The principle of consistency, the advancement of which was prepared by the history of natural science and philosophy, finds in the XX century more and more supporters in various fields of knowledge. In the 30-40s, the Austrian scientist L. von Bertalanffy successfully applied a systematic approach to the study of biological processes and after World War II he proposed the concept of developing a general systems theory.
In the program for constructing a general theory of systems, Bertalanffy indicated that its main tasks are:
1) identification of general principles and laws of behavior of systems, regardless of the nature of their constituent elements and the relationship between them;
2) the establishment, as a result of a systematic approach to biological and social objects, of laws similar to the laws of natural science;
3) the creation of a synthesis of modern scientific knowledge based on the identification of the isomorphism of the laws of various fields of activity.
There are a number of systemic principles that are important to understand the concept of a system:
· Dominance of the role of the whole over the particular, the complex over the simple.
· The whole is greater than the sum of its parts.
· The system has a structure with a certain arrangement and connection of its constituent parts.
· The system has a hierarchical structure.
· The system has many states corresponding to its various properties, which are described by a set of parameters.
· The structure of the system is the most conservative characteristic of the system, in contrast to the state of the system.
· The properties of the system as a whole are determined not only by the properties of its individual elements, but also by the properties of the structure of the system as a whole.
· The system stands out from the environment by its qualities. Systems are open and closed.
· Each system has parameters that are basic or vital for it. The existence of the system depends on them.
· The homeostasis of the system preserves vital parameters in the process of adaptation of the system to external conditions and thereby maintains the existence of the system itself.
General systems theory, as conceived by Bertalanffy, who proposed the first program for constructing such a theory, should be some general science of systems of all types ... However, the specific implementation of this and similar ambitious programs ran into very serious difficulties, the main one of which is that the generality of the concept of a system leads to the loss of specific content.
At present, several mathematical models of systems have been built using the apparatus of set theory and algebra. However, the applied achievements of these theories are still very modest. At the same time, systems thinking is increasingly used by representatives of almost all sciences (geography, political science, psychology, etc.). The systems approach is becoming more and more widespread in the analysis of processes.
Iskander Khabibrakhmanov wrote for the "Games Market" heading material about the theory of systems, the principles of behavior in them, interrelationships and examples of self-organization.
We live in a complex world and do not always understand what is happening around us. We see people who become successful without deserving it and those who are really worthy of success, but remain in obscurity. We are not sure about the future, we are closing more and more.
To explain things we didn't understand, we came up with 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 the 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 from 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 point is, we are living in an 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 powers do not explain all the diversity of the world around us, and it is often much more strongly influenced by the sociocultural aspects of society.
On the other hand, a huge amount of information and globalization lead to the fact that instead of 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, as we once needed tools for hunting and cultivating the land.
One of these tools is systems theory. Below there will be examples from systems theory and its general principles, there will be more questions than answers and hopefully there will be a little inspiration to learn more about this.
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 common between this behavior.
There are many definitions of the concept of a system, here is one of them. System - a set of elements in a relationship, 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;
- as far as possible to observe the entire system completely - small and large;
- by the presence of an element of randomness - deterministic and non-deterministic;
- by the presence of a goal 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 adjusts to changes outside).
Also, systems have special states, the study of which gives an understanding of the behavior of the system.
- Steady focus. With small deviations, the system returns to its original state. An example is a pendulum.
- Unstable focus. A slight deviation throws the system out of balance. An example is a cone placed with a point on a table.
- Cycle. Some system states are repeated cyclically. 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 a 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 into what special state we want to bring her. Ideally, if the new state of interest to us is a stable focus, then we can rest assured that if we succeed, it will not disappear the next day.
Complex systems
We increasingly meet complex systems around us. Here I did not find any sounding terms in Russian, so I have to speak 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). This behavior, on the contrary, 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 less share his share among themselves, and the situation will stabilize.
Often, complex systems have properties that can plunge the uninitiated into apathy, and make working with them difficult and intuitively incomprehensible. These properties are:
- simple rules of 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 incomprehensible to us by the intrigues 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 the initial arrangement of living cells.
- at the next moment in time, a living cell lives if it has two or three neighbors;
- otherwise she dies of loneliness or overpopulation;
- in an empty cell, next to which there are exactly three living cells, life is born.
In general, it will take five to six lines of code to write a program that will implement these rules.
At the same time, this system can produce rather complex and beautiful patterns of behavior, so it is difficult to guess them without seeing the rules themselves. And it's certainly hard to believe that this is implemented with a few lines of code. Perhaps the real world is also built on a few simple laws that we have not yet deduced, and all infinite diversity is generated by this set of axioms.
Butterfly Effect
In 1814, Pierre-Simon Laplace proposed a thought experiment, which consists in the existence of an intelligent creature 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 creature to predict the future of the universe.
This experiment has caused a lot of controversy in the scientific community. Scientists, inspired by advances in computational mathematics, tended to answer this question in the affirmative.
Yes, we know that the principle of quantum uncertainty excludes the existence of such a demon even in theory, and it is fundamentally impossible to predict the position of all particles in the world. 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 may be the limited amount of memory (we can store numbers with limited precision), but all the calculations in the world work like that, so it shouldn't be a problem.
