*… said the problem to the scientist.*

From Peter J. Denning (2007) Computation – a new way of science

Chapter “Representations of the Infinite”:

**“***The algorithm is another example of a finite description of the infinite. In this case, the entities represented are the computations that the algorithm can generate. This is what makes programming so difficult. The programâ€™s designer needs to be able to show that every computation in the infinite set meets the input-output specifications of the algorithm.***“**

**Is it really the infinite that causes the trouble in software development or isn’t it actually the complexity?** This seems important, since imho solving infinity puzzles is significantly different from handling complexity.

**While dealing with infinity is more about the existence of a solution dealing with complexity is mainly about finding the optimal effort/ benefit trade-off** (“ok, our solution cannot deal with the special-special-special-case, but therefore it’s only half the prize”). The latter is a core issue in everyday software business.

Have fun

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*ps*

related former post: Why finiteness counts

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About modelpractice

Modeling Theory and Abstraction Awareness in strive for scientific rigour and relevance to information systems engineering.

Well… I should have say it the reverse way: infinity is about a bug-free software which never fails working whatever the usage, while complexity is about a software which answers to a question. Infinity costs a lot but doesn’t deliver much value once the day to day usual work may be done. And complexity is at first a matter of having a well formed question: the mathematical existence of the question is half work done for the software which answers it… Costs trade-off is here about having reasonnable business requirements ðŸ™‚

Hi Vincent

yes, to put it in a one-liner: “infinity is a about finding the solution, complexity about defining the problem” (ok, bit over-simplified perhaps?)

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