Of Existence of the Universe

1

Yu.I. SHEMAKIN

SYSTEMANTICONTOLOGY

OF EXISTENCE OF THE UNIVERSE

MOSCOW

2009

Abstract

Systemantics is a new science concerned with the cognition of being on a structural basis of the natural and artificial systems. The name is made up of merging the words system and semantics. The system is a set of elements combined by a self-organization, unity of purpose and functional integrity. The semantics defines a relation between the form and substance.

The concept of “interaction” in semantics is taken as an absolute invariant and axiom of the global evolutionism. Based on this concept, the system axiomatics is constructed, which includes a principle of semantic essence of the relationship between the form and substance, the unity of the realistic and ideal. The system-generating function of knowledge and information, matter and energy is defined.

The semantic-based difference between natural and artificial systems is identified. The natural systems combine knowledge-oriented, information and real-valued and energy processes in the organic unity of the form and substance. In the artificial world of models the form is separated from the substance and is interpreted by the creators.

State of Systems Theory of Cognition

At present the notorious “system approach” for studies actually focused to severality is declared in this country and abroad in all sciences, that takes no effect.This is explained by a traditional definition of the notion of “system”. The notion of system is closely associated with the universe and picture of the world. A system organization is characteristic of the matter. The attempts to create an integral system picture of the world have been made since the ancient times. Theterm “system” wasnotappliedinreligion.To cognize the essence of the world and its components beyond the systems is utopian. The notion of “system” was firstly suggested in science by Aristotle, i.e. “the whole is irreducible to a sum of its integral parts. On the “whole” the definition is correct, but it does not determine “particularities” due to the absence of the unifying origin in it and its varieties coming up to our days.Thus, the Dictionary of the Russian Language of S.I.Ozhegov lists about ten definitions of the word “system” reflecting various particularities.

The articles placed in Internet with a key word “system” (S) basically define the following: fundamental irreducibility of the system properties to a sum of properties of its constituents and inability to derive them from the properties of the whole; dependence of each element, property and relation (S) on the place, functions, etc. within the whole.

Among the modern works it is necessary to quote the book “Systemology: Automation of solution of system tasks”[1]of G. Klir, professor, Dean of Faculty of Systemology of the University of the Empire State (USA).The words “system” and “logic” being formally interpreted provide a basis to the name of the science and educational discipline proposed by him. The studies of a variety of tasks solved by computer aids prevail in the book. They are classified, solution methods for several classes of tasks are proposed in the context of the “Universal solver of system tasks (USST)”. However, we are dealing with models, which do not belong to systems. The books “System Engineering”[2] by V.V.Druzhinin and D.S.Kontorov and “Systems Theory: Experience in course construction”[3] by D.M.Zhilin refer to this trend.

The drawbacks of the system definitions set forth in these books are their anthropomorphism and a lack of invariability, which inevitably lead to an unlimited extraction of various types of systems and special-purpose “systems theories”, disclosing the content of the notions of “system” of each type and kind. This generates a need for introducing a number of their classifications using different bases. For example, at the top level of such classifications the “material” and “ideal” concepts were historically formed. Presently, adequate concepts “realistic” and “abstract” are proposed. Therefore, provision is made for an infinite circumstantiation being detrimental for practice.

All of this affected the evolution of science itself, where currently the system analysis prevails over the synthesis. The science begins wherever all diversity comes down to the uniformity. In the Middle Ages such an attempt was most precisely presented in the works of the 17th century Dutch philosopher Baruch Spinoza.

The “General Systems Theory” was originally put forward in 1968 by the Austrian theoretician in biology Ludwig von Bertalanffy, who lived in the USA and Canada since 1949. Guided by the mechanical and mathematical methodology, he sought to develop a comprehensive picture of nature. The aim of this theory was to develop a mathematical apparatus describing various types of systems and establish the isomorphism of laws in various areas of knowledge. The formal apparatus, which informative truth remains open, predominates in both works.

Presently the attempts at developing the systems theory in the area of «formal ontology” are still in progress [65,66]. However, the dominating efforts, concerned only with the form, do not change the “essence”. Here the bottom line is to solve problems of a well-established information search and computer-aided translation through classifications by premotivated text fragments [70,74].

In the context of self-organization the synergetics confronted with fundamental difficulties, which is based on the form concerned with the processes of strategic prediction of complicated systems interfering with tactical conditions of local dynamic disturbances like whirlwinds, traffic congestions, i.e. narrows, which local conditions and laws of behavior are to be known for traffic. The most progressive and informative views of the system belong to Norbert Wiener being the originator of the word “cybernetics” (the Greek translation of “kebernetes” is “pilot” or “steersman”). However, the interpretation of N.Wiener’s translation should be clarified by combining the meanings of the words “pilot” and “steersman”. A pilot is involved temporarily for piloting in heavy-going regions.

