Science Only Has Two Legs
portal.acm.org
portal.acm.org
Let me concoct a trivial example. Suppose you wanted to know whether a bowling ball would drop from the top of a building faster than a tennis ball. You can do this without reference to theory - just go out and experiment. Doing the experiment would not give you the complete physical picture perhaps, but it would certainly
(a) answer your narrow question and
(b) give you a constraint that any theory must explain.
The problem is that there is a class of research where people who are faced with this question, would set up a computer model. Now let's say they didn't realise they had to model air resistance. They will discover something all right, but it will neither answer the narrow question, nor provide a robust constraint for theory.
In other words, playing around with computational models is not, by itself, theory or experimentation unless you are very careful on how you tie it back to the physical world.
Taking more from your example, I would agree that scientists today are trying to answer questions by diving right into computational models (theory) without experimentation, but I believe it's because they are tackling questions that are very difficult to experiment with. I don't think that's altogether unprecedented though. There are instances of scientists producing theories without the means to test them throughout history, only to have them proven or disproven by experimentation at much later times.
I guess that's where we disagree. I respect your point, and of course I accept we are in a regime where computational models are the only options in some cases (we can't start a whole new universe off to see how it worked). That has no bearing on their limitations, though.
I think a computational model is not a theory, it is a theoretical construct. I concede this may seem like splitting hairs to everyone except me.
Theories make predictions about the world. I'd argue they're useless without this. As such, they are function of a prior state, returning the new state.
Certainly you can't validate your theory without doing physical experiments, or at least having lots of data that the theory was not based on, to check it. But validation of a theory is distinct from its representation.
I'd say ok, that's a great theory about your model of pressure and your model of temperature in your model of the universe.
And you say, no no, this is a theory about actual pressure and temperature in the actual universe, just like your theory.
In that case your model is not enough. I also need reason to believe that your models of temperature and pressure are relevantly similar to actual temperature and pressure such that relational properties that hold of the entities of your model also hold of the entities they're supposed to represent. If it turns out you modeled temperature as a jpeg representing a picture of a cat, then how is that a theory about the temperature?
Whereas my theory is a theory about temperature simply because my theory says: temperature, that real thing in the world, will do such-and-such.
Your theory assumes these things, but you want to say that the model is invalid because it also assumes these things.
But the more important misconception you appear to have is that you are not considering the human element; no representation of the theory has meaning unless a human interprets it. Programs manipulate symbols; but it is the human who chooses what the symbols mean, and that includes incorporating theories / models implied or assumed about those meanings. It's no different for a theory written down in a book.
Similarly, programs manipulate symbols. It's up to the person who runs the program to imbue those symbols with meaning.
F=ma; that's some coloured pixels on your screen. It's also an expression of one of the laws of Newtonian mechanics. It can be used to calculate the approximate acceleration of a mass after a particular force has been applied to it. But the coloured pixels on your screen isn't doing that; you need to apply your mind.
Similarly, here's a function:
function calc_a(m, F) { return F / m; }
I apologize if anyone else has mentioned this, but I think most working scientists are uncomfortable with purely computational work and "theories" because they don't appear to be falsifiable within the framework developed by Popper. Current global climate models (GCM) are only subject to verifiability and this relegates them to a lower status then, say, Maxwell's equations. Maxwell's equations are capable of being used to explain almost all macroscopic electrodynamic phenomena, at least within the confines of classical physics. OTOH, there is no proper theory of the climate than can be treated with equal footing. There are only computational models and input data. There is a large amount of parameterization and data treatment (cynics would say massaging) that need to be done to get the models to converge.
A computer program just a different language of expressing the theory... Hence, I see no reason why one should separate theory and computer programs.
PS: There are limits on science, and the ability to conduct an actual meaningful exponent is one of the largest ones.
These two alone are much more than a mere thought experiment.
A thought experiment can tell you that if A then B. A model is a means of producing a prediction B from A by means of long, complicated calculations instead of simple logical inferences. In both cases the conclusions should be evaluated before accepted as physical truth.
