storage amounts, etc. taking into account all the 
significant exchanges in an ecosystem.

Another example of simulation might be fractal landscapes. These would 
be a simulated landscape because they "look" like topography and one can 
match the general fractal dimension (wiggliness) to an actual landscape 
to make it look characteristic of some real landscape, but the details 
will be all wrong - hills in different places, etc. but looking like 
real hills. The fractal simulation does not correspond to observations 
of the actual landscape at a detailed level, but it does represent one 
aspect of that landscape, its general wiggliness.

Given this vernacular (I'm sure it can be better described than i just 
did), the Rosen view enters the picture in a very odd way, I think. 
Essentially, it is saying that the dyanmical model is not really any 
more causal that the simulation in the case of modeling organisms. That 
is is no better a model, and in fact because it tries to hard it may 
even be worse. Experiment bears this out, as currently the statistical 
models of ecosystem patterns actually seem to out-perform the supposedly 
dynamical/causal ones. In other words, given that we really don't know 
enough about the causalities to model them, it is better to model some 
more general aspect of the system, using fewer assumptions and fewer 
parameters subject to error. There is a growing principle of appropriate 
modeling, developing in some circles, like appropriate technology, where 
one does not use more than is necessary for a given problem. That's 
where most ecosystem modeling seems to be these days, with regard to the 
biotic interactions. With regard to the biogeochemical flows and rates, 
however, the dynamical models do best, except for the uncertainty of the 
biotic component's influence; for example how the biota may compensate 
in unpredictable ways over time to changed physical conditions.

Now if Rosen's view is taken seriously, it explains why the supposed 
causal models of the biota don't work very well -- namely that they 
don't understand the true causalities and may be comming at them all 
wrong (i.e., from a mechanical mind set, because that mindset does work 
best for the physical parts). Most causes arethus  imagined to be 
physical in nature and law-like. Closed-loop entailments can only be 
accounted for by incorporating iterative loops in the law-like process 
outputs, or the statistical estimates. It can be applied to both kinds 
of model, statistical or process/dynamical; but it can only iterate 
law-like equations, and there is no control of accumulation of error. So 
the result of the iteration is generally fanciful, but nevertheless 
informative because it supposedly represents one possibility for future 
development. Then what some people do is run multiple simulations, 
varying the parameters reasonably and filling out the random number 
spaces, to try to map out the full range of future possibilities. Then 
managers can at least say, here's the best and worst case scenarios.

But even the above approach fails, because there are non-linearities 
that can't be predicted from prior behavior. The entire system may 
"flip" into a different entailment structure, where the same 
purtabations in parameters or the range of random variables, corresponds 
to entirely new behavior because of the entirely new organization of the 
system. In essence, an ecosystem can reach a limit of its ability to 
retain its identity, and then it simply degrades and re-organizes into a 
new identity. Organisms do the same thing, but their identities are so 
obvious and stable in one mode, that we call any reorganization "death." 
That is the kind of causality - the causalities involved in  stability 
and reorganization, that I think Rosen was most concerned about, and to 
my knowledge there are no successful models that deal with it. The 
non-linear mathematical approach itself seems to be no more than a 
simulation, i.e., an instrumentalism (matching exercise between equation 
and natural behavior with no underlying causal theory).

My sense is that because of the existence and importance of this deep 
level of organizational causality (pardon the invention of new terms), 
Rosen is then saying that everything model we currently have is really a 
simulation, even though many scientists humored themselves into thinking 
they were dealing with causalities. My own view is that Rosen's point is 
a good one, but a bit too harsh for many honest scientists who know 
their limits. The physical causes are important and constraining, but 
the problem is just that they are not a complete enough description of 
the system. Where his "irritating" statement is valid and appropriate, 
however, is in countering the dogmatist who does not know his limits and 
claims that the physical process model is all there should ever be, and 
that it is capable of eventually explaining even the non-linear 
behavior. I think a higher-level theory will be needed at the system 
information/organization level to deal with the organizational problem.

Sorry for the length and rambling, but I hope this paints a useful 
picture of ecosystem modeling.

John Kineman

Tim Gwinn wrote:

> Howard,
>
> HP:
>  
>
> > I meant that Rosen's meaning of "model" requires conforming with
> > the causal
> > structure of the system, whereas his "simulation" requires only
> > conforming
> > with the observables. Is that your interpretation?
> >    
>
> TG: On the model part of your statement, I would use the term "congruency"
> rather than "conforming", but otherwise I agree.  As to simulations, I'd say
> it requires only the correspondence of behavior of the simulation with
> correspondence of behavior of the natural system, but I think that's what
> you are saying, so we agree there. That puts us back to the original Rosen
> modelling relation, as far as I can see.
>
> Regards,
> Tim
>