John K. It is amazing how differently can be a description worded, even by persons basically thinking similarly. The aspect of 'causalities' did not even occur to me in identifying 'models', amd 'prediction' was not in my ballpark of thinking. My original 'modeling' came from the analytical chemistry, wherein composite matter was evaluated into components already knowable with no concern to yet unknown aspects. I did not include "over time" aspect either. Then again I never wanted to predict a hurricane from a model. The second phase was based on RR's elimination of a maximum model, what I translated into model = limited and cut, like a topical distinction from the wholeness of interinfluencing other territories. Which landed me into the "map = model" and even "territory = a wider model" all the way to the reductionistic sciences as models within their topic.
At this point I needed to separate 'my model' from the (math) simulational modeling science, where processes are calculated by simulational processes from other field, where the outcome can be predicted. (We used such so called "modeling" in pharmaceuticals, simulating the physiological action of a drug (quantity?) by equations taken from other fields, electrical circutery, mechanical contraptions, etc.) (My) model concept landed me at the scrutiny of "a cause" in the totally interconnected world-process assigning a model-appearance to one known origination within the aspects we consider in that model. I call a 'system' a model, because 'system' limits the view at the cut-off boundaries assigned topically to that particular process we include into that particular system. This is why I am so happy with RR's "natural system" in which I do understand (include?) those influences/ramifications which are cut out by the reduced (model)system identification. I am not sure how can I translate this stance into 'organization' vs. 'machine', the former one may not be cut - however the langage is mixed. This is why I preferred to speak about the networks of networks unlimited, extending the efficiency of interactions as a natural basis for (some) topical identifications. It is NOT closed loops, it is open. Differential influencing is not "law of physics (of nature?)". It is a variable and ever changing process in the unlimited potentials of the world.
So I became vague and imprecise, but what can you be in a starting idea which does not even have a glossary <G>?
I deny the use of 'reality': we have only an interpretation of part of it. What we see as objective, is subjective interpretation. What we see as reality, is our virtual decision about a partial vue. If our models are adequately cut, we can construct some 'valid' predictions - within that model. 'Statistical estimates' depend on the choice (and number?) of cases included into the 'counting'. Probability is a game, the classic example that casinos ALWAYS win depends on their limiting design of the allowed cases. If SimEarth (I don't know it) is adequately supported by wide enough base-lines then we really can kill the meteoroligsts if the prognosis fails. About the 'mimicking' of circumstances: they change like everything. The interinfluencing of 'remote' networks is subject to seemingly unrelated effects which makes the stupid butterfly effect less stupid indeed.
The best thing that could happen to humanity was the reductionist science way, to gather and organize observations and draw conclusions (predictions) within that edifice which grew out from the chaotic. Maybe I should mention also religion, as 2nd best, allowing humanity a relaxed evolution. In my scientific agnosticism, however, I think it is time to step forward from both. Carefully and cautiously. No "R" before the evolution.
Thanks for your thought provoking lines
John M
----- Original Message ----- From: "John Kineman" <***> To: <***> Sent: Friday, December 17, 2004 5:53 PM Subject: Re: Modeling vs. Simulation [from "fundamental problems in physics"]
John M.
Sorry to not be explicit, but i think it is not clearly definable in practice. I don't have a theoretical definition for it as Rosen does, or appears to. In practice, it seems to be any representation of a natural system over time that is not constructed on sub-models of causalities, ie. generally thought of as the processes (efficient causes). However, it is quite subjective to decide when a model has taken into account enough causalities to qualify as a prediction. Most climate models and hurricane models are referred to as simulations if they depict a possible event. For example, we have a weather simulator based on a database of historical storms. If one specifies a set of conditions and a location, it will construct a typical storm based on prior storms and then show it to you in the desired location. You can then examine how such a storm would affect that location, were it to occur. It is a hypothetical model run, in that case. Such simulations could be causally based or based on statistical estimates - either one. It is not a model because it does not refer to actual or expected conditions, only hypothetical ones. Another example, the game SimEarth actually had dynamical equations to deal with global climate and ecosystem change. It was simplified for game purposes, but it employed some knowledge of causalities. One could then construct hypothetical Earth systems and watch them evolve as one altered conditions, added factories or carbon sequestering units, etc. The idea was to see if you could stablize the system.
the distinction Rosen makes about simulation seems to more refer to how
good one's model is - whether or not it is based on reality or is just
meant to mimic or look like reality. But the problem I have with that is
that all scientific models are mimics to some degree, because we don't
know all the causalities or if we've formalized them adequately.
Best,
John K