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

[John M <***>]

Dear John,
no comments here, just a request. I read your logical text, then scrolled it
slowly down, then up, I could not find what you would propose as the brief
description of a simulation.
Could you condense it so tht even I can understand?

John M
----- Original Message -----
From: "John Kineman" <***>
To: <***>
Sent: Friday, December 17, 2004 2:44 PM
Subject: Re: Modeling vs. Simulation [from "fundamental problems in
physics"]


> 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
> >
> >