In the ship and aircraft long-distance navigation the kinematic trajectories leading to the target are calculated by a staff specialist, namely a navigator. The steersman (pilot in aviation) performs functions affecting on the steering mechanisms, which result to a dynamic process. Similar functional structures refer to any self-organizing systems of the Universe.

The study of the problem of self-organization was initiated in the cybernetics. The term “self-organizing system” was introduced in 1947 by the English neurophysiologist William Ross Ashby, one of the originators of cybernetics[67].

W.R.Ashby and French physicist Léon Brillouinformally investigated the problems of a common concept of entropy in the theory of information and thermodynamics [68].

In the Ancient Greek language the word entropia originated from merging the grammar elements: en (in, inward) and trope (turn, transformation).

The thermodynamic entropy describes the uncertainty of the thermal system conditions, as being a measure of its internal disorder. This notion was introduced into science by R. Clausius in 1865. The entropy in the information technique as a measure of uncertainty in a system-level situation was introduced by C. Shannon in 1949. In a classical information theory it became a common practice to connect information with the thermodynamic quantity – entropy. This practice was originated by N. Wiener, who discovered a close resemblance between C.Shannon’s formulae for information measure and L. Boltzmann’s formulae for physical entropy. This brought about the wrong understanding of negentropy as a negative entropy (i.e. entropy with a minus sign). In the majority of information sources the entropy or its variant taken with a minus sing is termed as a system negentropy (E. Schrödinger, 1943 and L. Brillouin, 1960)[4].

At present the entropy is recognized as one of the fundamental properties of any systems with a probabilistic behavior. To accomplish this, a notion of generalized entropy appears as a universal parameter combining the most general reality features such as the uncertainty and certainty, chaos and order. The generalization feature means that it can be defined for material and ideal models optimized in objective and factors affecting the system. In the context of system and semantics, with regard to the contingency of the notions “information” and “knowledge”, the entropy can be described as a measure for uncertainty in a source message, and negentropy can be described as a change in knowledge (thesaurus) of the recipient due to the received message. Complex systems shall distinguish a generalized negentropy field of information and a generalized negentropy field of knowledge.

A numerical number of information is equal to the entropy, which existed before the information is acquired. However, a numerical equality of information and entropy does not imply their identity. In fact, they are opposite to each other, since the information is not merely equal to the entropy, but the reduced entropy.

In the context of semantics the information shall be assessed with regard to the meaning content of the message. From this standpoint, the definition proposed in M.N.Vakhtomin’s[5] work is of interest, according to which the message contains the more semantic information, the more changes the recipient thesaurus undergoes under the action of this message. The amount of semantic information, which a recipient extracts from the message, is dependent not only on the message itself, but on the recipient knowledge, level of his thesaurus efficiency for perceiving this information.

When a person gets a message, he compares the information contained in this message with his extent of knowledge, namely with his thesaurus. In this case three situations are possible:

• Thesaurus has the data contained in the message;
• Thesaurus has no such data, but they are comparable;
• Thesaurus has no such data, and they are incomparable.

The conceptual information value and negentropy are as higher, as greater the changes are brought about by new data in human knowledge and thesaurus. Thevaluehasarelativenature. One and the same data can be found trivial for a big thesaurus, can be of great worth for a medium thesaurus and inaccessible for a small thesaurus.

Here, thesaurus is a model of the system knowledge, based on which the enthropy is quantified at a formal level in ambiguous terms of possible varieties of situations.

* * *

The imperfect state of the systems theory generally based on the classical mathematics methods generated a need for an explicit introduction of the semantics into systemology as the most essential factor, which shall be taken into account in studying, understanding and describing actual systems. Such methodology for analyzing complex systems was well defined by the author and manifested itself in development of a new scientific school, i.e. systemantics, which is the first to identify the system as a set of elements combined by self-organization, unity of purpose and functional integrity.

A new science concerned with the theoretical basis of the semantic self-organization system based on the system synthesis principles was called SYSTEMANTICS by the author. This name is composed of the notions of system and semantics. Тhe term “semantics” (in Greek semantikos means designative) was brought into a science use at the end of the XIX century. Sеmantics reflects the relationship between the form and substance and is the most important methodological principle of the relationship argumentation. The author takes notice of the factors identifying the systems of a various nature and reducing their variety to the uniformity. This called for a selection of the system forming material of many diverse sources on a single principle. This principle consisted in a semantic basis of the system unity of the form and substance.

