A systematic understanding of the relationship of various theories. General systems theory and other systems sciences

The concepts of "system" and "systemic" play an important role in modern science and practice. Intensive developments in the field of the systems approach and systems theory have been carried out since the middle of the 20th century. However, the very concept of "system" has a much older history. Initially, systemic representations were formed within the framework of philosophy: back in antiquity, the thesis was formulated that the whole is greater than the sum of its parts. Ancient philosophers (Plato, Aristotle, etc.) interpreted the system as a world order, arguing that systemicity is a property of nature. Later, I. Kant (1724-1804) substantiated the system nature of the process of cognition itself. The principles of consistency were also actively studied in the natural sciences. Our compatriot E. Fedorov (1853-1919), in the process of creating the science of crystallography, came to the conclusion that nature is systematic.

The principle of consistency in economics was formulated by A. Smith (1723-1790), who concluded that the effect of the actions of people organized in a group is greater than the sum of single results.

Various areas of systematic research led to the conclusion that this is a property of nature and a property of human activity (Fig. 2.1).

Systems theory serves as a methodological basis for control theory. This is a relatively young science, the organizational formation of which took place in the second half of the 20th century. The Austrian scientist L. Bertalanffy (1901-1972) is considered to be the founder of systems theory. The first international symposium on systems was held in London in 1961. The first report at this symposium was made by the outstanding English cyberneticist S. Beer, which can be considered evidence of the epistemological closeness of cybernetics and systems theory.

Central to systems theory is the notion "system"(from the Greek systēma - a whole made up of parts, a connection). A system is an object of an arbitrary nature that has a pronounced systemic property that none of the parts of the system has in any way of its division that is not derived from the properties of the parts.

Rice. 2.1.

The above definition cannot be considered exhaustive - it reflects only a certain general approach to the study of objects. Many definitions of a system can be found in the systems analysis literature (see Appendix 1).

In this tutorial, we will use the following working definition of a system:

" System is a holistic set of interrelated elements. It has a certain structure and interacts with the environment in order to achieve the goal."

This definition allows us to identify the following basic concepts:

integrity;
totality;
structuredness;
interaction with the external environment;
having a goal.

They represent a system of concepts, i.e., the internal organization of some stable object, the integrity of which is the system. The very possibility of identifying stable objects in the field of study is determined by the property of the integrity of the system, the goals of the observer and the possibilities of his perception of reality.

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Introduction

Systems approach

Aspects of the systems approach

The formation of the system

The system as a whole

System conversion

Types of model similarity

Adequacy of models

Conclusion

Bibliography

Introduction

In our time, an unprecedented progress in knowledge is taking place, which, on the one hand, has led to the discovery and accumulation of many new facts, information from various areas of life, and thus confronted humanity with the need to systematize them, to find the common in the particular, the constant in the changing. On the other hand, the growth of knowledge creates difficulties in its development, reveals the inefficiency of a number of methods used in science and practice. In addition, penetration into the depths of the Universe and the subatomic world, which is qualitatively different from the world commensurate with the already established concepts and ideas, caused doubt in the minds of individual scientists about the universal fundamentality of the laws of existence and development of matter. Finally, the process of cognition itself, which is increasingly acquiring the form of transforming activity, sharpens the question of the role of man as a subject in the development of nature, the essence of the interaction between man and nature, and in this regard, the development of a new understanding of the laws of development of nature and their action.

The fact is that the transforming human activity changes the conditions for the development of natural systems, and thereby contributes to the emergence of new laws, trends of movement.

In a number of studies in the field of methodology, a special place is occupied by a systematic approach and, in general, "systemic movement". The systemic movement itself was differentiated, divided into various directions: general systems theory, systems approach, system analysis, philosophical understanding of the systemic nature of the world.

There are a number of aspects within the methodology of systematic research: ontological (is the world in which we live in its essence systemic?); ontological-epistemological (is our knowledge systemic and is its systemic nature adequate to the systemic nature of the world?); epistemological (is the process of cognition systemic and are there limits to systemic cognition of the world?); practical (is the transformative activity of a person systemic?) The easiest way to get an idea of \u200b\u200bsystem analysis is by listing its most basic concepts and statements.

Systems approach

A systematic approach is a direction of research methodology, which is based on the consideration of an object as an integral set of elements in the totality of relationships and connections between them, that is, consideration of an object as a system.

Speaking of a systematic approach, we can talk about some way of organizing our actions, one that covers any kind of activity, identifying patterns and relationships in order to use them more effectively. At the same time, a systematic approach is not so much a method of solving problems as a method of setting problems. As the saying goes, "The right question is half the answer." This is a qualitatively higher, rather than just objective, way of knowing.

Basic principles of the systems approach

Integrity, which allows considering the system at the same time as a whole and at the same time as a subsystem for higher levels.

Hierarchy of the structure, that is, the presence of a plurality (at least two) of elements located on the basis of the subordination of elements of a lower level to elements of a higher level. The implementation of this principle is clearly visible in the example of any particular organization. As you know, any organization is an interaction of two subsystems: managing and managed. One is subordinate to the other.

Structuring, which allows you to analyze the elements of the system and their relationships within a specific organizational structure. As a rule, the process of functioning of the system is determined not so much by the properties of its individual elements, but by the properties of the structure itself.

Multiplicity, which allows using a variety of cybernetic, economic and mathematical models to describe individual elements and the system as a whole.

Consistency, the property of an object to have all the features of a system

Basic definitions of the systems approach

The founders of the system approach are: L. von Bertalanffy, A. A. Bogdanov, G. Simon, P. Drucker, A. Chandler.

System -- a set of elements and relationships between them.

Structure is a way of interaction between the elements of the system through certain connections (a picture of connections and their stability).

Process -- dynamic change of the system in time.

Function - the work of an element in the system.

State - the position of the system relative to its other positions.

The system effect is the result of a special reorganization of the elements of the system, when the whole becomes more than a simple sum of parts.

Structural optimization is a targeted iterative process of obtaining a series of system effects in order to optimize the applied goal within the given constraints. Structural optimization is practically achieved using a special algorithm for the structural reorganization of system elements. A series of simulation models has been developed to demonstrate the phenomenon of structural optimization and for training.

Aspects of the systems approach

A systematic approach is an approach in which any system (object) is considered as a set of interrelated elements (components) that has an output (goal), input (resources), communication with the external environment, feedback. This is the most difficult approach. The system approach is a form of application of the theory of knowledge and dialectics to the study of processes occurring in nature, society, and thinking. Its essence lies in the implementation of the requirements of the general theory of systems, according to which each object in the process of its study should be considered as a large and complex system and, at the same time, as an element of a more general system.

A detailed definition of a systematic approach also includes the obligatory study and practical use of the following eight of its aspects:

1) system-element or system-complex, consisting in identifying the elements that make up this system. In all social systems, one can find material components (means of production and consumer goods), processes (economic, social, political, spiritual, etc.) and ideas, scientifically conscious interests of people and their communities;

2) system-structural, which consists in clarifying the internal connections and dependencies between the elements of a given system and allowing you to get an idea of the internal organization (structure) of the system under study;

3) system-functional, involving the identification of functions for the performance of which corresponding systems have been created and exist;

4) system-target, meaning the need for a scientific definition of the goals and sub-goals of the system, their mutual linkage with each other;

5) system-resource, which consists in a thorough identification of the resources required for the functioning of the system, for the solution of a particular problem by the system;

6) system-integration, consisting in determining the totality of the qualitative properties of the system, ensuring its integrity and peculiarity;

7) system-communication, meaning the need to identify the external relations of a given system with others, that is, its relations with the environment;

8) system-historical, which allows to find out the conditions at the time of the emergence of the system under study, the stages it has passed, the current state, as well as possible development prospects.

Almost all modern sciences are built according to the systemic principle. An important aspect of the systematic approach is the development of a new principle of its use - the creation of a new, unified and more optimal approach (general methodology) to knowledge, to apply it to any cognizable material, with the guaranteed goal of obtaining the most complete and holistic view of this material.

The emergence and development of system representations

The scientific and technological revolution has led to the emergence of such concepts as large and complex economic systems with problems specific to them. The need to solve such problems led to the emergence of special approaches and methods that were gradually accumulated and generalized, eventually forming a special science - system analysis.

In the early 1980s, consistency became not only a theoretical category, but also a conscious aspect of practical activity. There is a widespread notion that our successes are related to how systematically we approach solving problems that arise, and our failures are caused by a lack of systematicity in our actions. A signal of insufficient consistency in our approach to solving a problem is the appearance of a problem, while the resolution of the problem that has arisen occurs, as a rule, when moving to a new, higher, level of systematicity of our activity. Therefore, consistency is not only a state, but also a process.

In various fields of human activity, various approaches and corresponding methods for solving specific problems have arisen, which have received various names: in military and economic issues - "operations research", in political and administrative management - "systems approach", in the philosophy of "dialectical materialism", in applied scientific research - "cybernetics". Later it became clear that all these theoretical and applied disciplines form, as it were, a single stream, a “system movement”, which gradually took shape in a science called “system analysis”. At present, system analysis is an independent discipline that has its own object of activity, its rather powerful arsenal of tools, and its own application area. Being essentially applied dialectics, system analysis uses all the means of modern scientific research - mathematics, modeling, computer technology and natural experiments.

The most interesting and difficult part of system analysis is “pulling out” a problem from a real practical problem, separating the important from the unimportant, finding the right formulation for each of the problems that arise, i.e. what is called "problem setting".

Many often underestimate the work involved in formulating a problem. However, many experts believe that “setting a problem well means solving it halfway”. Although in most cases it seems to the customer that he has already formulated his problem, the system analyst knows that the problem statement proposed by the client is a model of his real problem situation and inevitably has a target character, remaining approximate and simplified. Therefore, it is necessary to check this model for adequacy, which leads to the development and refinement of the original model. Very often, the initial formulation is stated in terms of languages that are not necessary for building the model.

The formation of the system

Becoming is a stage in the development of a system, during which it turns into a developed system. Becoming is the unity of "being" and "nothing", but this is not a simple unity, but unrestrained movement.

The process of formation, as well as the emergence of a system, is associated with a quantitative increase in a qualitatively identical set of elements. Thus, under the thermodynamic conditions of the earth's surface, the amount of oxygen and silicon prevails over all other elements, while other elements predominate on the surface of other planets. This indicates the potential for the quantitative growth of any element under favorable physicochemical conditions.

In the process of the formation of the system, new qualities appear in it: natural and functional. A natural quality is a defining feature of a particular class, level of systems, which allows us to speak about the identity of systems of this class. Functional quality includes the specific properties of the system acquired by it as a result of its way of communication with the environment. If the natural quality gradually disappears along with a given system, then the functional quality can change according to external conditions.

Therefore, new qualities also appear in individual elements of the system, or rather, the element acquires this quality when the system is formed (for example, the cost of goods).

The contradiction between qualitatively identical elements is one of the sources of system development. One of the consequences of this contradiction is the tendency for the spatial expansion of the system. Having arisen, qualitatively identical elements tend to disperse in space. This "striving" is due to the continuous quantitative growth of these elements and the contradictions that arise between them.

On the other hand, there are system-forming factors that do not allow the emerging system to disintegrate due to internal contradictions and expansion existing in the system. And there is a boundary of the system, going beyond which can be detrimental to the elements of the newly emerged system. In addition, the newly emerged elements of the new system are affected by systems that already exist, in this environment earlier. They prevent the penetration of new systems into the environment of their existence.

Thus, on the one hand, the elements of the new system are in conflict with each other, and on the other hand, under the pressure of the external environment and conditions of existence, they find themselves in interaction, in unity. At the same time, the development trend is such that internal contradictions between qualitatively identical elements of the system lead them to a close relationship, and, in the end, lead to the formation of the system as a whole. systems approach presentation

How, for example, the process of formation of atoms is described: “Once there was a “population” of elementary particles. Between them, combinatorics processes were carried out, and combinations were subjected to “selection.” Combinatorics obeyed the degrees of freedom and prohibitions operating in the world of elementary particles. Only those combinations “survived” , which were allowed by the environment. These were the processes of physical evolution of matter, its result is the system of atoms of the periodic table, and its duration is several tens of billions of years ".

Becoming is a contradictory unity of processes of differentiation and integration. Moreover, the deepening differentiation of elements, respectively, enhances their integration.

in the process of emergence and formation, a quantitative growth of new elements is observed. The main contradiction driving development is the contradiction between the new elements and the old system, which is resolved by the victory of the new, i.e. the emergence of a new system, a new quality.

The system as a whole

The integrity or maturity of the system is determined, along with other features, by the presence in a single system of dominant opposite subsystems, each of which combines elements with functional qualities that are opposite to the functional qualities of another subsystem.

The system in the period of maturity is internally contradictory not only due to the deep differentiation of elements, leading the dominant of them to mutual opposition, but also due to the duality of its state as a system that completes one form of movement, and is an elementary carrier of the highest form of movement.

