The mathematical law of evolutionary information dynamics and an observer's evolution regularities

TL;DR

This paper develops a mathematical framework based on entropy functionals and a minimax variation principle to model evolutionary information dynamics, revealing regularities like order creation, hierarchy, and self-organization in evolution.

Contribution

It formulates a unified mathematical law of evolution regularities using entropy-based stochastic models and introduces the role of observers in information acquisition and evolution dynamics.

Findings

Evolution creates order from stochastic processes.

Hierarchical growth of information and adaptation potential.

Mechanisms of self-organization and genetic coding emerge from the model.

Abstract

An interactive stochastics, evaluated by an entropy functional (EF) of a random field and informational process' path functional (IPF), allows us modeling the evolutionary information processes and revealing regularities of evolution dynamics. Conventional Shannon's information measure evaluates a sequence of the process' static events for each information state and do not reveal hidden dynamic connections between these events. The paper formulates the mathematical forms of the information regularities, based on a minimax variation principle (VP) for IPF, applied to the evolution's both random microprocesses and dynamic macroprocesses. The paper shows that the VP single form of the mathematical law leads to the following evolutionary regularities: -creation of the order from stochastics through the evolutionary macrodynamics, described by a gradient of dynamic potential, evolutionary speed and the evolutionary conditions of a fitness and diversity; -the evolutionary hierarchy with growing information values and potential adaptation; -the adaptive self-controls and a self-organization with a mechanism of copying to a genetic code. This law and the regularities determine unified functional informational mechanisms of evolution dynamics. By introducing both objective and subjective information observers, we consider the observers' information acquisition, interactive cognitive evolution dynamics, and neurodynamics, based on the EF-IPF approach. An evolution improvement consists of the subjective observer s ability to attract and encode information whose value progressively increases. The specific properties of a common information structure of evolution processes are identifiable for each particular object-organism by collecting a behavioral data from these organisms.