Not really.
In 1960, Edward Lorenz created a simplified weather model consisting of several parameters (temperature, wind speed, pressure) and laws by which the state at the next moment in time is obtained from the current state, representing a set of differential equations.
dt = 0.001
x0 = 3.051522
y0 = 1.582542
z 0 = 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, Lorenz decided to rebuild the graph by taking some intermediate point. It is logical that the graph would turn out to be exactly the same, since the initial state and transition rules have not changed in any way. However, when he did this, something unexpected happened. In the chart below, the blue line is responsible for 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 from the old one, starting to behave differently.
As it turned out, the reason for the paradox lay in the fact that all data in the computer's memory were stored with an accuracy of up to six decimal places, and were 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 we think are unimportant end up having a huge impact on outcomes. The behavior of such systems cannot be predicted, 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, looked at maps of the distribution of racial classes in various cities in America, and observed the following picture:
This is a map of Chicago and different colors of the area where people of different nationalities live. 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 to come to mind: 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. Let's 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 this continues until every resident is not satisfied with his position. The inhabitants of this city cannot be called intolerant at all, because they only need 25% of people who are just like them. In our world, they would be called saints, a true example of tolerance.
However, if we start the process of moving, then from the random location of the residents above, we get the following picture:
That is, we get a racially segregated city. If, instead of 25%, each inhabitant wants at least half of the neighbors who are the same as 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 used 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 wrong conclusions, because, as we have seen, the behavior of a system at the micro and macro levels can be opposite. Therefore, often going down to the micro level, we try to do our best, but it turns out as always.
This property of the system, when the whole cannot be explained by the sum of the elements, is called emergence.
Self-organization and adaptive systems
Perhaps the most interesting subclass of complex systems is 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 changing. Such systems everywhere, practically any socio-economic or biological, just like the community of any product, are examples of adaptive systems.
And here is a video with puppies.
At first, the system is in chaos, but when you add an external stimulus, it becomes orderly and some pretty cute behavior appears.
Ant swarm behavior
The behavior of an ant swarm when searching for food is an excellent example of an adaptive system built on simple rules. When looking for food, each ant wanders randomly until it finds food. Having found food, the insect returns home, marking the traversed path with pheromones.
In this case, the probability of choosing a direction when wandering is proportional to the amount of pheromone (the strength of the smell) along the given 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 the graphs in real time.
In this case, the behavior of the system is described by simple rules, each of which is critically important. So the randomness of wandering allows you to find new sources of food, and the volatility 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 observed this phenomenon in many teams, when the presence of clear job descriptions and tightly regulated processes made the team toothless, and any outside noise knocked it out of a rut. On the other hand, the absence of processes led to the fact that the team acted unconsciously, did not accumulate knowledge, and therefore all of its unsynchronized efforts did not lead to a result. Therefore, the construction of such a system, and this is precisely 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, since it knows nothing about the outside world.
- The presence of positive and negative feedbacks... Negative feedback loops allow the system to remain in an advantageous state as they reduce response to external noise. However, adaptation is impossible without positive feedbacks that help the system move to a new better state. If we talk about organizations, then processes are responsible for negative feedback, while new projects are responsible for positive feedback.
- Variety of elements and connections between them... Empirically, an increase in 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 temperature for a given bee, it begins to flap its wings to warm the hive. Bees lack coordination and the desired temperature is embedded in the bee's DNA.
If all the bees have the same desired temperature, then when it drops below, all the bees will simultaneously flap their wings, 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 warming the hive in turn, starting with the most "freezing" ones.
That's all, in the end I would like to repeat some of the ideas that were discussed above:
- Sometimes things aren't quite what they seem.
- Negative feedback helps you stay in place, positive feedback helps you move forward.
- Sometimes, to do better, you need to add chaos.
- Sometimes simple rules are enough for complex behavior.
- Appreciate variety, even if you are not a bee.
General systems theory L. Bertalanffy
Irkutsk 2015
Introduction
General Provisions
General Systems Studies
Cybernetics
Fields of application of OTS according to Bertalanffy:
Conclusion
Bibliography
Introduction
The emergence of the systems approach gave scientists some hope that, finally, the "whole" from a diffuse and non-constructive form will take on a clear outline of an operational research principle.
The term "system" has a very ancient origin, and there is hardly any scientific direction that did not use it. Suffice it to recall the "circulatory system", "digestive system", etc., which are still accepted by some researchers as an expression of a systematic approach. For the most part, the term "system" is used when it is about something brought together, ordered, organized, but, as a rule, the criterion by which the components are assembled, ordered, organized is not mentioned.
Obviously, OTC is not the product of a handful of thinkers. Several scientific trends contributed to its emergence. Open systems concepts developed simultaneously in thermodynamics and biology in the 1930s. The concept of equifinality was introduced by Bertalanffy in 1940. The fundamental differences between inanimate and living nature were described by Brillouin in 1949. Examples of open systems in ecology, neuroscience and philosophy are given by Whittaker, Krech and Bentley in publications of the 50s.