Completing one form of movement, the system is an integrity and "strives" to fully reveal the possibilities of this highest form of movement. On the other hand, as an element of a higher system, as an elementary system - the bearer of a new form of movement, it is limited in its existence by the laws of the external system. Naturally, this contradiction between possibility and reality in the development of the external system as a whole has an impact on the development of its elements. And the most promising in development are those elements whose functions correspond to the needs of the external system. In other words, the system, by specializing, has a positive effect on the development of mainly those elements whose functions correspond to specialization. And since elements whose functions correspond to the conditions of the external system (or environment) are predominant in the system, the system as a whole becomes specialized. It can exist, function only in the environment in which it was formed. Any transition of a mature system to another environment inevitably causes its transformation. So, "a simple transition of a mineral from one area to another causes a change and rearrangement in it that meets new conditions. This is explained by the fact that a mineral can exist unchanged only as long as it is in the conditions of its formation. As soon as it leaves them , new stages of existence begin for him.

Even under favorable external conditions, internal contradictions in the system lead it out of the state of equilibrium achieved at a certain stage, thus, the system inevitably enters a period of transformation.

System conversion

Just as in the formation of a system during its transformation, change, there are internal and external causes that manifest themselves with greater or lesser force in various systems.

External reasons:

1. Change in the external environment, causing a functional change in the elements. In the existing environment, a long-term existence of an unchanged system is impossible: any change, no matter how slowly and imperceptibly it proceeds, inevitably leads to a qualitative change in the system. Moreover, a change in the external environment can occur both independently of the system and under the influence of the system itself. An example is the activity of human society, which contributes to changing the environment not only for the benefit, but also to the detriment (pollution of water bodies, the atmosphere, etc.)

2. Penetration of alien objects into the system, leading to functional changes in individual elements (transformations of atoms under the influence of cosmic rays).

Internal reasons:

1. Continuous quantitative growth of differentiated elements of the system in a limited space, as a result of which the contradictions between them become aggravated.

2. Accumulation of "mistakes" in the reproduction of their own kind (mutations in living organisms). If the element - "mutant" is more consistent with the changing environment, then it begins to multiply. This is the emergence of the new, which comes into conflict with the old.

3. Termination of growth and reproduction of the elements that make up the system, as a result, the system dies.

Based on the understanding of a mature system as the unity and constancy of the structure, it is possible to determine various forms of transformation that are directly related to the change in each of the listed attributes of the system:

Transformation leading to the destruction of all interconnections of the elements of the system (crystal destruction, atom decay, etc.).

Transformation of the system into a qualitatively different, but equal in degree of organization state. This is due to:

a) changes in the composition of the elements of the system (substitution of one atom in the crystal for another),

b) functional change of individual elements and/or subsystems in the system (transition of mammals from a terrestrial to aquatic way of life).

Transformation of the system into a qualitatively different, but lower degree of organization state. It occurs due to:

a) functional changes in elements and / or subsystems in the system (adaptation of animals to new environmental conditions)

b) structural change (modification transformations in inorganic systems: for example, the transition of diamond to graphite).

Transformation of the system into a qualitatively different, but higher state in terms of degree of organization. It occurs both within the framework of one form of movement, and during the transition from one form to another. This type of transformation is associated with the progressive, progressive development of the system.

Transformation is an inevitable stage in the development of a system. It enters into it by virtue of the growing contradictions between the new and the old, between the changing functions of the elements and the nature of the connection between them, between the opposite elements. The transformation can reflect both the final final stage in the development of the system, and the transition of system-stages into each other. Transformation is a period of disorganization of the system, when old connections between elements are broken, and new ones are just being created. Transformation can also mean the reorganization of the system, as well as the transformation of the system as a whole into an element of another, higher system.

Today, special sciences convincingly prove the system nature of the parts of the world they know. The universe appears to us as a system of systems. Of course, the concept of "system" emphasizes limitation, finiteness, and, thinking metaphysically, one can come to the conclusion that since the Universe is a "system", it has a boundary, i.e. finite. But from a dialectical point of view, no matter how one imagines the largest of the systems, it will always be an element of another, larger system. This is also true in the opposite direction, i.e. The Universe is infinite not only "in breadth", but also "in depth".

Until now, all the facts at the disposal of science testify to the systemic organization of matter.

Models and modeling. Model classification

Initially, a model was called a kind of auxiliary tool, an object that, in certain situations, replaced another object. For example, a mannequin in a certain sense replaces a person, being a model of a human figure. Ancient philosophers believed that nature could be displayed only with the help of logic and correct reasoning, i.e. according to modern terminology with the help of language models. A few centuries later, the motto of the English Scientific Society became the slogan: “Nothing with words!”, Only conclusions supported by experimental or mathematical calculations were recognized.

Currently, there are 3 ways to comprehend the truth:

theoretical research;

experiment;

modeling.

A model is a substitute object, which under certain conditions can replace the original object, reproducing the properties and characteristics of the original that are of interest to us, and has significant advantages:

Cheapness;

visibility;

Ease of operation, etc.

In model theory, modeling is the result of mapping one abstract mathematical structure onto another - also abstract, or as a result of interpreting the first model in terms and images of the second.

The development of the concept of a model went beyond mathematical models and began to refer to any knowledge and ideas about the world. Since models play an extremely important role in the organization of any human activity, they can be divided into cognitive (cognitive) and pragmatic, which corresponds to the division of goals into theoretical and practical.

The cognitive model is focused on the approximation of the model to the reality that this model displays. Cognitive models are a form of organization and presentation of knowledge, a means of connecting new knowledge with existing ones. Therefore, when a discrepancy between the model and reality is detected, the task of eliminating this discrepancy by changing the model arises.

Pragmatic models are a means of management, a means of organizing practical actions, a way of presenting exemplary correct actions or their results, i.e. are a working representation of the goals. Therefore, if a discrepancy between the model and reality is found, efforts must be directed to changing reality in such a way as to bring reality closer to the model. Thus, pragmatic models are of a normative nature, they play the role of a model, under which reality is adjusted. Examples of pragmatic models are plans, codes of laws, shop drawings, and so on.

Another principle for classifying the goals of modeling can be the division of models into static and dynamic.

For some purposes, we may need a model of a specific state of an object at a certain point in time, a kind of “snapshot” of an object. Such models are called static. An example is the structural models of systems.

In those cases when there is a need to display the process of changing states, dynamic models of systems are required.

At the disposal of man there are two types of materials for building models - the means of consciousness itself and the means of the surrounding material world. Accordingly, models are divided into abstract (ideal) and material.

Obviously, abstract models include language constructs and mathematical models. Mathematical models have the highest accuracy, but in order to reach their use in this area, it is necessary to obtain a sufficient amount of knowledge. According to Kant, any branch of knowledge can be called a science the more it uses mathematics to a greater extent.

Types of model similarity

So that some material structure can be a model, i.e. replaced the original in some respect, a relationship of similarity must be established between the original and the model. There are different ways to establish this similarity, which gives the models features that are specific to each method.

First of all, this is the similarity established in the process of creating a model. Let's call this similarity direct. An example of such similarity is photographs, scale models of aircraft, ships, building models, patterns, dolls, etc.

It should be remembered that no matter how good the model is, it is still only a substitute for the original, only in a certain respect. Even when the model of direct similarity is made of the same material as the original, i.e. similar to it substratively, there are problems of transferring the simulation results to the original. For example, when testing a reduced model of an aircraft in a wind tunnel, the problem of recalculating the data of a model experiment becomes nontrivial and a branched, meaningful similarity theory arises, which makes it possible to bring the scale and conditions of the experiment, flow velocity, viscosity and air density into line. It is difficult to achieve the interchangeability of the model and the original in photocopies of works of art, holographic images of works of art.

The second type of similarity between the model and the original is called indirect. Indirect similarity between the original and the model objectively exists in nature and is found in the form of sufficient closeness or coincidence of their abstract mathematical models and, as a result, is widely used in the practice of real modeling. The most characteristic example is the electromechanical analogy between a pendulum and an electric circuit.

It turned out that many patterns of electrical and mechanical processes are described by the same equations, the difference lies in the different physical interpretation of the variables included in this equation. The role of models with indirect similarity is very great and the role of analogies (models of indirect similarity) in science and practice can hardly be overestimated. Analog computers make it possible to find a solution to almost any differential equation, thus representing a model, an analogue of the process described by this equation. The use of electronic analogues in practice is determined by the fact that electrical signals are easy to measure and fix, which gives the well-known advantages of the model.

The third, special class of models consists of models whose similarity to the original is neither direct nor indirect, but is established as a result of an agreement. Such similarity is called conditional. Models of conditional similarity have to be dealt with very often, since they are a way of material embodiment of abstract models. Examples of conditional similarity are money (value model), identity card (owner model), all kinds of signals (message models).

For example, fires on mounds served as a signal for the advance of nomads among the ancient Slavs. Paper money can play the role of a model of value only as long as there are legal norms in the environment of their circulation that support their functioning. Kerenki currently have only historical value, but they are not money, unlike royal gold coins, which are of material value due to the presence of precious metal. The conditionality of iconic models is especially clear: a flower in the window of Stirlitz's safe house meant the failure of the turnout, neither the variety nor the color had anything to do with the iconic function of the flower.

Adequacy of models

The model with the help of which the set goal is successfully achieved will be called adequate to this chain. Adequacy means that the requirements for completeness, accuracy and correctness (truth) of the model are not met in general, but only to the extent that is sufficient to achieve the goal.

In some cases, it is possible to introduce a measure of the adequacy of some goals, i.e. indicate a way to compare two models in terms of the degree of success in achieving the goal with their help. If, in addition, there is a way to quantify the measure of adequacy, then the task of improving the model is greatly facilitated. It is in such cases that it is possible to quantitatively pose questions about the identification of the model, i.e. about finding the most adequate model in a given class, about studying the sensitivity and stability of models, i.e. dependence of the measure of the adequacy of the model on its accuracy, on the adaptation of models, i.e. adjusting the model parameters in order to improve its accuracy.

Approximation of the model should not be confused with adequacy. The approximation of the model can be very high, but in all cases the model is a different object and differences are inevitable (the only perfect model of any object is the object itself). The magnitude, measure, degree of acceptability of the difference can only be entered by correlating it with the purpose of modeling. So, even experts cannot distinguish some fake works of art from the original, but still it is just a fake, and from the point of view of capital investment it is of no value, although for art lovers it is no different from the original. During the war, the British Field Marshal Montgomery had a double, whose appearance on different sectors of the front deliberately misinformed the German intelligence.

Simplification is a powerful tool for revealing the main effects in the phenomenon under study: this can be seen in the example of such physical phenomena as an ideal gas, an absolutely elastic body, a mathematical pendulum and an absolutely rigid lever.

There is another, rather mysterious, aspect of the model's simplification. For some reason, it turns out that of the two models that describe the system equally well, the one that is simpler is closer to the truth. The geocentric model of Ptolemy made it possible to calculate the motion of the planets, albeit using very cumbersome formulas, with interweaving of complex cycles. The transition to the heliocentric model of Copernicus greatly simplified the calculations. The ancients said that simplicity is the seal of truth. These are, in general terms, the main ideas of systems analysis as a methodology for solving problems.

The application of systems analysis in practice can occur in two situations: when the starting point is the emergence of a new problem and when the starting point is a new opportunity found outside the direct connection with this range of problems. The solution of a problem in a situation of a new problem is carried out according to the following main stages: detection of a problem, assessment of its relevance, determination of the goal and coercive links, definition of criteria, opening of the structure of the existing system, identification of defective elements of the existing system that limit the receipt of a given output, assessment of the weight of their influence on the determined system output criteria, defining a structure for building a set of alternatives, building a set of alternatives, evaluating alternatives, choosing alternatives for implementation, determining the implementation process, agreeing on the solution found, implementing the solution, evaluating the results of implementing the solution.

Implementation of the new feature takes a different path. The use of this opportunity in a given area depends on the presence in it or in related areas of an actual problem that needs such an opportunity for its solution. Exploiting opportunities in the absence of problems can be, at the very least, a waste of resources. Exploiting opportunities when there are problems, but ignoring problems as an end in itself, can deepen and exacerbate the problem. The development of science and technology leads to the fact that the emergence of a new opportunity situation becomes an ordinary phenomenon. This requires a serious analysis of the situation when a new opportunity arises. A capability is disposed of if the best alternative includes that capability. Otherwise, the opportunity may remain unused. The introduction of new technology based on the criterion of the payback period alone can be an example of an approach where the utilization of a new technical capability is carried out outside the analysis of problems. A large percentage of failures in the introduction of machine control systems in the United States at the first stage of their creation is largely a consequence of the lack of a problem-oriented approach during this period.

Consider now how systems analysis represents the organization. An untimely, wasteful solution or an aggravation of the problem and the resulting losses indicate that the mechanism for monitoring the state of the system in which the problem arose, developing and implementing the necessary solutions is not working satisfactorily. For example, this could be when determining a product that is promising for a given market or when adopting a given technical system. But the unsatisfactory work of this mechanism means the unsatisfactory work of the organization that implements this mechanism. Improving its performance can be achieved by improving the performance of the problem-solving functions provided by the systems analysis. To do this, it is necessary to consider the organization not as a subordination structure with established or established relationships, but as a process of solving a problem. This approach allows us to consider the organization as a system, and to describe, study and improve it, use the conceptual apparatus of system analysis.

