Non-stationary stationarity in systems of third type and philosophy of instability

2015 ◽  
Vol 4 (2) ◽  
pp. 65-74
Author(s):  
Гавриленко ◽  
T. Gavrilenko ◽  
Еськов ◽  
Valeriy Eskov ◽  
Еськов ◽  
...  
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Biological Systems ◽  
Deterministic Chaos ◽  
Stochastic Approach ◽  
Distribution Functions ◽  
Self Organization ◽  
Deterministic Approach ◽  
The Third ◽  
Specific Assessment

There are several criteria in science for stationarity (stability) of different dynamical systems. The stationarity in physics, engineering and chemistry is being interpreted as matching the requirements of dx/dt=0, where x=x(t) - is the vector of system’s state, or the equality of distribution functions f(x) for different samples which characterize the system. However, in case of social or biological systems the matching of the requirements is impossible and there is a problem of specific assessment of stationary regimes of complex systems of the third type. The possibility of studying of such systems within the frame of deterministic chaos, stochastic approach and theory of chaos and self-organization is being discussed. This article explains why I.R. Prigogine refused from materialistic (in fact deterministic) approach in the description of such special systems of third type and tried to get away from the traditional science in the description of biological systems.

An analysis of I.R. Prigogone and J.A. Wheeler’s views concerning emergency of biosystems from the perspective of the third paradigm

10.12737/7652 ◽  
2014 ◽  
Vol 3 (4) ◽  
pp. 47-62 ◽  
Author(s):  
Еськов ◽  
V. Eskov ◽  
Джумагалиева ◽  
L. Dzhumagalieva ◽  
Филатова ◽  
...  
Keyword(s):  
Deterministic Chaos ◽  
Modern Science ◽  
Main Property ◽  
Type Systems ◽  
Distribution Functions ◽  
Living Systems ◽  
Self Organization ◽  
Initial State ◽  
The Third ◽  
Emergent Systems

The main problem of modern science is pointed out: reality of specific three-type systems (TTS) that are usually presented as complexity, and simultaneously impossibility of a description of such systems by a traditional modern reducing approach. Studying properties of system elements cannot help in a description of a complex system itself – complexity (three-type systems, living systems). Hence there arises an acute need in creation of new theories which would operate with maximum uncertainty and unpredictability and provide modeling TTS. The first step in this direction was taken on the grounds of a creation of theory of chaos and self-organization (TCS), according to which complexity cannot repeat an initial state of a system (or a vector parameters x(t0)), measures are not invariant, autocorrelation functions do not converge to zero and Lyapunov exponents are not positive. Chaos of TTS differs from a deterministic chaos and statistical distribution functions f(x) are not appropriate to describe it, because they continuously change. Deterministic, stochastic and chaotic models cannot describe TTS. This is the main property of emergent systems (complexity, TTS), therefore they are described by quasi-attractors.

Scaling

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Distribution Function ◽  
Dynamical Systems ◽  
Complex Systems ◽  
Power Law ◽  
Scaling Laws ◽  
Power Laws ◽  
Distribution Functions ◽  
Temporal Process ◽  
Approximate Power

Scaling appears practically everywhere in science; it basically quantifies how the properties or shapes of an object change with the scale of the object. Scaling laws are always associated with power laws. The scaling object can be a function, a structure, a physical law, or a distribution function that describes the statistics of a system or a temporal process. We focus on scaling laws that appear in the statistical description of stochastic complex systems, where scaling appears in the distribution functions of observable quantities of dynamical systems or processes. The distribution functions exhibit power laws, approximate power laws, or fat-tailed distributions. Understanding their origin and how power law exponents can be related to the particular nature of a system, is one of the aims of the book.We comment on fitting power laws.

