I love experimental research. Modeling and simulation could strengthen the experimental data and they are complementary.
What an outstanding question, emerging out of the fine mind of Lijo Francis. Ludwig Boltzmann said, ¨Nothing is more practical than a good theory.¨¨ Therefore I would say that theoretical modeling comes first, experimental research afterwards. Of course if experiment disproves the theory, the theory should be adjusted to fit the facts, that is, the theoretical model should never be overvalued, notwithstanding its priority.
Dear Lijo,
It is really a very interesting question that frequently is being asked by our peers and generally the peers are divided to three groups: (1) those believe that experiments are much important than modelling (simulation), (2) those believe that without theory the experimental field will be retarded and (3) those who consider both as equal and should go together.
I would like to introduce some facts that may help us to weigh things correctly and make our judgment supported by facts:
First of all, models are typically used when it is either impossible or impractical to create experimental conditions in which scientists can directly measure outcomes. Direct measurement of outcomes under controlled conditions will always be more reliable than modeled estimates of outcomes.
Therefore, it is a must to make models when experiments are not available. A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way. All models are in simulacra, that is, simplified reflections of reality that, despite being approximations, can be extremely useful. Building and disputing models is fundamental to the scientific enterprise. Complete and true representation may be impossible, but scientific debate often concerns which is the better model for a given task, e.g., which is the more accurate climate model for seasonal forecasting. Attempts to formalize the principles of the empirical sciences use an interpretation to model reality, in the same way logicians axiomatize the principles of logic. The aim of these attempts is to construct a formal system that will not produce theoretical consequences that are contrary to what is found in reality. Predictions or other statements drawn from such a formal system mirror or map the real world only insofar as these scientific models are true. For the scientist, a model is also a way in which the human thought processes can be amplified. For instance, models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon, or process being represented. Such computer models are in silico. Other types of scientific model are in vivo (living models, such as laboratory rats) and in vitro (in glassware, such as tissue culture).
A model is evaluated first and foremost by its consistency to empirical data; any model inconsistent with reproducible observations must be modified or rejected. One way to modify the model is by restricting the domain over which it is credited with having high validity. A case in point is Newtonian physics, which is highly useful except for the very small, the very fast, and the very massive phenomena of the universe. However, a fit to empirical data alone is not sufficient for a model to be accepted as valid. Other factors important in evaluating a model include:
Ability to explain past observations
Ability to predict future observations
Cost of use, especially in combination with other models
Refutability, enabling estimation of the degree of confidence in the model
Simplicity, or even aesthetic appeal
People may attempt to quantify the evaluation of a model using a utility function.
References:
Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford University Press
Hacking, Ian. 1983. Representing and Intervening. Introductory Topics in the Philosophy of Natural Science. Cambridge University Press
Frigg and Hartmann (2009) state: "Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice". Source: Frigg, Roman and Hartmann, Stephan, "Models in Science", The Stanford Encyclopedia of Philosophy (Summer 2009 Edition), Edward N. Zalta (ed.), (source)
Box, George E.P. & Draper, N.R. (1987). [Empirical Model-Building and Response Surfaces.] Wiley. p. 424
Hagedorn, R. et al. (2005) http://www.ecmwf.int/staff/paco_doblas/abstr/tellus05_1.pdf Tellus 57A:219-233
Leo Apostel (1961). "Formal study of models". In: The Concept and the Role of the Model in Mathematics and Natural and Social. Edited by Hans Freudenthal. Springer. p. 8-9 (Source)],
Ritchey, T. (2012) Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling
C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p.61
Griffiths, E. C. (2010) What is a model?
Based on what I have described above I find that both are equally interesting and no one is preferable over the other. Hoping my answer will contribute to the discussion on this very very interesting topic.
Rafik
Thank you very much, Prof. Rafik for your elaborate answer and your valuable time.
Cheers
Lijo
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