To improve the performance of the problem-solving functions implemented by the organization, a variety of methods can be used: from the rationalization of document forms to the use of mathematical models and computers. Methods may therefore have alternatives, and their selection may be made in accordance with the principles of systems analysis. The "power" of all functional subsystems from the detection (identification) of problems to the implementation of the solution should be approximately the same. It is pointless to have powerful decision methods if the state identification function is not performed satisfactorily. The decision to improve an organization must grow out of its problems and match them in scale and complexity. Thus, individual methods of improving functions can find their place only when constructing an organization as an integral system.

Conclusion

We see that the world is a unity of systems at different levels of development, and each level serves as a means and basis for the existence of another, higher level of systems development. This applies not only to nature, but also to society, where we observe a number of organizational forms, the most grandiose of which are called "socio-economic formations."

The systems that played their role go away, while others continue to exist.

Of the basic laws of the existence of the Universe is the existence of some systems at the expense of others. Let's say crystals appear on the material of the base rock, solution or melt; plants transform minerals, animals develop at the expense of plants and other animals; man for his existence transforms both animals and plants and systems of inanimate nature.

The world, being a system of systems, the most complex material formation, is in the process of continuous movement, emergence and destruction, mutual transition of one system to another, and some systems change slowly and seem unchanged for a long time, while others change so rapidly that, within the framework of ordinary human ideas, in fact does not exist. The larger the system, the slower it changes, and the smaller, the faster it goes through the stages of its existence. This simple correspondence hides a deep meaning of the still not fully understood connection between space and time. And here you can see one of the laws of the development of matter: from smaller to larger and from larger to smaller, the awareness of which led to an understanding of the development and qualitative change of the systems that make up the world, and the world as a system.

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11. Kulyndyshev V.A., Kuchay V.K. Inheritance: qualitative and quantitative assessments. - System research in geology. Vladivostok, 1979.

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background

Like any scientific concept, general systems theory is based on the results of previous research. Historically, “the beginnings of the study of systems and structures in a general form arose quite a long time ago. Since the end of the 19th century, these studies have become systematic (A. Espinas, N. A. Belov, A. A. Bogdanov, T. Kotarbinsky, M. Petrovich and others) ” . So, L. von Bertalanffy pointed out the deep connection between the theory of systems and the philosophy of G. W. Leibniz and Nicholas of Cusa: “Of course, like any other scientific concept, the concept of a system has its own long history ... In this regard, it is necessary to mention Leibniz’s “natural philosophy” , Nicholas of Cusa with his coincidence of opposites, the mystical medicine of Paracelsus, the version of the history of the sequence of cultural entities, or "systems", proposed by Vico and Ibn Khaldun, the dialectic of Marx and Hegel ... ". One of the immediate predecessors of Bertalanffy is "Tectology" by A. A. Bogdanov, which has not lost its theoretical value and significance at the present time. The attempt made by A. A. Bogdanov to find and generalize general organizational laws, the manifestations of which can be traced at the inorganic, organic, mental, social, cultural, etc. levels, led him to very significant methodological generalizations that opened the way to revolutionary discoveries in the field of philosophy, medicine, economics and sociology. The origins of the ideas of Bogdanov himself also have a developed background, going back to the works of G. Spencer, K. Marx and other scientists. The ideas of L. von Bertalanffy, as a rule, are complementary to the ideas of A. A. Bogdanov (for example, if Bogdanov describes "degression" as an effect, Bertalanffy explores "mechanization" as a process).

Immediate predecessors and parallel projects

Little known to this day remains the fact that already at the very beginning of the 20th century, the Russian physiologist Vladimir Bekhterev, completely independently of Alexander Bogdanov, substantiated 23 universal laws and extended them to the spheres of mental and social processes. Subsequently, a student of Academician Pavlov, Pyotr Anokhin, builds a "theory of functional systems", close in terms of generalization to the theory of Bertalanffy. Often, the founder of holism, Jan Christian Smuts, appears as one of the founders of systems theory. In addition, in many studies on praxeology and the scientific organization of labor, one can often find references to Tadeusz Kotarbinsky, Alexei Gastev and Platon Kerzhentsev, who are considered the founders of system-organizational thinking.

Activities of L. von Bertalanffy and the International Society for the General Systems Sciences

General systems theory was proposed by L. von Bertalanffy in the 1930s. The idea that there are common patterns in the interaction of a large but not infinite number of physical, biological, and social objects was first proposed by Bertalanffy in 1937 at a Philosophy Seminar at the University of Chicago. However, his first publications on the subject did not appear until after World War II. The main idea of the General Systems Theory proposed by Bertalanffy is the recognition of the isomorphism of the laws governing the functioning of system objects. Von Bertalanffy also introduced the concept and explored "open systems" - systems that are constantly exchanging matter and energy with the external environment.

General Systems Theory and World War II

Integration of these scientific and technical areas into the core general systems theory enriched and diversified its content.

The post-war stage in the development of systems theory

In the 50-70s of the XX century, a number of new approaches to the construction of a general theory of systems were proposed by scientists belonging to the following areas of scientific knowledge:

Synergetics in the context of systems theory

Non-trivial approaches to the study of complex system formations are put forward by such a direction of modern science as synergetics, which offers a modern interpretation of such phenomena as self-organization, self-oscillations and co-evolution. Scientists such as Ilya Prigogine and Herman Haken turn their research to the dynamics of nonequilibrium systems, dissipative structures, and entropy production in open systems. The well-known Soviet and Russian philosopher Vadim Sadovsky comments on the situation as follows:

System-wide principles and laws

Both in the works of Ludwig von Bertalanffy and in the works of Alexander Bogdanov, as well as in the works of less significant authors, some general system regularities and principles of functioning and development of complex systems are considered. Traditionally, these include:

"Hypothesis of Semiotic Continuity". “The ontological value of systems studies, as one might think, is determined by a hypothesis that can be conditionally called the “hypothesis of semiotic continuity”. According to this hypothesis, the system is an image of its environment. This should be understood in the sense that the system as an element of the universe reflects some of the essential properties of the latter”: :93. The "semiotic" continuity of the system and environment also extends beyond the structural features of systems. “A change in a system is at the same time a change in its environment, and the sources of change can be rooted both in changes in the system itself and in changes in the environment. Thus, the study of the system would make it possible to reveal the cardinal diachronic transformations of the environment”:94;
"feedback principle". The position according to which stability in complex dynamic forms is achieved by closing feedback loops: “if the action between the parts of a dynamic system has this circular character, then we say that it has feedback”: 82 . The principle of reverse afferentation, formulated by Academician Anokhin P.K., which in turn is a concretization of the feedback principle, fixes that regulation is carried out “on the basis of continuous feedback information about the adaptive result”;
“the principle of organizational continuity” (A. A. Bogdanov) states that any possible system reveals infinite “differences” on its internal boundaries, and, as a result, any possible system is fundamentally open with respect to its internal composition, and thus it is connected in those or other chains of mediation with the entire universe - with one's own environment, with the environment of the environment, etc. This consequence explicates the fundamental impossibility of "vicious circles" understood in the ontological modality. “World ingression in modern science is expressed as continuity principle. It is defined variously; its tectological formulation is simple and obvious: between any two complexes of the universe, with sufficient research, intermediate links are established that introduce them into one chain of ingression» :122 ;
“compatibility principle” (M. I. Setrov), fixes that “the condition for interaction between objects is that they have a relative compatibility property”, that is, relative qualitative and organizational homogeneity;
“the principle of mutually complementary relations” (formulated by A. A. Bogdanov), complements the law of divergence, fixing that “ systemic divergence contains a development trend towards additional connections» :198 . In this case, the meaning of the additional relations is wholly “reduced to exchange connection: in it the stability of the whole, the system, is increased by the fact that one part assimilates what is deassimilated by the other, and vice versa. This formulation can be generalized to any and all additional relations” :196 . Additional relationships are a typical illustration of the constitutive role of closed feedback loops in determining the integrity of the system. The necessary "basis for any stable systemic differentiation is the development of mutually complementary links between its elements" . This principle is applicable to all derivatives of complexly organized systems;
"The Law of Necessary Variety" (W. R. Ashby). A very figurative formulation of this principle fixes that "only diversity can destroy diversity" :294. Obviously, an increase in the diversity of elements of systems as a whole can lead both to an increase in stability (due to the formation of an abundance of interelement connections and the compensatory effects caused by them) and to its decrease (connections may not be of an interelemental nature in the absence of compatibility or weak mechanization, for example, and lead to diversification);
“the law of hierarchical compensations” (E. A. Sedov) fixes that “the actual growth of diversity at the highest level is ensured by its effective limitation at previous levels” . "This law, proposed by the Russian cyberneticist and philosopher E. Sedov, develops and refines Ashby's well-known cybernetic law on the necessary diversity". An obvious conclusion follows from this provision: since in real systems (in the strict sense of the word) the primary material is homogeneous, therefore, the complexity and variety of actions of regulators is achieved only by a relative increase in the level of its organization. Even A. A. Bogdanov repeatedly pointed out that system centers in real systems turn out to be more organized than peripheral elements: Sedov’s law only fixes that the level of organization of the system center must necessarily be higher in relation to peripheral elements. One of the trends in the development of systems is the tendency of a direct decrease in the level of organization of peripheral elements, leading to a direct limitation of their diversity: “only under the condition of limiting the diversity of the lower level, it is possible to form various functions and structures at higher levels”, i.e. "the growth of diversity at the lower level [of the hierarchy] destroys the upper level of organization". In a structural sense, the law means that "the absence of restrictions ... leads to the destructurization of the system as a whole", which leads to a general diversification of the system in the context of its surrounding environment;
“principle of monocentrism” (A. A. Bogdanov), fixes that a stable system “is characterized by one center, and if it is complex, chain, then it has one higher, common center”:273. Polycentric systems are characterized by dysfunction of coordination processes, disorganization, instability, etc. Effects of this kind occur when some coordination processes (pulses) are superimposed on others, which causes the loss of integrity;
“the law of the minimum” (A. A. Bogdanov), generalizing the principles of Liebig and Mitcherlich, fixes: “ the stability of the whole depends on the smallest relative resistances of all its parts at any moment» :146 . “In all those cases where there are at least some real differences in the stability of different elements of the system in relation to external influences, the overall stability of the system is determined by its least partial stability.” Also referred to as the "law of least relative resistance", this provision is a fixation of the manifestation of the principle of the limiting factor: the rate of restoration of the stability of the complex after violating its impact is determined by the smallest partial, and since the processes are localized in specific elements, the stability of systems and complexes is determined by the stability of its weakest link (element );
“the principle of external addition” (derived by S. T. Beer) “reduces to the fact that, by virtue of Gödel’s incompleteness theorem, any control language is ultimately insufficient to perform tasks in front of it, but this disadvantage can be eliminated by including a “black box” in control circuit". The continuity of coordination contours is achieved only by means of a specific arrangement of the hyperstructure, the tree structure of which reflects the ascending line of summation of influences. Each coordinator is built into the hyperstructure in such a way that it transmits only partial influences from the coordinated elements (for example, sensors) upward. Ascending influences to the system center are subjected to a kind of "generalization" when they are summed up in the reducing nodes of the branches of the hyperstructure. Descending on the branches of the hyperstructure coordination influences (for example, to effectors) asymmetrically ascending are subjected to “degeneralization” by local coordinators: they are supplemented by influences coming from feedback from local processes. In other words, the coordination impulses descending from the system center are continuously specified depending on the nature of local processes due to feedback from these processes.
"the recursive structure theorem" (S. T. Beer) suggests that in the case "if a viable system contains a viable system, then their organizational structures must be recursive";
“the law of divergence” (G. Spencer), also known as the principle of chain reaction: the activity of two identical systems tends to progressive accumulation of differences. At the same time, "the divergence of the initial forms proceeds" like an avalanche ", like how the values grow in geometric progressions - in general, according to the type of a progressively ascending series" :186 . The law also has a very long history: “as G. Spencer says, “different parts of a homogeneous aggregation are inevitably subject to the action of heterogeneous forces, heterogeneous in quality or intensity, as a result of which they change differently.” This Spencerian principle of inevitable heterogeneity within any systems ... is of paramount importance for tectology. The key value of this law lies in understanding the nature of the accumulation of "differences", which is sharply disproportionate to the periods of action of exogenous environmental factors.
the "law of experience" (W. R. Ashby) covers the operation of a special effect, a particular expression of which is that "information associated with a change in a parameter tends to destroy and replace information about the initial state of the system" :198. The system-wide formulation of the law, which does not link its action with the concept of information, states that the constant " a uniform change in the inputs of some set of transducers tends to reduce the diversity of this set» :196 - in the form of a set of transducers, both a real set of elements can act, where the effects on the input are synchronized, and one element, the effects on which are dispersed in the diachronic horizon (if its line of behavior shows a tendency to return to its original state, etc. it is described as a set). At the same time, the secondary, additional changing the parameter value makes it possible to reduce the variety to a new, lower level» :196 ; moreover: the reduction in diversity with each change reveals a direct dependence on the length of the chain of changes in the values of the input parameter. This effect, viewed by contrast, makes it possible to more fully comprehend the law of divergence of A. A. Bogdanov - namely, the position according to which "the divergence of the original forms goes" avalanche "":197, that is, in a direct progressive trend: since in the case of uniform effects on set of elements (that is, “transformers”), there is no increase in the variety of states they manifest (and it decreases with each change in the input parameter, that is, the impact force, qualitative aspects, intensity, etc.), then the initial differences are no longer “joined dissimilar changes" :186 . In this context, it becomes clear why the processes occurring in an aggregate of homogeneous units have the power to reduce the diversity of the states of the latter: the elements of such an aggregate “are in continuous connection and interaction, in constant conjugation, in the exchange merging of activities. It is precisely to this extent that the leveling of the developing differences between the parts of the complex is evident” :187: the homogeneity and uniformity of the interactions of units absorb any external disturbing influences and distribute the unevenness over the area of the entire aggregate.
“the principle of progressive segregation” (L. von Bertalanffy) means the progressive nature of the loss of interactions between elements in the course of differentiation, however, the moment carefully hushed up by L. von Bertalanffy should be added to the original version of the principle: in the course of differentiation, channels of interaction mediated by the system center between elements become established. It is clear that only direct interactions between elements are lost, which essentially transforms the principle. This effect turns out to be a loss of "compatibility". It is also important that the process of differentiation itself is, in principle, unrealizable outside of centrally regulated processes (otherwise, the coordination of developing parts would be impossible): “the divergence of parts” cannot necessarily be a simple loss of interactions, and the complex cannot turn into a certain set. independent causal chains, where each such chain develops independently, independently of the others. In the course of differentiation, direct interactions between elements do weaken, but only because of their mediation by the center.
“the principle of progressive mechanization” (L. von Bertalanffy) is the most important conceptual moment. In the development of systems, "parts become fixed in relation to certain mechanisms." The primary regulation of the elements in the original aggregate “is due to dynamic interaction within a single open system, which restores its mobile balance. As a result of progressive mechanization, secondary regulatory mechanisms are superimposed on them, controlled by fixed structures, mainly of the feedback type. The essence of these fixed structures was thoroughly considered by Bogdanov A. A. and called “degression”: in the course of the development of systems, special “degressive complexes” are formed that fix processes in the elements associated with them (that is, limiting the variety of variability, states and processes). Thus, if Sedov's law fixes the limitation of the diversity of elements of the lower functional-hierarchical levels of the system, then the principle of progressive mechanization indicates ways to limit this diversity - the formation of stable degressive complexes: ""skeleton", linking the plastic part of the system, seeks to keep it within its form, and thereby retard its growth, limit its development ", a decrease in the intensity of metabolic processes, the relative degeneration of local system centers, etc. extend to limiting the diversity of external processes.
The “principle of actualization of functions” (first formulated by M. I. Setrov) also fixes a very non-trivial situation. “According to this principle, an object acts as an organized one only if the properties of its parts (elements) appear as functions of the preservation and development of this object”, or: “an approach to organization as a continuous process of becoming the functions of its elements can be called the principle of actualization of functions” .Thus, the principle of actualization of functions fixes that the trend in the development of systems is a trend towards the progressive functionalization of their elements; the very existence of systems is due to the continuous formation of the functions of their elements.