The notion of interaction was selected as an absolute invariant and axiom of the global evolutionism. The system axiomatics with the concept of semantic essence of relationship between the form and substance, unity of the realistic and ideal is built on its basis. It is defined by a system-generating function of knowledge and information, matter and energy. To accomplish this, the role of structures and their functions is considered. The commonality and difference of the natural and artificial systems are established on a semantic basis. An emphasis is laid on the identification of the brain structure and activity with the structure and activity of the organism sensomotor channels, revealing the principle of associative thinking and the nature of being.

At the beginning the author had to analyze natural intelligence systems in nature, as they act as a motivated semantic basis for “artificial” models of “formal ontology”.

Systemantics of Being and Cognition

The interaction is an eternal existence of the Universe characteristic of its past and present. Itwilldefineitsfuture. This notion does not depend on the finiteness or eternity of the Universe, no matter whether Big Bang, the Flood and other cataclysms have occurred or not. The essence of any system and its element can be adequately understood only in their interaction with other surrounding systems and other elements. The perception of essentials means a perception of their interaction. It is traced at all levels of the Universe. It integrates the areas of inorganic, organic and social evolution and allows to identify them as systems. This notion provides the basis for all system axiomatics covering the primary methodological principle of sеmantic argumentation of essence of interrelation between the form and substance, unity of the realistic and ideal[6].

A primitive system based on its definition can be composed of two elements. One individual element or two noninteracting elements do not make up a system (Figure 1, а). The system can be composed of two interacting elements with different functions. The system-generated loop constitutes an elementary binary structure. One of its elements performs functions of the subject depicted in the figure (Figure 1, b) with an aureole of “system holiness” of the substance, and the other element performs functions of the object, depicted in the figure with a continuous dot of “a solid” form and being opposed with the unity of interaction purpose. (Figure 1, c). The subject and object in the unity (monad), as defined by English philosopher B.F.Bradley, constitute the “Absolute”. The subject is a source of activity directed to the object. The object is an item (part of the objective reality) opposing the subject in its goal-directed activity.

The subject has an effect on the object in a feedforward, the object has an effect on the subject in a feedback. The feedforward and feedback comprise a loop ensuring a conscious feature of the whole nature, i.e. the ability for self-organization and mutation. The environment manifests itself as a third member of interaction, becoming apparent through the system objective. The interaction of these elements in the system is determined by the needs of its existence and development.

Complex-structure systems, namely linear, planar and three-dimensional systems with spherical and elliptic trajectories are formed from the hierarchy-based elementary systems. But this complexity in its turn is overcome by the same binary subjective-objective hierarchy levels. A number of hierarchy levels in the Universe is infinite.

Figure 1. Interactionofelements

The systems interact with the environment (Ref. Figure 2).

Figure 2. System and environment interaction

Space and time are parameters defining the system boundaries and lifetime. The time is irreversible and represents a kinematic coordinate («time arrow»), identifying the ideal position of the system in the evolutionary process, the space is a dynamic coordinate of the real position of the system relative to the time arrow. In cyclic processes the kinematic trajectories determine centers of phase spaces of the ideal position in the form rings, ellipses. The dynamic coordinates via a real movement in a three-dimensional space forms rings and tori, respectively.

Figure 3. Semantic model of nature

The functional integrity of the system of any nature and complexity is provided by four attributive elements (Figure 3). In so doing, the matter and system appear as synonymous concepts. The substance and energy represent a system form, the knowledge and information represent a content.

1. “Substance” as possessing a rest mass is a static component being a knowledge medium;

2. “Energy” as a physical field is a dynamic component being an information medium;

3. “Knowledge” as a strategic information necessary for defining the ideal system state by selecting the objective and generating a kinematic trajectory for moving thereto;

4. “Information” as on-line knowledge required by the system in the dynamic process transforming it from a real actual state to a desired ideal state.

The substance and energy, knowledge and information by pairs constitute mutually conjugated notions. The substance and energy make up a system form, and the knowledge and information express its conceptual essence. In system interaction of these elements the substance manifests itself as a knowledge medium, and the energy as an information medium. In accordance with the law of conservation of matter, they keep the system at the equilibrium transferring from one substance to another, if needed.

The hierarchy of complex systems is based on the unity of static, kinematic and dynamic processes at all stages of their existence and development.

The in-system elements and the system and environment interactions are caused by a movement of substance, energy, knowledge and information. The system and environment exchange calls for a definite contact between them and is based on the principle of оpenness, the in-system elements are exchanged on the principle of closeness. In the system context they are not opposite, but complement each other. Only open or only closed self-organizing systems do not exist. The openness ensures a required system and environment exchange. The closeness based on a closed-loop feedback ensures a definite logic of the system response to modification of its operating conditions.