Richard, If I can make a guess at where Jim is coming from: Clearly, "intelligent systems" CAN be produced. Assuming we can define "intelligent system" well enough to recognize it, we can generate systems at random until one is found. That is impractical, however. So, we can look at the problem as one of search optimization. Evolution produced intelligent systems through a biased search, for example, so it is at least possible to improve search over completely random generate and test. What other ways can be used to speed up search? Jim is suggesting some methods that he believes may help. If I understand what you've said about your approach, you have some very different methods than what he is proposing to focus the search. I do not understand exactly what Jim is proposing; presumably he is aiming to use his SAT solver to guide the search toward areas that contain partial solutions or promising partial models of some sort. It seems to me very difficult to define the goal formally, very difficult to develop a meta system in which a sufficiently broad class of candidate systems can be expressed, and very difficult to describe the "splices" or "reductions" or partial models in such a way to smooth the fitness landscape and thus speed up search. So I don't know how practical such a plan is. But (again assuming I understand Jim's approach) it avoids your complex system arguments because it is not making any effort to predict global behavior from the low-level system components, it's just searching through possibilities.
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