General systems theory and other systems sciences

Notes

Philosophical Dictionary / Ed. I. T. Frolova. - 4th ed.-M.: Politizdat, 1981. - 445 p.
Malinovsky A.A.. General questions of the structure of systems and their significance for biology. In the book: Malinovsky A.A.. Tectology. Theory of systems. Theoretical biology. - M.: "Editorial URSS", 2000. - 488s., P.82.
Bertalanffy L. von. General systems theory - a survey of problems and results. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., S. 34-35.
“Alien in its universality to the type of scientific thinking prevailing at that time, the idea of a general organization was perceived by few people quite fully and did not spread”: Takhtadzhyan A. L. Tectology: history and problems. In: System Research. Yearbook. - M.: Nauka, 1971, p.205. For the current edition, see: Bogdanov A. A. Tectology: General organizational science. - M .: Finance, 2003. The term "tectology" comes from the Greek. τέχτων - builder, creator and λόγος word, doctrine.
“In search of ‘single principles of the world process’, Bekhterev turned to the laws of mechanics, considering them as universal foundations that operate at all levels and floors of living and inanimate nature. A detailed substantiation of these ideas is contained in Bekhterev's Collective Reflexology, in which 23 universal laws are distinguished, which, according to the scientist, operate both in the organic world and in nature, and in the sphere of social relations: the law of conservation of energy, the law of gravity, repulsion, inertia , entropy, continuous motion and variability, etc.”: Brushlinsky A. V., Koltsova V. A. Socio-psychological concept of V. M. Bekhterev / In the book: Bekhterev V. M. Selected Works on Social Psychology.- M.: Nauka, 1994. (Monuments of Psychological Thought), P.5. It is not without interest that Bekhterev, along with Bogdanov, did not bypass the energy teaching of "Mayer - Ostwald - Mach". “The concept of energy ... is considered in Bekhterev’s concept as a basic, substantial, extremely wide ... source of development and manifestation of all forms of human activity and society”: ibid.
Cm.: Anokhin P.K. Key questions of the theory of functional systems. - M.: Nauka, 1980.
Bogolepov V., Malinovsky A. Organization // Philosophical Encyclopedia. In 5 volumes - M .: Soviet Encyclopedia. Edited by F. V. Konstantinov. 1960-1970.
Bertalanffy L. von General Systems Theory - Critical Review / In the book: Studies in General Systems Theory. - M .: Progress, 1969. S. 23-82. In English: L. von Bertalanffy, General System Theory - A Critical Review // "General Systems", vol. VII, 1962, p. 1-20.
The term "cybernetics" (ancient Greek. κυβερνήτης - helmsman) was first used by M. A. Ampere in the meaning of the science of government. About cybernetics as a science about the general laws of the processes of control and transmission of information in various systems; see for example:
Viner N. Cybernetics, or control and communication in animals and machines / Per. from English. 2nd ed. - M.: Soviet radio, 1968;
Ashby R. W. Introduction to cybernetics. - M.: KomKniga, 2005. - 432 p.
rand corporation(short for English. Research and Development). “In 1948, within the United States Department of the Air Force … the Weapons Systems Evaluation Group (WSEG) was formed, which played an important role in the development and application of systems analysis …” See. Nikanorov S.P. System analysis: a stage in the development of problem solving methodology in the USA // In the book: Optner S. L. System analysis for solving business and industrial problems. - M.: Soviet radio, 1969.- 216s.- S.24-25.
“In the 50s, numerous research system groups arose in a number of countries ... In the USA, the most powerful of them work within the framework of the RAND Corporation, System Development Corporation, etc.”: Blauberg I. V., Sadovsky V. N., Yudin E. G. System Research and General Systems Theory // In the book: System Research. Yearbook. - M.: Nauka, 1973.- P.11.
See, for example: Morse F, Kimbell J. Operations research methods. - M.: Soviet radio, 1956; Akof R. L., Sasieni M. Operations Research Methods / Per. from English - M .: Mir, 1971. - 536s.
See for example: Good G.-H., Makall R.-E. System engineering. Introduction to the design of large systems / Per. from English - M.: Soviet radio, 1962.
Kirby, p. 117
Kirby, pp. 91-94
See for example: Shchedrovitsky G.P.. Selected works. - M.: "School of cultural policy", 1995. - 800s.
See for example: . On the principles of systems research // Questions of Philosophy, No. 8, 1960, pp.67-79.
See for example: Sadovsky V. N. Foundations of General Systems Theory: Logical and Methodological Analysis. M.: "Nauka", 1974; Sadovsky V. N. Change of paradigms of systems thinking. In: System Research. Methodological problems. Yearbook. 1992-1994. M., 1996, pp.64-78; Sadovsky V. N. General systems theory as a metatheory. XIII International Congress on the History of Science. M.: "Nauka", 1971.
See for example: . System Research and General Systems Theory. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp.7-29; Blauberg I. V., Yudin E. G. Formation and essence of the system approach, M., 1973.
See for example: Yudin E. G. System approach and principle of activity: methodological problems of modern science. Academy of Sciences of the USSR, Institute of the History of Natural Science and Technology. M.: "Nauka", 1978.
See for example: Uyomov A. I. System approach and general systems theory. - M.: Thought, 1978. - 272 p.; Uyomov A. I. Systems and system parameters. // Problems of formal analysis of systems. - M., Higher School, 1968. - S. 15-34 .; Uyomov A. I. Logical analysis of a systematic approach to objects and its place among other research methods. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 80-96; Uyomov A.I. L. von Bertalanffy and. In: System approach in modern science. - M.: "Progress-Tradition", 2004. - 560s., pp.37-52.
See for example: Laszlo, Ervin. The Systems View of the World: a Holistic Vision for Our Time. Hampton press, Inc., 1996; Laszlo, Ervin. 1996. The Systems View of the World. Hampton Press, NJ.
See for example: Akof R. L. Systems, organizations and interdisciplinary research. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.143-164; Akof R. L. General systems theory and systems research as opposite concepts of systems science. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M.: "Mir", 1966, pp.66-80; Akof R. L., Sasieni M. Fundamentals of Operations Research / Per. from English. M.: "Mir", 1971, 536s.
See for example: Setrov M. I. General principles of systems organization and their methodological significance. L .: "Science", 1971; Setrov M. I. The principle of consistency and its basic concepts. In: Problems of System Research Methodology. M.: "Thought", 1970, pp.49-63; Setrov M. I. The degree and height of the organization of systems. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 156-168.
See for example: Sedov E. A. Information-entropy properties of social systems // Social sciences and modernity, No. 5, 1993, pp. 92-100. See also: Tsirel S. "QWERTY-effects", "Path Dependence" and the law of hierarchical compensation // Questions of Economics, No. 8, 2005, pp.19-26.
See for example: Serov N. K. On the diachronic structure of processes // Questions of Philosophy, No. 7, 1970, pp.72-80.
See for example: Melnikov, G. P.. - M.: Soviet radio, 1978. - 368 p.
See for example: Lyapunov A. A. On the control systems of living nature // Problems of Cybernetics, Sat. No. 10. State publishing house of physical and mathematical literature: 1963, pp.179-193; Lyapunov A. A. Relationship between the structure and origin of control systems. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp.251-257.
See for example: Kolmogorov A. N. Information theory and theory of algorithms. - M.: Nauka, 1987. - 304 p.
See for example: Mesarovic M. General Systems Theory: Mathematical Foundations / M. Mesarovich, Y. Takahara; Per. from English. E. L. Nappelbaum; ed. V. S. Emelyanova. - M.: "Mir", 1978; Mesarovic M. Theory of hierarchical multilevel systems. Per. from English. Ed. I. F. Shakhnova. Foreword Corresponding Member Academy of Sciences of the USSR G. S. Pospelova. M.: "Mir", 1973; Mesarovic M. Systems Theory and Biology: A Theorist's Perspective. In: System Research. Yearbook. - M.: "Nauka", 1970. - 208 p., pp. 137-163.
See for example: Zade L. A. Fundamentals of a new approach to the analysis of complex systems and decision-making processes. In the book: "Mathematics Today". - M.: "Knowledge", 1974.
See for example: Kalman, Falb, Arbib. Essays on the mathematical theory of systems
See for example: Anokhin P. K. Systemogenesis as a general regularity of the evolutionary process. Bull. exp. biol. and honey. 1948, Vol. 26, No. 8, pp. 81-99; Anokhin P. K. Key questions of the theory of functional systems. M.: "Nauka", 1980.
See for example: Trincher K.S. Biology and information: elements of biological thermodynamics. M.: "Nauka", 1965; Trincher K.S. Existence and evolution of living systems and the second law of thermodynamics // Questions of Philosophy, No. 6, 1962, pp.154-162.
See for example: Takhtadzhyan A. L. Tectology: history and problems. In: System Research. Yearbook. - M.: "Nauka", 1971, 280 pp., pp. 200-277; Takhtadzhyan A. L. Principia Tectologica. Principles of organization and transformation of complex systems: an evolutionary approach. Ed. 2nd, add. and reworked. St. Petersburg: SPHFA Publishing House, 2001. - 121p.
See for example: Levich A. P. Substitutional time of natural systems // Questions of Philosophy, No. 1, 1996, pp.57-69; Levich A. P. Entropy parameterization of time in the general theory of systems. In: System approach in modern science. - M .: "Progress-Tradition", 2004. - 560 pp., pp. 167-190.
See for example: Urmantsev Yu. A. Experience of axiomatic construction of the general theory of systems // System research: 1971. M., 1972, pp.128-152; Urmantsev Yu. A., Trusov Yu. P. On the properties of time // Questions of Philosophy, 1961, No. 5, pp. 58-70.
See for example: Geodakyan V. A. Organization of living and non-living systems. In: System Research. Methodological problems. Yearbook. - M., Nauka, 1970, pp. 49-62; Geodakyan V. A. System-evolutionary interpretation of brain asymmetry. In: System Research. Methodological problems. Yearbook. - M., Nauka, 1986, pp. 355-376.
See for example: Ashby W. R. Introduction to Cybernetics: Per. from English. / under. ed. V. A. Uspensky. Foreword A. N. Kolmogorova. Ed. 2nd, stereotypical. - M.: KomKniga, 2005. Ashby W. R. General systems theory as a new scientific discipline. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.125-142; Ashby W. R. Principles of self-organization. In: Principles of self-organization. Per. from English. Ed. and with a preface by Dr. tech. Sciences A. Ya. Lerner, M .: "Mir", 1966, pp.314-343.
See for example: Rapoport A. Remarks on the general theory of systems. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M.: Mir, 1966, pp. 179-182; Rapoport A. Mathematical aspects of abstract systems analysis. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.83-105; Rapoport A. Different approaches to general systems theory. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 55-80.
Cm. Weick, Karl. Educational Organizations as Loosely Coupled Systems // Administrative Science Quarterly. 1976 Vol. 21. P. 1-19.
See for example: George Jiri Klir. An Approach to General Systems Theory, Van Nostrand Reinhold, New York, 1969; George Jiri Klir. Methodology in Systems Modeling and Simulation, with B. P. Zeigler, M. S. Elzas, and T. I. Oren (ed.), North-Holland, Amsterdam. 1979.
See for example: Beer S. T. Cybernetics and management. Translation from English. V. Ya. Altaeva / Ed. A. B. Chelyustkina. Foreword L. N. Ototsky. Ed. 2nd. - M.: "KomKniga", 2006. - 280s.; Beer S. T. The brain of the firm. Translation from English. M. M. Lopukhina, Ed. 2nd, stereotypical. - M.: "Editorial URSS", 2005. - 416p.
See for example: Prigogine I., Stengers I. Order out of chaos: A new dialogue between man and nature. Moscow: Progress, 1986; Prigogine I. From existing to emerging: Time and complexity in the physical sciences. Moscow: Nauka, 1985.
Sadovsky V. N. Ludwig von Bertalanffy and the development of systems research in the 20th century. In: System approach in modern science. - M.: "Progress-Tradition", 2004, p.28.
Vinogradov V. A., Ginzburg E. L.. System, its updating and description. In: System Research. Yearbook. - M.: "Nauka", 1971, 280s.
Ashby R. W
Anokhin P.K.. Key questions of the theory of functional systems. M.: "Nauka", 1980, p.154.
Bogdanov A.A.. Tectology: General organizational science. Editorial board V. V. Popkov (responsible editor) and others. Compiled, foreword and comments by G. D. Gloveli. Afterword by V. V. Popkov. - M.: "Finance", 2003. ISBN 5-94513-004-4
Setrov M.I. General principles of systems organization and their methodological significance. L .: "Science", 1971, p.18.
Takhtadzhyan A.L.. Tectology: history and problems. In: System Research. Yearbook. - M.: "Nauka", 1971, p.273.
Sedov E.A.. Information-entropy properties of social systems // ONS, No. 5, 1993, p.92.
Tsirel S. "QWERTY-effects", "Path Dependence" and the law of hierarchical compensation // Questions of Economics, No. 8, 2005, p.20.
Sedov E.A.. Information-entropy properties of social systems // ONS, No. 5, 1993, p.100.
Sedov E.A.. Information-entropy properties of social systems // ONS, No. 5, 1993, p.99.
Takhtadzhyan A.L.. Tectology: history and problems. In: System Research. Yearbook. - M.: "Nauka", 1971, p.245.
Beer S.T. Cybernetics and management. Translation from English. V. Ya. Altaeva / Ed. A. B. Chelyustkina. Foreword L. N. Ototsky. Ed. 2nd. - M.: "KomKniga", 2006. - 280s., P.109.
Beer S.T. The brain of the firm. Translation from English. M. M. Lopukhina, Ed. 2nd, stereotypical. - M .: "Editorial URSS", 2005. - 416 p., P. 236.
Takhtadzhyan A. L. Tectology: history and problems. In: System Research. Yearbook. - M.: "Nauka", 1971, p.259.
Bertalanffy L. von. An outline of General System Theory. - «British Journal for Philosophy of Science». Vol. 1, no. 2, P.148.
This is precisely what determines the whole complexity of deep rearrangements of the material captured in the process. After all, “each differentiation is a local integration, a local solution that connects with others in a system of solution or global integration ...”: Deleuze J. Difference and repetition. St. Petersburg: "Petropolis", 1998, p.259.
“The primary state is that of a unitary system which splits up gradually into independent causal chains. We may call this progressive segregation»: Bertalanffy L. von. An outline of General System Theory. - «British Journal for Philosophy of Science». Vol. 1, no. 2. (Aug., 1950), P.148.
Bertalanffy L. von. An outline of General System Theory. - «British Journal for Philosophy of Science». Vol. 1, no. 2, P.149.
Bertalanfi L. background. General systems theory - a critical review. In: Research in General Systems Theory. Collection of translations. M.: Progress, 1969, p.43.
Bogdanov A. A. Tectology: General organizational science. Editorial board V. V. Popkov (responsible editor) and others. Compiled, foreword and comments by G. D. Gloveli. Afterword by V. V. Popkov. - M.: "Finance", 2003, p.287.
Setrov M. I. Degree and height of organization of systems. In: System Research. Yearbook. - M.: "Nauka", 1969, p.159.
There.
C. E. Shannon "A Mathematical Theory of Communication" (Translation in the collection Shannon K."Works on Information Theory and Cybernetics". - M.: IL, 1963. - 830 p., S. 243-322)
Anokhin P. K. Fundamental questions of the general theory of functional systems. M., 1971.

