Vladimir Igorevich Arnold (1937-2010) on mathematical models [1]:
The scheme of construction of a mathematical theory is exactly the same as that in any other natural science. First we consider some objects and make some observations in special cases. Then we try and find the limits of application of our observations, look for counter-examples which would prevent unjustified extension of our observations onto a too wide range of events.
As a result we formulate the empirical discovery that we made (for example, the Fermat conjecture or Poincaré conjecture) as clearly as possible. After this there comes the difficult period of checking as to how reliable are the conclusions.
At this point a special technique has been developed in mathematics. This technique, when applied to the real world, is sometimes useful, but can sometimes also lead to self-deception. This technique is called modelling. When constructing a model, the following idealisation is made: certain facts which are only known with a certain degree of probability or with a certain degree of accuracy, are considered to be “absolutely” correct and are accepted as “axioms”. The sense of this “absoluteness” lies precisely in the fact that we allow ourselves to use these “facts” according to the rules of formal logic, in the process declaring as “theorems” all that we can derive from them.
It is obvious that in any real-life activity it is impossible to wholly rely on such deductions. The reason is at least that the parameters of the studied phenomena are never known absolutely exactly and a small change in parameters (for example, the initial conditions of a process) can totally change the result. Say, for this reason a reliable long-term weather forecast is impossible and will remain impossible, no matter how much we develop computers and devices which record initial conditions.
In exactly the same way a small change in axioms (of which we cannot be completely sure) is capable, generally speaking, of leading to completely different conclusions than those that are obtained from theorems which have been deduced from the accepted axioms. The longer and fancier is the chain of deductions (“proofs”), the less reliable is the final result.
Complex models are rarely useful (unless for those writing their dissertations).
The mathematical technique of modelling consists of ignoring this trouble and speaking about your deductive model in such a way as if it coincided with reality. The fact that this path, which is obviously incorrect from the point of view of natural science, often leads to useful results in physics is called “the inconceivable effectiveness of mathematics in natural sciences” (or “the Wigner principle”).
Here we can add a remark by I.M. Gel’fand: there exists yet another phenomenon which is comparable in its inconceivability with the inconceivable effectiveness of mathematics in physics noted by Wigner – this is the equally inconceivable ineffectiveness of mathematics in biology.
“The subtle poison of mathematical education” (in F. Klein‘s words) for a physicist consists precisely in that the absolutised model separates from the reality and is no longer compared with it.
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I did remove one comment in parentheses to shorten the passage. Hence, this is not an exact reproduction of the original passage in [1]. The “Wigner Principle” that Arnold refers to is the thesis of Eugene Wigner’s famous article [2]. Lastly, I do wonder how many theoretical chemists, theoretical physicists, and systems biologists out there were angered by Arnold’s “heretical” thoughts.
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References
[1] Vladimir Igorevich Arnold, A.V. Goryunov (translator), On teaching mathematics, 1998.
[2] Eugene Paul Wigner, The Unreasonable Effectiveness of Mathematics in the Natural Sciences, February 1960.
