Hidden information and regularities of information dynamics IIR

TL;DR

This paper investigates the mechanisms of hidden information cooperation in stochastic processes, revealing how hierarchical information networks and genetic codes emerge from the dynamics of observed random processes.

Contribution

It introduces a law-based framework for understanding the cooperative structures and genetic coding in hierarchical information networks derived from stochastic processes.

Findings

Hierarchical information networks encode genetic information.

Cooperative structures emerge from the law of information dynamics.

Geometrical analysis reveals curvature and complexity of information structures.

Abstract

Part 1 has studied the conversion of observed random process with its hidden information to related dynamic process, applying entropy functional measure (EF) of the random process and path functional information measure (IPF) of the dynamic conversion process. The variation principle, satisfying the EF-IPF equivalence along shortest path-trajectory, leads to information dual complementary maxmin-minimax law, which creates mechanism of arising information regularities from stochastic process(Lerner 2012). This Part 2 studies mechanism of cooperation of the observed multiple hidden information process, which follows from the law and produces cooperative structures, concurrently assembling in hierarchical information network (IN) and generating the IN digital genetic code. We analyze the interactive information contributions, information quality, inner time scale, information geometry of the cooperative structures, evaluate curvature of these geometrical forms and their cooperative information complexities. The law information mechanisms operate in 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.