If I characterize a problem and then create a system which models my methods to find a solution, does this vary from simply sharing an algorithm with a person capable of generalizing the method? It seems that by over generalizing and formalizing a method that it may make it usable by a person who is unable to generalize and translate algorithms. This also makes it more likely for that person to apply the method in the wrong context. If the underlying skill of creating your own system and translating it effectively to the appropriate instance was taught, then it would seem to me that more valid results would result. I see no difference in defining the approach at this level unless the person is somehow innately incapable of making that translation, and as such would likely make the wrong model and data set association in any case. With a strict correlation between perceived situation and response, the ambiguity of selection is removed, however the operating intellect ( AI or other ) is completely incapable of response beyond randomness when faced with an analogous case.
If I am doing Monte Carlo on a stochastic process looking for the hidden Markov, some might call this battleship.
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