Types of scientific rationality in aspects of the three paradigm

2015 ◽  
Vol 4 (1) ◽  
pp. 22-30
Author(s):  
Карпин ◽  
V. Karpin ◽  
Живогляд ◽  
R. Zhivoglyad ◽  
Гудкова ◽  
...  
Keyword(s):  
Complex Systems ◽  
Basic Principle ◽  
Self Organization ◽  
Scientific Rationality ◽  
Basic Principles ◽  
The Third ◽  
Self Development ◽  
The Future

Since the release of the well-known work of W. Weaver «Science and Complexity» (1948) only V.S. Stepin had taken some significant efforts to develop the doctrine of the three types of systems in nature. In this case, the main achievements of V.S. Stepin in postnonclassic reduced to two fundamental results: violation of the basic principle of T. Kuhn´s contradictions when changing paradigms (V.S. Stepin shows the effect of «investments», when complex systems operate classical and nonclassical rationality simultaneously) and repeated emphasis on the possibility of «change ... the probability of emerging of other (the system) conditions». At the same time, V.S. Stepin in his last works (monographs) identified a particular role of self-organization and self-development in case of complex biosocial systems. All this in theory of chaos and self-organization form 5 basic principles of functioning of complexity (or systems of the third type - STT). In fact, V.S. Stepin laid the foundation for the future (new) philosophy and developed now theory of chaos and self-organization in which humanity moved into the area of uncertainty of living (social in particular) systems completely. However, the rationality of the third type (postnonclassic) requires corrections and additions, as shown in a number of monographs of V.S. Stepin.

scholarly journals Overview of the modeling of complex biological systems and its role in neurosurgery

2021 ◽  
Vol 12 ◽  
pp. 433
Author(s):  
Zaid Aljuboori
Keyword(s):  
Nervous System ◽  
Complex Systems ◽  
Biomedical Research ◽  
Systems Approach ◽  
Biological Systems ◽  
Self Organization ◽  
Human Biology ◽  
Linear Approach ◽  
Human Nervous System

Biological systems are complex with distinct characteristics such as nonlinearity, adaptability, and self-organization. Biomedical research has helped in advancing our understanding of certain components the human biology but failed to illustrate the behavior of the biological systems within. This failure can be attributed to the use of the linear approach, which reduces the system to its components then study each component in isolation. This approach assumes that the behavior of complex systems is the result of the sum of the function of its components. The complex systems approach requires the identification of the components of the system and their interactions with each other and with the environment. Within neurosurgery, this approach has the potential to advance our understanding of the human nervous system and its subsystems.

Homeostatic System Can Not Be Described by Stochastics or Deterministic Chaos

2015 ◽  
Vol 22 (4) ◽  
pp. 28-33 ◽  
Author(s):  
Полухин ◽  
V. Polukhin ◽  
Еськов ◽  
V. Eskov ◽  
Эльман ◽  
...  
Keyword(s):  
Distribution Function ◽  
Stationary State ◽  
Natural Science ◽  
Invariant Measures ◽  
Deterministic Chaos ◽  
Living Systems ◽  
Deterministic Approach ◽  
The Third ◽  
Rate Of Increase ◽  
Mixing Property

The living systems (complexity, homeostatic systems) are a special systems of the third type of complexity in natural science and for such systems it is impossible to determine the stationary state in form of dx/dt=0 (deterministic approach) or in the form of invariance of distribution function f(x) for samples acquired in a row of, the any component xi of all vectors of state x=x(t) =(x1,x2,…,xm)T in m‐dimensional phase space of states. At the same time the mixing property doesn’t met (no invariant measures), the autocorrelation functions A(t) don’t tend to zero if t→∞, Lyapunov’s constants can continuously change the sign. Such systems of the third type (complexity) do not meet the condition of Glansdorff – Prigogine’s theorem, i.e. P ‐ the rate of increase of entropy E (P=dE/dt) doesn’t minimized near the point of maximum entropy E (i.e., at point of thermodynamic equilibrium). It is proposed to use the concept of quasi‐attractors to describe the complexity.