Literature

Akof R. L., Sasieni M. Fundamentals of Operations Research / Per. from English. M .: "Mir", 1971. - 536s.
Bertalanffy L. von
Beer S. T. Cybernetics and management. Translation from English. V. Ya. Altaeva / Ed. A. B. Chelyustkina. Foreword L. N. Ototsky. Ed. 2nd. - M.: "KomKniga", 2006. - 280s. ISBN 5-484-00434-9
Blauberg I. V., Yudin E. G
Bogdanov A. A. Tectology: General organizational science. International Alexander Bogdanov Institute. Editorial board V. V. Popkov (responsible editor) and others. Compiled, foreword and comments by G. D. Gloveli. Afterword by V. V. Popkov. M.: "Finance", 2003. ISBN 5-94513-004-4
Mesarovic M. General theory of systems: mathematical foundations / M. Mesarovich, Y. Takahara; Per. from English. E. L. Nappelbaum; ed. V. S. Emelyanova. - M.: "Mir", 1978.
Prigogine I
Ashby W. R. Introduction to Cybernetics: Per. from English. / under. ed. V. A. Uspensky. Foreword A. N. Kolmogorova. Ed. 2nd, stereotypical. - M.: "KomKniga", 2005. - 432 p. ISBN 5-484-00031-9
Yudin E. G. System approach and principle of activity: methodological problems of modern science. Academy of Sciences of the USSR, Institute of the History of Natural Science and Technology. M.: "Nauka", 1978.

Books in Russian

Akof R. L., Sasieni M. Fundamentals of Operations Research / Per. from English. - M.: Mir, 1971. - 536 p.
Anokhin P. K. Key questions of the theory of functional systems. - M.: Nauka, 1980.
Bekhterev V. M. Selected Works in Social Psychology. - M.: Nauka, 1994. - 400 p. - (Monuments of psychological thought) ISBN 5-02-013392-2
Beer St. Cybernetics and management. Translation from English. V. Ya. Altaeva / Ed. A. B. Chelyustkina. Foreword L. N. Ototsky. Ed. 2nd. - M.: KomKniga, 2006. - 280 p. ISBN 5-484-00434-9
Beer St. The brain of the firm. Translation from English. M. M. Lopukhina, Ed. 2nd, stereotypical. - M.: Editorial URSS, 2005. - 416 p. ISBN 5-354-01065-9
Blauberg I. V., Yudin E. G. Formation and essence of the system approach. M., 1973.
Bogdanov A. A. Questions of socialism: works of different years. - M.: Politizdat, 1990. - 479 p. - (Library of Socialist Thought) ISBN 5-250-00982-4
Bogdanov A. A. Tectology: General organizational science. International Alexander Bogdanov Institute. Editorial board V. V. Popkov (responsible editor) and others. Compiled, foreword and comments by G. D. Gloveli. Afterword by V. V. Popkov. - M.: Finance, 2003. ISBN 5-94513-004-4

A classic work in the field of organizational theory and management principles. Bogdanov shows that "the entire experience of science convinces us that the possibility and probability of solving problems increase when they are formulated in generalized form” (p. 23)

Bogdanov A. A. Empiriomonism: articles on philosophy / Ed. ed. V. N. Sadovsky. Afterword by V.N. Sadovsky; A. L. Andreeva and M. A. Maslina. - M.: Respublika, 2003. - 400 p. - (Twentieth Century Thinkers) ISBN 5-250-01855-6
Baudrillard J. Symbolic exchange and death. - M.: Dobrosvet, 2000. - 387 p. ISBN 5-7913-0047-6

“In 1963, the Soviet mathematician Lyapunov proved that in all living systems, a small amount of energy or matter is transmitted through precisely established channels, containing a huge amount of information, which is subsequently responsible for controlling large amounts of energy and matter. From this perspective, many phenomena, both biological and cultural (accumulation, feedback, communication channels, etc.), can be seen as different aspects of information processing... Five years ago, I drew attention to the convergence of genetics and linguistics - autonomous but parallel disciplines in a wider range of communication sciences (which also includes zoosemiotics). The terminology of genetics is full of expressions taken from linguistics and information theory (Jacobson 1968, who emphasized both the main similarities and significant structural and functional differences between the genetic and verbal code) ... Thus, both language and living systems can be described from a single cybernetic point of view " (p.128)

Bosenko V. A. General theory of development. - Kyiv, 2001. - 470s. ISBN 966-622-035-0
Wiener N. Cybernetics, or control and communication in animals and machines / Per. from English. I. V. Solovyov and G. N. Povarova. Ed. G. N. Povarova. - 2nd edition. - M.: "Science"; Main edition of publications for foreign countries, 1983. - 344p.
Volkova V. N. Theory of systems: textbook / V. N. Volkova, A. A. Denisov. - M .: "Higher School", 2006. - 511s., ill. ISBN 5-06-005550-7
Gastev A. K. How to work. A practical introduction to the science of labor organization. Ed. 2nd. M, "Economics", 1972. - 478s.
Gig J. van. Applied General Systems Theory. Per. from English. - M.: "Mir", 1981. - 336 p., ill.
Zhilin D. M. Systems Theory: An Experience in Building a Course. Ed. 4th, rev. - M.: "LKI", 2007. - 184 p. ISBN 978-5-382-00292-7
Kachala V. V. Fundamentals of systems theory and system analysis. Textbook for universities. - M.: "Hot Line" - Telecom, 2007. - 216 p.: ill. ISBN 5-93517-340-9
Kerzhentsev P. M. Organization principles. (Selected works). M .: "Economics", 1968. - 464 p.
Kolmogorov A. N. Information theory and theory of algorithms. - M.: "Nauka", 1987. - 304 p.
Lefevre V. A. Reflection. - M., "Cogito-Center", 2003. - 496s. ISBN 5-89353-053-5
Malinovsky A. A. Tectology. Theory of systems. Theoretical biology. - M.: "Editorial URSS", 2000. - 488s. (Philosophers of Russia of the 20th century) ISBN 5-8360-0090-5
Mamchur E. A., Ovchinnikov N. F., Uemov A. I. The principle of simplicity and the measure of complexity. - M.: Nauka, 1989. - 304 p. ISBN 5-02-007942-1
Melnikov, G. P. Systemology and linguistic aspects of cybernetics. - M.: "Soviet radio", 1978. - 368 p.
Mesarovic M. General Systems Theory: Mathematical Foundations / M. Mesarovich, Y. Takahara; Per. from English. E. L. Nappelbaum; ed. V. S. Emelyanova. - M.: "Mir", 1978.
Mesarovic M. Theory of hierarchical multilevel systems. Per. from English. Ed. I. F. Shakhnova. Foreword Corresponding Member Academy of Sciences of the USSR G. S. Pospelova. M .: "Mir", 1973.
Mesarovic M., Takahara I. General Systems Theory: Mathematical Foundations. Per. from English. - M.: "Mir", 1978. - 311 p.
Morse F, Kimbell J.. Operations research methods. Per. from English. I. A. Poletaeva and K. N. Trofimova. Ed. A. F. Gorokhova. - M.: "Soviet radio", 1956.
Nikolaev V.I., Brook V.M.. System engineering: methods and applications. Leningrad: "Engineering", 1985.
Optner S. L. Systems analysis for solving business and industrial problems. Per. from English. S. P. Nikanorov. M .: "Soviet radio", 1969. - 216s.
Prigogine I., Stengers I. Order out of chaos: A new dialogue between man and nature. M.: "Progress", 1986.
Prigogine I. From existing to emerging: Time and complexity in the physical sciences. M.: "Nauka", 1985.
Redko V. G. Evolutionary cybernetics / V. G. Redko. - M.: "Nauka", 2003. - 156 p. - (Computer Science: Unlimited Possibilities and Possible Limitations) ISBN 5-02-032793-X
Sadovsky V. N. Foundations of General Systems Theory: Logical and Methodological Analysis. M.: "Nauka", 1974.
Setrov M. I. General principles of systems organization and their methodological significance. L .: "Science", 1971.
System analysis and decision making: Dictionary-reference book: Proc. allowance for universities / Under. Ed. V. N. Volkova, V. N. Kozlova. - M.: "Higher School", 2004. - 616 p.: ill., p.96. ISBN 5-06-004875-6
Systems approach and psychiatry. Digest of articles. Minsk: "High School", 1976.
Takhtadzhyan A.L. Principia Tectologica. Principles of Organization and Transformation of Complex Systems: An Evolutionary Approach. - Ed. 2nd, revised. and additional .. - St. Petersburg: SPFHA Publishing House, 2001. - 121 p. - 500 copies. - ISBN 5-8085-0119-9
Trincher K.S. Biology and information: elements of biological thermodynamics. M.: "Nauka", 1965.
Uyomov A.I. System approach and general systems theory. - M.: Thought, 1978. - 272 p.

