On the auto-randomization of knowledge

In this paper, we first apply traditional computability theory to prove that the randomization problem, as defined herein, is recursively unsolvable. We then move on to extend traditional computability theory for the case of k-limited fine-grained parallel processors (i. e., temporal relativity). Using this modification, we are able to prove the Semantic Randomization Theorem (SRT). This theorem states that the complexity of an arbitrary self-referential functional (i.e., implying representation and knowledge) is unbounded in the limit. Furthermore, it then follows from the unsolvability of the randomization problem that effective knowledge acquisition in the large must be domain-specific and evolutionary. It is suggested that a generalized operant mechanics will be the fixed-point randomization of a domain-general self-referential randomization. In practice, this provides for the definition of knowledge-based systems that can formally apply analogy in the reasoning process as a consequence of semantic randomization.