Classification of Self-Organization and Emergence in Chemical and Biological Systems

2006 ◽  
Vol 59 (12) ◽  
pp. 849 ◽  
Author(s):  
Julianne D. Halley ◽  
David A. Winkler
Keyword(s):  
Complex Systems ◽  
Biological Systems ◽  
Self Organization ◽  
Systems Science ◽  
New Paradigm ◽  
Chemical And Biological Systems ◽  
Complex Systems Science

Most chemical and biological systems are complex, but the application of complex systems science to these fields is relatively new compared to the traditional reductionist approaches. Complexity can provide a new paradigm for understanding the behaviour of interesting chemical and biological systems, and new tools for studying, modelling, and simulating them. It is also likely that some very important, but very complicated systems may not be accessible by reductionist approaches. This paper provides a brief review of two important concepts in complexity, self-organization and emergence, and describes why they are relevant to chemical and biological systems

scholarly journals Organisms, Machines, and Thunderstorms: A History of Self-Organization, Part Two. Complexity, Emergence, and Stable Attractors

2009 ◽  
Vol 39 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Evelyn Fox Keller
Keyword(s):  
Complex Systems ◽  
Paradigm Shift ◽  
Self Organization ◽  
Scientific Thinking ◽  
Physical Sciences ◽  
Physical Systems ◽  
The Third ◽  
Naturally Occurring ◽  
Biological Phenomena ◽  
History Of

Part Two of this essay focuses on what might be called the third and most recent chapter in the history of self-organization, in which the term has been claimed to denote a paradigm shift or revolution in scientific thinking about complex systems. The developments responsible for this claim began in the late 1960s and came directly out of the physical sciences. They rapidly attracted wide interest and led to yet another redrawing of the boundaries between organisms, machines, and naturally occurring physical systems (such as thunderstorms). In this version of self-organization, organisms are once again set apart from machines precisely because the latter depend on an outside designer, but——in contrast to Kant's ontology——they are now assimilated to patterns in the inorganic world on the grounds that they, too, like many biological phenomena, arise spontaneously.

Stochastic and chaos in evaluation order parameter in regenerative medicine

10.12737/7655 ◽  
2014 ◽  
Vol 3 (4) ◽  
pp. 87-100
Author(s):  
Синенко ◽  
D. Sinenko ◽  
Гараева ◽  
G. Garaeva ◽  
Еськов ◽  
...  
Keyword(s):  
Chaotic Dynamics ◽  
Biological Systems ◽  
Parallel Applications ◽  
Self Organization ◽  
Stochastic Methods ◽  
The Third ◽  
Initial Stage ◽  
Dimensional Phase Space ◽  
The Difference ◽  
Comparative Description

The basis of the third global paradigm of theory of chaos and self-organization, which focuses on the assessment of the chaotic dynamics of the state vector of complex biological systems using multi-dimensional phase space of states. The paper presents a comparative description of the effectiveness of the traditional stochastic methods and methods of calculating the parameters of quasi-attractors. It is showed the difference in efficiency (low) of stochastics, which leads to the uncertainty of the 1st kind, and methods of multidimensional phase spaces, providing the solution of system synthesis. Volumes quasi-attractors with kinesotherapy in patients with acute stroke increased 5.3 times in the initial stage of treatment, and then falling off sharply. It is discussed the need for parallel applications and stochastic methods and methods of theory of chaos and self-organization in the study of complex medical and biological systems.

The complexity in I. Prigogine and H. Haken’s interpretation differs from the complexity of W. Weaver and TCS

10.12737/6723 ◽  
2014 ◽  
Vol 3 (3) ◽  
pp. 46-56
Author(s):  
Гудкова ◽  
S. Gudkova ◽  
Джумагалиева ◽  
L. Dzhumagalieva ◽  
Еськов ◽  
...  
Keyword(s):  
Modern Science ◽  
Order Parameters ◽  
Distribution Functions ◽  
Synthesis Problem ◽  
Self Organization ◽  
Stochastic Methods ◽  
Type I ◽  
Type Ii ◽  
The Third ◽  
Significant Difference

The present paper shows that the term “complexity” includes absolutely different notions than now it seems to be presented in modern science and philosophy. V.S. Stepin’s postnon-classics has come to this new recognition too close, but, actually, it is a new recognition of uncertainty for systems of the third type (not deterministic and not stochastic). We introduce the interpretation of a type I uncertainty that implies that stochastic methods show systems identified, but methods of the theory of chaos and self-organization and neurocomputing show significant difference of target systems (processes). The concrete examples show the type I uncertainty and give an idea of a type II uncertainty, that implies the coincidence of distribution functions f(x) for different samplings. We prove that neurocomputing method not only differentiates samplings, but also identifies order parameters. In this case we also solve the system synthesis problem.

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