One of the main works of A. I. Uemov, which outlines his version of the GTS - Parametric general system theory, its formal apparatus is the language of ternary description (LTO), as well as the most complete list of system regularities.

Khomyakov P. M. System analysis: a short course of lectures / Ed. V. P. Prokhorov. Ed. 2nd, stereotypical. - M.: "KomKniga", 2007. - 216s. ISBN 978-5-484-00849-0, ISBN 5-484-00849-2
Shchedrovitsky G.P. Selected works. - M.: "School of cultural policy", 1995. - 800s. ISBN 5-88969-001-9
Ashby W. R. Introduction to Cybernetics: Per. from English. / under. ed. V. A. Uspensky. Foreword A. N. Kolmogorova. Ed. 2nd, stereotypical. - M.: "KomKniga", 2005. - 432 p. ISBN 5-484-00031-9
Yudin E. G. System approach and principle of activity: methodological problems of modern science. Academy of Sciences of the USSR, Institute of the History of Natural Science and Technology. M.: "Nauka", 1978.

Textbooks in Russian

Articles in Russian

Russian periodicals provide rich materials for research in the field of systems theory. First of all, the classic journal “Problems of Philosophy” and the yearbook “System Research. Methodological problems". In addition, a whole lot of deep and significant works have been published in such publications as "Investigations in General Systems Theory", "Problems of Cybernetics", "Principles of Self-Organization", etc., the value of which has not been lost at the present time.

Articles in the journal "Problems of Philosophy"

. On the specifics of biological structures // Questions of Philosophy, 1965, No. 1, pp. 84-94.
Kovalev I. F.. The second law of thermodynamics in the individual and general evolution of living systems // Questions of Philosophy, 1964, No. 5, pp.113-119.
Kremyansky V. I. The emergence of the organization of material systems // Questions of Philosophy, 1967, No. 3, pp.53-64.
Levich A. P. Substitutional time of natural systems // Questions of Philosophy, 1996, No. 1, pp.57-69.

The author shows how the theory of systems "allows you to explicate the properties of time, given by specific structures of systems, but leads to the" indistinguishability "of the temporal properties of objects at the underlying levels of the structure" (p.63)

Lektorsky V. A., Sadovsky V. N. On the principles of systems research // Questions of Philosophy, 1960, No. 8, pp.67-79.
Moiseev N. N. Tectology of A. A. Bogdanov - modern perspectives // Questions of Philosophy, 1995, No. 8, pp. 8-13.
Prigogine I. R. Philosophy of instability // Questions of Philosophy, 1991, No. 6, pp.46-57.
Serov N. K. On the diachronic structure of processes // Questions of Philosophy, 1970, No. 7, pp. 72-80.

The article deals with the categories of structural analysis of processes: diachronic structure and module of the process, calendar frame, superposition, etc.

Spirkin A. G., Sazonov B. V. Discussion of methodological problems in the study of systems and structures // Questions of Philosophy, 1964, No. 1, pp.158-162.
Trincher K.S. Existence and evolution of living systems and the second law of thermodynamics // Questions of Philosophy, 1962, No. 6, pp.154-162.
Urmantsev Yu. A. The nature of adaptation (systemic explication) // Questions of Philosophy, 1998, No. 12.
Urmantsev Yu. A., Trusov Yu. P. On the properties of time // Questions of Philosophy, 1961, No. 5, pp. 58-70.
Ashby W. R. The use of cybernetics in biology and sociology // Questions of Philosophy, 1958, No. 12, pp. 110-117.

Some of the system-wide laws are considered, for example, Mayer's principle. “It says that certain processes (such as perpetuum mobile and the creation of energy from nothing) are impossible” (p.112)

Articles in the yearbook “System Research. Methodological problems»

Bertalanffy L. von. History and status of general systems theory. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp.20-37.
Bertalanffy L. von. General systems theory - a survey of problems and results. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 30-54.

Some information is given regarding the processes of segregation and mechanization, as well as "problems of order, organization, integrity, teleology, etc., which were demonstratively excluded from consideration in mechanistic science" (p.37)

Blauberg I. V., Sadovsky V. N., Yudin E. G.. System Research and General Systems Theory. In: System Research. Yearbook. - M.: "Nauka", 1973, pp.7-29.
Vedenov M. F., Kremyansky V. I. Towards an analysis of the general and biological principles of self-organization. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 140-155.

The basics of the system design are considered, in particular - "principles of building on and removing" (p.142)

Vinogradov V. A., Ginzburg E. L.. System, its updating and description. In: System Research. Yearbook. - M.: "Nauka", 1971, 280 pp., pp. 93-102.
Gaaze-Rapoport M. G. Cybernetics and systems theory. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp.63-75.
Geodakyan V. A. Organization of living and non-living systems. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1970, pp. 49-62.
Geodakyan V. A. System-evolutionary interpretation of brain asymmetry. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1986, pp. 355-376.
Kagan M. S. System and structure. - In the book: System Research; Methodological problems. Yearbook. M.: 1983. pp. 86-106.
Lyapunov A. A. Relationship between the structure and origin of control systems. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp.251-257.
Mesarovic M. Systems Theory and Biology: A Theorist's Perspective. In: System Research. Yearbook. - M.: "Nauka", 1970. - 208 p., pp. 137-163.
Rapoport A. Different approaches to general systems theory. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 55-80.
Sadovsky V. N. Paradoxes of systems thinking. In: System Research. Methodological problems. Yearbook. - M.: "Nauka", 1973, pp. 133-146.
Sadovsky V. N. Change of paradigms of systems thinking. In: System Research. Methodological problems. Yearbook. 1992-1994. M., 1996, pp.64-78.
Setrov M. I. The degree and height of the organization of systems. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 156-168.
Takhtadzhyan A. L. Tectology: history and problems. In: System Research. Yearbook. - M.: "Nauka", 1971, 280 pp., pp. 200-277.

The organizational laws derived by A. A. Bogdanov are generalized. For example, “the basis of any stable system differentiation is the development of mutually complementary links between its elements” (p. 273).

Uyomov A. I. Logical analysis of a systematic approach to objects and its place among other research methods. In: System Research. Yearbook. - M.: "Nauka", 1969. - 203 p., pp. 80-96.
Urmantsev Yu. A. Experience of axiomatic construction of the general theory of systems // System Research: 1971. M., 1972, pp.128-152.

Articles in other specialized publications "Research on General Systems Theory", "Problems of Cybernetics", "Principles of Self-Organization"

Akof R. L. Systems, organizations and interdisciplinary research. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.143-164.
Akof R. L. General systems theory and systems research as opposite concepts of systems science. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M.: "Mir", 1966, pp.66-80.
Bertalanffy L. von. General systems theory - a critical review. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.23-82.
Boulding K. General systems theory is the skeleton of science. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.106-124.
Volkova V. N. Diffuse (poorly organized) system. In the book: System analysis and decision making: Dictionary-reference book: Proc. allowance for universities / Under. Ed. V. N. Volkova, V. N. Kozlova. - M.: "Higher School", 2004. - 616 p.: ill., p.96. ISBN 5-06-004875-6
Volkova V. N. Information infrastructure. In the book: System analysis and decision making: Dictionary-reference book: Proc. allowance for universities / Under. Ed. V. N. Volkova, V. N. Kozlova. - M .: "Higher School", 2004. - 616 pp.: ill., pp. 158-161. ISBN 5-06-004875-6
Drenik R. Principle of causality and predictability of signals. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M.: Mir, 1966, pp.158-170.
Kapralov M. V. Tectological rule of behavior of self-reproducing systems. In: Tectological Almanac. Issue I. A. Bogdanov International Institute / Ed. collegium G. D. Gloveli, V. D. Mekhryakov, V. V. Popkov. M.: "2000", pp.121-127.
Lange Oh. Whole and development in the light of cybernetics. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.181-251.
Levich A. P. Entropy parameterization of time in the general theory of systems. In: System approach in modern science. - M .: "Progress-Tradition", 2004. - 560 pp., pp. 167-190. ISBN 5-89826-146-X

The author shows how “a category-theoretic description of systems does not require the mandatory explication of a natural system by a mathematical structure. A “qualitative” categorical description of systems is possible, that is, an enumeration and description of the states of the system, as well as all transitions between states ... ”(P.177)

Lyapunov A. A. On the control systems of living nature // Problems of Cybernetics, Sat. No. 10. State publishing house of physical and mathematical literature: 1963, pp.179-193.
Rapoport A. Remarks on the general theory of systems. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M .: "Mir", 1966, pp. 179-182.
Rapoport A. Mathematical aspects of abstract systems analysis. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.83-105.
Sadovsky V. N. History of creation, theoretical foundations and fate of empiriomonism A. A. Bogdanova. Afterword to the book: Empiriomonism: articles on philosophy / Ed. ed. V. N. Sadovsky. Afterword by V.N. Sadovsky; A. L. Andreeva and M. A. Maslina. - M.: "Republic", 2003. - 400s. - (Thinkers of the XX century), pp.340-365.
Sadovsky V. N. Ludwig von Bertalanffy and the development of systems research in the 20th century. In: System approach in modern science. - M.: "Progress-Tradition", 2004. - 560s., pp.7-36. ISBN 5-89826-146-X
Sadovsky V. N. General systems theory as a metatheory. XIII International Congress on the History of Science. M.: "Nauka", 1971.
Sedov E. A. Information-entropy properties of social systems // Social sciences and modernity, No. 5, 1993, pp. 92-100.
Sedov E. A. Parts and the whole in biosystems: what L. von Bertalanffy did not know. In: System approach in modern science. - M.: "Progress-Tradition", 2004. - 560s., pp.504-508. ISBN 5-89826-146-X
Setrov M. I. The principle of consistency and its basic concepts. In: Problems of System Research Methodology. M.: "Thought", 1970, pp.49-63.
Uyomov A. I. L. von Bertalanffy and parametric general systems theory. In: System approach in modern science. - M.: "Progress-Tradition", 2004. - 560s., pp.37-52. ISBN 5-89826-146-X
Shterenberg M. I. Beginnings of the content theory of systems. In: System approach in modern science. - M.: "Progress-Tradition", 2004. - 560s., pp.525-548. ISBN 5-89826-146-X
Shushpanov A. N. General organizational science and "organic" thinking. In: Tectological Almanac. Issue I. A. Bogdanov International Institute / Ed. collegium G. D. Gloveli, V. D. Mekhryakov, V. V. Popkov. M.: "2000", pp.325-329.
Kharin Yu. A. The law of negation of negation // Philosophical Sciences, No. 4, 1979, pp.110-119.

The author considers the application of the categories of dialectics to the analysis of complex systems. “In contrast to destruction, withdrawal is understood as a denial of the system with the retention, preservation and transformation of any of its structural elements in a new phenomenon ”(p. 110)

Tsirel S. "QWERTY-effects", "Path Dependence" and the law of hierarchical compensation // Questions of Economics, No. 8, 2005, pp.19-26.
Churchman Ch. One approach to general systems theory. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M .: "Mir", 1966, pp. 183-186.
Ashby W. R. A few notes. In: General Systems Theory. Per. from English. V. Ya. Altaev and E. L. Nappelbaum. M.: Mir, 1966, pp.171-178.
Ashby W. R. General systems theory as a new scientific discipline. In: Research in General Systems Theory. Collection of translations. M.: "Progress", 1969, pp.125-142.
Ashby W. R. Principles of self-organization. In: Principles of self-organization. Per. from English. Ed. and with a preface by Dr. tech. Sciences A. Ya. Lerner, M .: "Mir", 1966, pp.314-343.

