In Probabilistic Information Theory, as in Algorithm and Programming theory, there is no need to worry about how we understand or how we come to create meaning. Within each of these complex cases, we run into the same paradox: formal identity between maximum complexity and randomness (i.e. disorder within maximum statistical homogeneity). And, in both cases, the solution to this paradox consists of ignoring that sense and meaning exist a priori while supposing it does, which eliminates the hypothesis of randomness. It isn’t until recently that paper in Algorithmic Complexity have attempted to solve this paradox, taking into consideration a definition of complexity that is meaning based. A first approach is to account for complexity in noise. A second, more recent approach, uses Multi-Agent Cellular Automata Systems to catch the emergence of functional meaning within the self-organizing networks. As a result of these approaches, we find a large under-determination of theories within the facts, and a small number of networks that allow us to retrace the origins and even quantify them. This under-determination of theory appears to be the most spectacular expression of what is natural complexity.