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.
The article presents three approaches (deterministic, stochastic and chaotic – self-organizing) for studying biomedical systems. The authors show that complex biosystems cann’t be described by deterministic and stochastics because of constant changing parameters xi of a state vector of such systems x=x(t). The fundamental distinguish of deterministic and stochastic systems from chaotic – self-organizing is continuous movement x(t) in phase space of states. The authors also present complex of objects which the authors have been studying for the last 30 years and which conform the type III systems. The particular features of the personalized medicine are presented, that denies possibility of identification of body state at one measurement (a point in a phase space). It is connected with the fact that there is a uniform distribution x(t) in time-domain xi which is revealed in continuous change of distribution functions f(x) for different discrete recording time-domain x(t) at all xi. The authors assert that behavior dynamics of neural networks is similar to work of neuroemulators that is terminated by certainty in quasi-attractor’s volumes.
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.
The present paper shows that the term “complexity” includes absolutely different notions than now it seems to be presented in modern science and philosophy. 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). With specific examples presented uncertainty of type 1 and gives an idea of the uncertainty of type 2, when the distribution function f (x) for different samples are the same. At the same neuro-computers not only divides the sample, but also shows the order parameters. In this case, at the same time solve the problem of system synthesis, which in society is now very difficult to solve (the basic model of social relations now – it´s deterministic society).
The theory of chaos and self-organization presents three main reasons (social compartmentation – clusterization, constant «glimmering» of system state vector, teleology) that should be taken into consideration by complexity philosophy and instability. Modern dynamics of social and mental development of Russia is presented. There are discussed possible ways round the stationary quasi-attractor to teleological purpose – transition to knowledge-based synergetic postindustrial society. On this way three order parameters are detected for that 6th way of life should provide their steady progress, they include population, gross domestic product (its structure), quantity and quality of brainpower. These parameters arouse anxiety in the Russian Federation.
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