Articles in other publications

Anokhin P. K. Systemogenesis as a general regularity of the evolutionary process. Bull. exp. biol. and honey. 1948, Vol. 26, No. 8, pp. 81-99.
Bogolepov V., Malinovsky A. Organization // Philosophical Encyclopedia. In 5 volumes - M .: Soviet Encyclopedia. Edited by F. V. Konstantinov. 1960-1970.
Zade L. A. Fundamentals of a new approach to the analysis of complex systems and decision-making processes. In the book: "Mathematics Today". - M.: "Knowledge", 1974.

Books in English

Articles in English

Ash, M. G. (1992). Cultural Contexts and Scientific Change in Psychology: Kurt Lewin in Iowa. American Psychologist Vol. 47, no. 2, pp. 198-207.
Bertalanffy, Ludwig Von. (1955). An Essay on the Relativity of Categories. Philosophy of Science, Vol. 22, no. 4, pp. 243-263.

GENERAL SYSTEM THEORY- in a broad sense, it is understood as an interdisciplinary area of scientific research, the tasks of which include: 1) the development of generalized models of systems; 2) building a logical and methodological apparatus for describing the functioning and behavior of system objects ... Geological Encyclopedia

General systems theory- a scientific discipline that develops methodological principles for the study of systems. These principles are interdisciplinary in nature, since systems of various kinds are studied by many sciences: biology, economics, ... ... Economic and Mathematical Dictionary

general systems theory- A scientific discipline that develops methodological principles for the study of systems. These principles are interdisciplinary in nature, since systems of various types are studied by many sciences: biology, economics, technology, etc. One of… … Technical Translator's Handbook

GENERAL SYSTEM THEORY- (general system theory) see System theory ... Big explanatory sociological dictionary

GENERAL SYSTEM THEORY- a specially scientific and logical-methodological concept of studying objects that are systems. O. t. s. is closely related to the systematic approach and is a concretization and logical and methodical expression of its principles and methods. Fundamentals of O. t. with ... Encyclopedic Dictionary of Psychology and Pedagogy

Parametric general systems theory- Parametric general systems theory is one of the variants of the general systems theory developed by Avenir Ivanovich Uyomov and his philosophical school. During the "boom" for systematic research in the 60-80s. In the twentieth century, various theories were proposed ... Wikipedia, A.I. Uyomov. The monograph discusses the philosophical problems of system research, the importance of a systematic approach to the study of complex phenomena of reality, for practice, one of the options is presented ...

Lecture 2TO.rtf

Lecture 2. System views

Formation of system views .
Concepts characterizing the structure of systems.
System classification .
Properties of the system.

Formation of system views

The concepts of "system" and "systematic" play an important role in modern science and practice. Since the middle of the XX century. intensive developments are underway in the field of a systematic approach to research and systems theory. At the same time, the very concept of a system has a long history. Initially, systemic representations were formed within the framework of philosophy: back in the ancient world, the thesis was formulated that the whole is greater than the sum of its parts. Ancient philosophers (Plato, Aristotle, etc.) interpreted the system as a world order, that systemicity is a property of nature.

The principles of systematicity were actively studied in philosophy (for example, I. Kant sought to substantiate the systematic nature of the process of cognition itself) and in the natural sciences. Our compatriot E. Fedorov at the end of the XIX century. came to the conclusion that nature is systematic in the process of creation crystallography.

The principle of consistency in economics was also formulated by A. Smith, who concluded that the effect of the actions of people organized in a group is greater than the sum of single results.

Various areas of systematic research led to the conclusion that this is a property of nature and a property of human activity (Fig. 2.1).

Rice. 2.1. Consistency as a universal property of matter

Systems theory serves as a methodological basis for control theory. This is a relatively young science, the organizational formation of which took place in the second half of the 20th century. The Austrian scientist L. von Bertalanffy is considered to be the founder of systems theory. The first international symposium on systems was held in London in 1961. The first report was made by the outstanding English cyberneticist S. Veer, which can be considered evidence of the epistemological closeness of cybernetics and systems theory.

The central concept of systems theory is a system (from the Greek systema - "a whole made up of parts"). A system is an object of an arbitrary nature that has a pronounced system property that none of the parts of the system has in any way of its division, a property that is not derived from the properties of the parts.

The above definition of the system cannot be considered exhaustive - it reflects only a certain general approach to the study of objects. In the literature on system analysis, you can find many definitions of the system. (See: for example, Uyomov A.I. System approach and general theory of systems. - M., 1978. See also Appendix 5)

In this manual, we will use the following working definition of a system: "A system is an integral set of interrelated elements that has a certain structure and interacts with the environment in order to achieve a goal." Analyzing this definition, we can identify several basic concepts: integrity, totality, structuredness, interaction with the external environment, the presence of a goal, etc. They represent a system of concepts, i.e., the internal organization of some stable object, the integrity of which is the system. The very possibility of identifying stable objects in the field of study is determined by the property of the integrity of the system, the goals of the observer and his ability to perceive reality.

Let's consider some basic terms and concepts widely used in system research.

^ State of the system - an ordered set of essential properties that it possesses at a certain point in time.
Properties of the system- a set of parameters that determine the behavior of the system.
Behavior systems - the actual or potential operation of the system.
Action- an event occurring with the system, caused by another event.
Event- change at least one property of the system.

Concepts characterizing the structure of systems

Under element It is customary to understand the simplest indivisible part of the system. The concept of indivisibility is associated with the goal of considering an object as a system. Thus, an element is the limit of system division from the point of view of solving a specific problem.

The system can be divided into elements not immediately, but by successive division into subsystems, larger than the elements, but smaller than the system as a whole. The possibility of dividing the system into subsystems is associated with the isolation of a set of elements capable of performing relatively independent functions aimed at achieving the overall goal of the system. For a subsystem, a subgoal should be formulated, which is its system-forming factor.

If the task is not only to isolate the system from the environment and study its behavior, but also to understand its internal structure, it is necessary to study structure systems. The term "structure" comes from the Latin structura - “structure”, “location”, “order”. The structure of the system includes its elements, the links between them and the attributes of these links. In most cases, the concept of "structure" is usually associated with a graphical display, but this is not necessary. The structure can be represented in the form of set-theoretic descriptions, matrices, graphs, etc.

Connection - a concept expressing necessary and sufficient relations between elements. The connection attributes are:

orientation;
force;
character.

By focus links are divided into directed and wronglazy. Directed links, in turn, are divided into straight and aboutmilitary.

By strength of manifestation connections are divided into weak and strong.

By character links are divided into ties of subordination and communications onbirth. The former can be divided into linear and functional; the latter characterize the cause-and-effect relationship.

Relationships between elements are characterized by a certain order, internal properties, and focus on the functioning of the system. Such features of the system are called its organization.

Structural bonds are relatively independent of the elements and can act as an invariant in the transition from one system to another. This means that the regularities revealed in the study of systems representing objects of one nature can be used in the study of systems of another nature. Communication can also be represented and considered as a system that has its own elements and connections.

The concept of "structure" in the narrow sense of the word can be identified with the concept of "system-forming relations", i.e. structure can be considered as a system-forming factor,

In the broad sense of the word, structure is understood as the totality of relations between elements, and not just system-forming relations.

The method of isolating system-forming relations from the environment depends on whether we are talking about designing a system that does not yet exist or about analyzing a systemic representation of a known object, material or ideal. There are different types of structures. The most famous of them are shown in Fig. 2.2.
Rice. 2.2. Types of structures

System classification

Consider first some types of systems. abstract systems are systems all of whose elements are concepts

Specific systems are systems whose elements are physical objects. They are divided into natural(arising and existing without human intervention) and artificial(man-made).

open systems - exchanging matter, energy and information with the external environment.

^ Closed systems are systems that have no exchange with the external environment.

In its pure form, open and closed systems do not exist.

Dynamic systems occupy one of the central places in the general theory of systems. Such a system is a structured object that has inputs and outputs, an object into which, at certain moments, you can enter and from which you can output matter, energy, information. Dynamic systems are presented as systems in which processes proceed continuously in time, and as systems in which all processes occur only at discrete moments of time. Such systems are called discrete dynamical systems. Moreover, in both cases it is assumed that the behavior of the system can be analyzed in a certain period of time, which is directly defined by the term "dynamic".

^ Adaptive Systems - systems operating under conditions of initial uncertainty and changing external conditions. The concept of adaptation was formed in physiology, where it is defined as a set of reactions that ensure the adaptation of the body to changes in internal and external conditions. In the theory of adaptation management, they call the process of accumulation and use of information in a system aimed at achieving an optimal state with initial immediacy and changing external conditions.

^ Hierarchical systems - systems, the elements of which are grouped by levels, vertically correlated with one another; in this case, the elements of the levels have branching outputs. Although the concept of "hierarchy" was constantly present in scientific and everyday life, a detailed theoretical study of hierarchical systems began recently. Considering hierarchical systems, let us turn to the principle of opposition. The object of opposition will be systems with a linear structure (radial, centralized). For systems with centralized control, the unambiguity of control actions is characteristic. Unlike them, there are hierarchical systems, systems of an arbitrary nature (technical, biological, social, and others), which have a multi-level and branched structure in functional, organizational or other terms. Hierarchical systems are the subject of special attention in the theory and practice of management due to their universal nature and a number of advantages compared to, for example, linear structures. Among these advantages: freedom of local influences, no need to pass very large information flows through one control point, increased reliability. In addition, if one element of the centralized system fails, the entire system will also fail; if one element of the hierarchical system fails, the probability of failure of the entire system is negligible. All hierarchical systems have a number of characteristics:

sequential vertical arrangement of levels that make up the system (subsystem);
priority of actions of top-level subsystems (the right to intervene);
the dependence of the actions of the upper-level subsystem on the actual performance by the lower levels of their functions;
relative independence of subsystems, which makes it possible to combine centralized and decentralized management of a complex system.

Considering the conditionality of any classification, it should be noted that attempts at classification should in themselves have the properties of consistency, so classification can be considered a kind of modeling.

Let us consider some types of classification of systems according to various criteria.

Classification of systems by origin (Fig. 2.3).

Classification of systems according to the description of variables (Fig. 2.4).
Classification of systems according to the method of control (Fig. 2.5).
Classification of systems according to the type of their operators (Fig. 2.6).

There are many other ways to classify, for example, according to the degree of resource provision of management, including energy, material, information resources.

In addition to the considered classifications of systems, they can be divided into simple and complex, deterministic and probabilistic, linear and non-linear, etc.

System properties

Analysis of the working definition of the system allows us to highlight some of its general properties:

any system is a complex of interrelated elements;
the system forms a special unity with the external environment;
any system is an element of a system of a higher order;
the elements that make up the system, in turn, act as systems of a lower order.

These properties can be analyzed using Fig. 2.7 (A - system; B and D - elements of system A; C - element of system B).

Element B, which serves as an element of system A, in turn, is a lower-level system that consists of its own elements, including, for example, element C. And if we consider element B as a system interacting with the external environment, then the latter in In this case, it will represent system B (an element of system A). Therefore, the feature of the unity of the system with the external environment can be interpreted as the interaction of elements of the system of a higher order. Similar reasoning can be carried out for any element of any system.

The study of the properties of the system involves, first of all, the study of the relationship of parts and the whole. This means that:

1) the whole is primary, and the parts are secondary;

2) system-forming factors are the conditions for the interconnection of parts within one system;

3) parts of the system form an inseparable whole, so the impact on any of them affects the entire system;

4) each part of the system has its own purpose in terms of the goal towards which the activity of the whole is directed;

5) the nature of the parts and their functions are determined by the position of the parts as a whole, and their behavior is regulated by the relationship of the whole and its parts;

6) the whole behaves like a single entity, regardless of the degree of complexity.

From the whole variety of properties of systems for the study of organizational processes, it is advisable first of all to single out such properties as emergence, equifinality and homeostasis.

emergence is one of the most essential properties of systems. This is the irreducibility of the properties of the system to the properties of its elements; in other words, emergence is the presence of new qualities of the whole that are absent from its constituent parts. Thus, the properties of the whole are not a simple sum of the properties of its constituent elements, although they depend on them. At the same time, the elements integrated into the system may lose the properties inherent in them outside the system, or acquire new ones.

equifinality- one of the least studied properties of the system, characterizing the limiting capabilities of systems of a certain class of complexity. L. von Bertalanffy, who proposed this term, defined equifinality in relation to an open system as the ability of a system (in contrast to the equilibrium states in closed systems, completely determined by the initial conditions) to achieve a state independent of time and initial conditions, which is determined solely by the parameters of the system. The need to introduce this concept arises starting from a certain level of system complexity. equifinality- the internal predisposition of the system to achieve a certain limiting state, independent of external conditions. Idea equifinality is to study the parameters that determine a certain limiting level of organization.