Consider what is meant by a thought experiment in popular literature. Take Einstein's elevator. Would you call that "Einstein's elevator theory?" Or is that a prediction based on the theory of equivalence?
Maybe in this vein a clear distinction is that a thought experiment is a proof discovery technique. It is working with heuristics and intuition. At the end there is something; a statement or prediction that seems right. But no matter how much intuition confirms it, in the physical sciences it needs to be tested and in the mathematical sciences it needs a formal proof. Beyond that, it is just another hypothesis. After that it leads to a theory.
If you don't standardize your terminology first, then you're just discussing the boundaries of your personal definitions, instead of embarking on actual intellectual discovery of the terrain.
Something isn't intellectual discovery just because information is exchanged and both people feel they are learning stuff. Intellectual discovery makes sense if you are trying to structure the knowledge and are connecting it to knowledge you already have. Without things to connect it to, such as a shared definition of 'theory', you end up running around in circles.
It's like being dropped into a maze and not trying to solve it, but just running around in it. It may be fun, and you won't hear me complain, but as soon as someone starts wondering whether they are going too much into the details of solving it ("maybe I'm splitting hairs"), I feel obliged to point out they haven't really started to solve it. They've just been randomly discovering the territory, not noticing whether they returned to the same point several times.
I'm not trying to be disparaging; I'm just pointing out that talk of 'splitting hairs' is really premature.
Oddly, you nearly admit the usefulness of it. "They've just been randomly discovering the territory". Discovering territory is valuable, whether it's random or not!
But the oddest thing is that you don't provide any useful information. You don't link to a page with the info you think we should read, or to a book on the matter, or a paper. Nothing, but condescension.
As a starting point, http://www.teach12.com/ttcx/CourseDescLong2.aspx?cid=4100 may be a pretty decent introduction. There are torrents floating around if you want a taste.
https://notes.utk.edu/bio/greenberg.nsf/0/f2d03252295e0d0585...
In physics, theories often describe a world which is both infinite and continuous, which is hard to encode in a form suitable for computation; typically finite-volume and discretisation approximations (at the very least) are made.
In these situations the theory represented in code isn't the same as the theory you started with. One has to work quite hard to demonstrate that the differences are quantitatively understood and under control, and that the domain of applicability is understood.
I would classify numerical/computational models, along with mathematical and philosophical models, under "theory". Number-crunching and heavy data analysis is a way to categorize and present observations. None of these deserve to be treated as new and separate legs of science; they are merely subsets of the existing legs of theory and observation.
The inciting text can be found in the description section here (click to expand the full Aims & Scope section): http://www.elsevier.com/wps/find/journaldescription.cws_home...
An excerpt:
"Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory." - Peter Sloot, Editor-in-Chief
I do agree more with this article that it's a kind of theory, but I think the people who disagree aren't only computational science people, but also (some) theory people.
Interestingly, perhaps the field that most strongly holds to the view that models need testing is weapons research.
In other words, computational transparency is important if computation is just an extension of the traditional scientific method.
So the question is: is computational science like a car or like a leg. Is only a tool or is something that will make a change in the way we think and conceive experiments, in the way we consider thinks to be possible and shape our future?
In ancient times there was only one leg for science, that was authority. I see no problem which the three or four legs concept. The only thinks I would consider silly is to confuse a leg with a finger. Anyway, if you don't want to use body analogies, don't ask for know many legs science has in the first place.
what a scientist does is to make a model which is the computed than she makes measurements and obtains errors - the error or the residuals - is the knowledge
people may call computeds -theory -model -hypothesis -framework and similar words but the process is always the same
for instance ptolemaic theory is a mathematical framework that results in very good residuals which means that ptolemaic model saves the naked eye observations very well
if as the op writes an experiment generates -40 terabytes of raw data per second- you still have to model it and obtain residuals
but the real interesting problem facing contemporary science is that now what is -computed- and what is -measured- are no longer clearly separated