Hidden information and regularities of information dynamics IIR

Vladimir S. Lerner 13603 Marina Pointe Drive, Marina Del Rey, CA 90292, USA, [email protected] Abstract Part 1 (Lerner 2012) has studied the conversion of observed random process with its hidden information to related dynamic process applying entropy functional (EF), as entropy measure of the random process, and information path functional (IPF), as information measure of the dynamic conversion process. The variation principle (VP), satisfying the EF-IPF equivalence along a shortest path-trajectory, leads to information dual complementary maxmin-minimax law, which creates a mechanism of arising information regularities from a stochastic process. This Part 2 studies a mechanism of cooperation of the observed multiple hidden information process, which follows from the law and produces cooperative structures, concurrently assembling in a hierarchical information network (IN) and generating the IN’s digital genetic code. We analyze the interactive information contributions, information quality, inner time scale, information geometry of the cooperative structures; evaluate a curvature of these geometrical forms, and their cooperative information complexities. The law information mechanisms operate an information observer. The observer, acting according the law, selects random information, converts it in information dynamics, builds the IN cooperatives, which generate the genetic code.