The organization, being a holistic entity, always strives to reproduce itself, restore the lost balance, overcome resistance, in particular the external environment. This property of an organization is called homeostasis.

Iskander Khabibrakhmanov wrote material for the Game Market column on systems theory, principles of behavior in systems, interconnections and examples of self-organization.

We live in a complex world and do not always understand what is happening around. We see people who become successful without deserving it and those who really deserve success, but remain in obscurity. We are not sure about tomorrow, we are closing more and more.

To explain things we don't understand, we invented shamans and fortune-tellers, legends and myths, universities, schools and online courses, but it didn't seem to help. When we were in school, we were shown the picture below and asked what would happen if we pulled a string.

Over time, most of us have learned to give the correct answer to this question. However, then we went out into the open world, and our tasks began to look like this:

This led to frustration and apathy. We have become like the wise men in the parable of the elephant, each of whom sees only a small part of the picture and cannot draw a correct conclusion about the object. Each of us has our own misunderstanding of the world, it is difficult for us to communicate with each other, and this makes us even more lonely.

The fact is that we live in the age of a double paradigm shift. On the one hand, we are moving away from the mechanistic paradigm of society inherited from the industrial age. We understand that inputs, outputs and capacities do not explain the diversity of the world around us, and often it is much more influenced by the socio-cultural aspects of society.

On the other hand, a huge amount of information and globalization lead to the fact that instead of an analytical analysis of independent quantities, we must study interdependent objects, indivisible into separate components.

It seems that our survival depends on the ability to work with these paradigms, and for this we need a tool, just as we once needed tools for hunting and tilling the land.

One such tool is systems theory. Below there will be examples from systems theory and its general provisions, there will be more questions than answers and, hopefully, there will be some inspiration to learn more about it.

Systems theory

Systems theory is a fairly young science at the junction of a large number of fundamental and applied sciences. This is a kind of biology from mathematics, which deals with the description and explanation of the behavior of certain systems and the commonality between this behavior.

There are many definitions of the concept of a system, here is one of them. System - a set of elements that are in relationships, which forms a certain integrity of structure, function and processes.

Depending on the objectives of the research, the systems are classified:

by the presence of interaction with the outside world - open and closed;
by the number of elements and the complexity of the interaction between them - simple and complex;
if possible, observations of the entire system - small and large;
by the presence of an element of randomness - deterministic and non-deterministic;
by the presence of goals in the system - casual and purposeful;
according to the level of organization - diffuse (random walks), organized (the presence of a structure) and adaptive (the structure adapts to external changes).

Also, systems have special states, the study of which gives an understanding of the behavior of the system.

sustainable focus. With small deviations, the system returns to its original state again. An example is a pendulum.
Unstable focus. A small deviation brings the system out of equilibrium. An example is a cone placed with a point on a table.
Cycle. Some states of the system are cyclically repeated. An example is the history of different countries.
Complex behavior. The behavior of the system has a structure, but it is so complex that it is not possible to predict the future state of the system. An example is stock prices on the stock exchange.
Chaos. The system is completely chaotic, there is no structure in its behavior.

Often when working with systems, we want to make them better. Therefore, we need to ask ourselves the question in what special state we want to bring it. Ideally, if the new state of interest to us is a stable focus, then we can be sure that if we achieve success, then it will not disappear the next day.

Complex systems

We are increasingly seeing complex systems around us. Here I did not find sounding terms in Russian, so I have to speak in English. There are two fundamentally different concepts of complexity.

The first (complicatedness) - means some complexity of the device, which is applied to fancy mechanisms. This kind of complexity often makes the system unstable to the slightest changes in the environment. So, if one of the machines stops at the plant, it can disable the entire process.

The second (complexity) - means the complexity of behavior, for example, biological and economic systems (or their emulations). On the contrary, this behavior persists even with some changes in the environment or the state of the system itself. So, when a major player leaves the market, the players will share his share less among themselves, and the situation will stabilize.

Often complex systems have properties that can lead the uninitiated into apathy, and make working with them difficult and intuitive. These properties are:

simple rules for complex behavior,
butterfly effect or deterministic chaos,
emergence.

Simple rules for complex behavior

We are used to the fact that if something exhibits complex behavior, then it is most likely complex internally. Therefore, we see patterns in random events and try to explain things that are incomprehensible to us by the machinations of evil forces.

However, this is not always the case. A classic example of a simple internal structure and complex external behavior is the game "Life". It consists of a few simple rules:

the universe is a checkered plane, there is an initial arrangement of living cells.
at the next moment of time, a living cell lives if it has two or three neighbors;
otherwise it dies of loneliness or overpopulation;
in an empty cell, next to which there are exactly three living cells, life is born.

In general, writing a program that will implement these rules will require five to six lines of code.

At the same time, this system can produce quite complex and beautiful patterns of behavior, so without seeing the rules themselves it is difficult to guess them. And it's certainly hard to believe that this is implemented in a few lines of code. Perhaps the real world is also built on a few simple laws that we have not yet deduced, and the entire boundless variety is generated by this set of axioms.

Butterfly Effect

In 1814, Pierre-Simon Laplace proposed a thought experiment, which consisted in the existence of an intelligent being capable of perceiving the position and speed of every particle of the universe and knowing all the laws of the world. The question was the theoretical ability of such a being to predict the future of the universe.

This experiment caused a lot of controversy in scientific circles. Scientists, inspired by progress in computational mathematics, tended to answer yes to this question.

Yes, we know that the principle of quantum uncertainty excludes the existence of such a demon even in theory, and predicting the position of all particles in the world is fundamentally impossible. But is it possible in simpler deterministic systems?

Indeed, if we know the state of the system and the rules by which they change, what prevents us from calculating the next state? Our only problem might be a limited amount of memory (we can store numbers with limited precision), but all calculations in the world work this way, so this should not be a problem.

Not really.

In 1960, Edward Lorenz created a simplified weather model, consisting of several parameters (temperature, wind speed, pressure) and the laws by which the state at the next time is obtained from the current state, representing a set of differential equations.

dt = 0.001

x0 = 3.051522

y0 = 1.582542

z0 = 15.623880

xn+1 = xn + a(-xn + yn)dt

yn+1 = yn + (bxn - yn - znxn)dt

zn+1 = zn + (-czn + xnyn)dt

He calculated the values of the parameters, displayed them on the monitor and built graphs. It turned out something like this (graph for one variable):

After that, Lorentz decided to rebuild the graph, taking some intermediate point. It is logical that the graph would have turned out exactly the same, since the initial state and the transition rules have not changed in any way. However, when he did, something unexpected happened. In the graph below, the blue line represents the new set of parameters.

That is, at first, both graphs go very close, there are almost no differences, but then the new trajectory moves further and further away from the old one, starting to behave differently.

As it turned out, the reason for the paradox lay in the fact that in the computer's memory all data was stored with an accuracy of up to the sixth decimal place, and was displayed with an accuracy of up to the third. That is, a microscopic change in the parameter led to a huge difference in the trajectories of the system.

It was the first deterministic system to have this property. Edward Lorenz gave it the name The Butterfly Effect.

This example shows us that sometimes events that seem unimportant to us end up having a huge impact on outcomes. The behavior of such systems is impossible to predict, but they are not chaotic in the truest sense of the word, because they are deterministic.

Moreover, the trajectories of this system have a structure. In three-dimensional space, the set of all trajectories looks like this:

What is symbolic, it looks like a butterfly.

emergence

Thomas Schelling, an American economist, examined maps of the distribution of racial classes in various American cities, and observed the following pattern:

This is a map of Chicago, and here the places where people of different nationalities live are shown in different colors. That is, in Chicago, as in other cities in America, there is a fairly strong racial segregation.

What conclusions can we draw from this? The first thing that comes to mind is: people are intolerant, people do not accept and do not want to live with people who are different from them. But is it?

Thomas Schelling proposed the following model. Imagine a city in the form of a checkered square, people of two colors (red and blue) live in the cells.

Then almost every person from this city has 8 neighbors. It looks something like this:

Moreover, if a person has less than 25% of neighbors of the same color, then he randomly moves to another cell. And so it continues until each resident is satisfied with his situation. The inhabitants of this city cannot be called intolerant at all, because they only need 25% of people like them. In our world, they would be called saints, a real example of tolerance.

However, if we start the process of moving, then from the random location of the inhabitants above, we will get the following picture:

That is, we get a racially segregated city. If, instead of 25%, each resident wants at least half of the neighbors like him, then we will get almost complete segregation.

At the same time, this model does not take into account such things as the presence of local temples, shops with national utensils, and so on, which also increase segregation.

We are accustomed to explaining the properties of a system by the properties of its elements and vice versa. However, for complex systems, this often leads us to incorrect conclusions, because, as we have seen, the behavior of the system at the micro and macro levels can be opposite. Therefore, often going down to the micro level, we try to do the best, but it turns out as always.

This property of a system, when the whole cannot be explained by the sum of its elements, is called emergence.

Self-organization and adaptive systems

Perhaps the most interesting subclass of complex systems are adaptive systems, or systems capable of self-organization.

Self-organization means that the system changes its behavior and state, depending on changes in the external world, it adapts to changes, constantly transforming itself. Such systems everywhere, almost any socio-economic or biological, just like the community of any product, are examples of adaptive systems.

Here is a video of the puppies.

At first, the system is in chaos, but when an external stimulus is added, it becomes more orderly and quite nice behavior appears.

Ant Swarm Behavior

The foraging behavior of an ant swarm is a perfect example of an adaptive system built around simple rules. When looking for food, each ant wanders randomly until it finds food. Having found food, the insect returns home, marking the path it has traveled with pheromones.

At the same time, the probability of choosing a direction when wandering is proportional to the amount of pheromone (smell strength) on this path, and over time, the pheromone evaporates.

The efficiency of the ant swarm is so high that a similar algorithm is used to find the optimal path in graphs in real time.

At the same time, the behavior of the system is described by simple rules, each of which is critical. So the randomness of the wander allows finding new food sources, and the evaporability of the pheromone and the attractiveness of the path, proportional to the strength of the smell, allows you to optimize the length of the route (on a short path, the pheromone will evaporate more slowly, since new ants will add their pheromone).

Adaptive behavior is always somewhere between chaos and order. If there is too much chaos, then the system reacts to any, even insignificant, change and cannot adapt. If there is too little chaos, then stagnation is observed in the behavior of the system.

I have seen this phenomenon in many teams, where having clear job descriptions and tightly regulated processes made the team toothless, and any outside noise unsettled them. On the other hand, the lack of processes led to the fact that the team acted unconsciously, did not accumulate knowledge, and therefore all its unsynchronized efforts did not lead to a result. Therefore, the construction of such a system, and this is the task of most professionals in any dynamic field, is a kind of art.

In order for the system to be capable of adaptive behavior, it is necessary (but not sufficient):

openness. A closed system cannot adapt by definition because it knows nothing about the outside world.
Presence of positive and negative feedbacks. Negative feedbacks keep the system in a favorable state as they reduce the response to outside noise. However, adaptation is also impossible without positive feedbacks that help the system move to a new, better state. When it comes to organizations, processes are responsible for negative feedbacks, while new projects are responsible for positive feedbacks.
Variety of elements and relationships between them. Empirically, increasing the variety of elements and the number of connections increases the amount of chaos in the system, so any adaptive system must have the necessary amount of both. Diversity also allows for a smoother response to change.

Finally, I would like to give an example of a model that emphasizes the need for a variety of elements.

It is very important for a bee colony to maintain a constant temperature in the hive. Moreover, if the temperature of the hive falls below the desired for a given bee, she begins to flap her wings to warm the hive. Bees have no coordination and the desired temperature is built into the bee's DNA.

If all the bees have the same desired temperature, then when it drops below, all the bees will begin to flap their wings at the same time, quickly warm the hive, and then it will also quickly cool down. The temperature graph will look like this:

And here is another graph where the desired temperature for each bee is randomly generated.

The temperature of the hive is kept at a constant level, because the bees are connected to the heating of the hive in turn, starting from the most "freezing".

That's all, finally, I want to repeat some of the ideas that were discussed above:

Sometimes things are not quite what they seem.
Negative feedback helps you stay put, positive feedback helps you move forward.
Sometimes, to make it better you need to add chaos.
Sometimes simple rules are enough for complex behavior.
Appreciate variety, even if you're not a bee.

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A systematic understanding of the relationship of various theories. General systems theory and other systems sciences

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Portal for the student. Self-training

Send your good work in the knowledge base is simple. Use the form below

Similar Documents

background

Immediate predecessors and parallel projects

Activities of L. von Bertalanffy and the International Society for the General Systems Sciences

General Systems Theory and World War II

The post-war stage in the development of systems theory

Synergetics in the context of systems theory

System-wide principles and laws

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Books in Russian

Textbooks in Russian

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