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Abstract

Perhaps CS Methodology for Teaching/Learning and to be more audacious for Research when dealing with extremely complex systems as those derived from economy and social sciences could be envisaged as a scientific way to understand and to manage them. Let's see briefly how Humans have from ancient times faced problems and found their corresponding solutions.

1. Analytically, by using mathematics and logic, getting the "truth, only the truth but the truth";
2. By numerical computation, approaching to the "truth" as much as possible" throughout convergent processes;
3. Statistically, within the realm of "probabilities", approaching to a "probabilistic truth" as much as possible;
4. Heuristically, via behavior models and quantitative techniques proper of "systems simulation", approaching also to a vague and less rigorous "probabilistic truth" as much as possible;
5. "Guessing", by using new logical mathematical and statistical tools, such as "fuzzy logic", "artificial neural networks", Expert Systems, and Chaos Theory", approaching to a weak, vague and far from rigorous "estimated truth", as much as possible.

We may assign to each one of these 5 types of solutions, or approaches to, typical types of applications and/or scenarios where they apply as follows:

1. Mathematics and Geometry, Physics ("hard physics", like for instance., Optics, Mechanics);
2. Engineering, Physics (not so hard physics, like for instance. electricity, electronics), Chemical Reactions;
3. Medicine (Physiological Cycles), Economy ("hard Economy" like the hypothetical scenarios of certain "Economic Laws"), Social Sciences (its "hardest" applications), Thermodynamics and "soft" branches of Physics like Quantum Mechanics;
4. Behavioral Systems, Medicine (Patients and Group of Patients), Business scenarios, Political Sciences, Social Sciences;
5. "Soft" Behavioral Systems, High Complexity Systems in Political, Economical and Sociological scenarios, extremely soft branches of physics like Cosmo genesis.

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