A large role in the emergence of GPV as a science was played by scientific directions and concepts associated with the names of outstanding scientists:
Neumann had developed a general theory of automata by 1948 and laid the foundations for the theory of artificial intelligence.
Shannon's work on information theory (1948), in which the concept of the amount of information was given from the perspective of communication theory.
Cybernetics of Wiener (1948), with the help of which a connection was found between the concepts of entropy, disorder, amount of information and uncertainty. The particular importance of these concepts for the study of systems was emphasized.
Ashby by 1956 had developed the concepts of self-regulation and self-government, which are a further development of the ideas of Wiener and Shannon.
The notions brought to life in connection with the development of cybernetics and information theory lead to two partly contradictory consequences: first, they make it possible to approximate open systems by closed ones by introducing a feedback mechanism; secondly, they show the impossibility of artificial reproduction on a model of a number of features of the automatic regulation process in living systems.
Scientists following the first path have focused their efforts on the construction of models and theories of organizations, which are dominated by concepts borrowed from the analytical and mechanistic approaches. The beauty of these theories is their rigor. However, within the framework of these theories, many specific properties of living systems cannot be determined. The second path has proven important for the development of a behavioral theory of organizations, which combines the concepts of economics with behavioral concepts derived from psychology, sociology and anthropology. The latter better explain the phenomenon of behavior than analytic-mechanistic theories, but are inferior to them in rigor.
In order to emphasize the fact that general systems do not exist, and we are talking about the search for general theories, perhaps some other combination of these words would be more appropriate. Laszlo pointed out that this "semantic misunderstanding" originally arose from the translation from German of the early works of Bertalanffy. In the above-mentioned works, a "theory applicable in various fields of science" was built, and not "a theory of what is called general systems", as it was erroneously in the English version. Bertalanffy's seminal work was titled General System Theory in English only once.
The purpose of this work is to consider the general theory of systems by L. Bertalanffy.
Systems theory is an interdisciplinary field of science and the study of the nature of complex systems in nature<#"justify">general theory of the bertalanffy system
Preconditions for the emergence of interdisciplinary theory
The motives leading to the advancement of the idea of general systems theory can be summarized in the following several positions.
Until the 20th century, the field of science as an activity aimed at establishing an explanatory and predicative system of laws was practically identified with theoretical physics. Only a few attempts to create systems of laws in non-physical areas have received general recognition (for example, genetics). Nevertheless, the biological, behavioral and social sciences have found their own basis, and therefore it has become an urgent problem whether it is possible to extend scientific conceptual schemes to those areas and problems where the application of physics is insufficient or generally impracticable.
Classical science did not use concepts and did not solve problems that existed in biological or sociological fields. For example, in a living organism, there is organization, regulation, continuous dynamics and order, as in human behavior, but such questions went beyond the framework of classical science, based on the so-called mechanistic worldview; such questions were considered metaphysical.
The situation described was closely related to the structure of classical science. The latter dealt mainly with problems with two variables (linear causal series, one cause and one effect) or, at best, problems with several variables. Mechanics are a classic example of this. It provides an accurate solution to the problem of the attraction of two celestial bodies - the Sun and the planet, and thanks to this opens up the possibility of accurately predicting the future positions of stars and even the existence of planets that have not yet been discovered. Nevertheless, the problem of three bodies in mechanics is already insoluble in principle and can be analyzed only by the method of approximations. A similar situation takes place in the more modern field of physics - atomic physics. Here, too, the problem of two bodies, for example a proton and an electron, is quite solvable, but as soon as we touch on the problem of many bodies, difficulties arise again. Unidirectional causality, the relationship between cause and effect, two or a small number of variables - all these mechanisms operate in a wide area of scientific knowledge. However, many of the problems that arise in biology, in the behavioral and social sciences, in fact, are problems with many variables and require new conceptual means for their solution. Warren Weaver, one of the founders of information theory, expressed this point in an often quoted statement. Classical science, he argued, dealt with either linear causal series, that is, problems of two variables, or problems related to disorganized complexity. The latter can be resolved by statistical methods and ultimately follow from the second law of thermodynamics. In modern physics and biology, problems of organized complexity arise everywhere, that is, the interaction of a large, but not infinite number of variables, and they require new conceptual means for their solution.
The above is not a metaphysical or philosophical statement. We are not erecting a barrier between inorganic and living nature, which, obviously, would be unreasonable, if we bear in mind the various intermediate forms, such as viruses, nucleoproteins and self-reproducing elements in general, which in a certain way connect these two worlds. Likewise, we do not declare that biology, in principle, is "irreducible to physics," which would be unreasonable in view of the colossal advances in the physical and chemical explanation of life processes. Likewise, it is not our intention to establish a barrier between biology and the behavioral and social sciences. And yet this does not eliminate the fact that in these areas we “do not have suitable conceptual means for explanation and prediction, similar to those that exist in physics and in its various applications.
There seems to be an urgent need to extend the tools of science to areas that go beyond physics and have specific features of biological, behavioral and social phenomena. This means that new conceptual models have to be built. Each science is, in the broad sense of the word, a model, that is, a conceptual structure aimed at reflecting certain aspects of reality. One of these highly successful models is the physics system. But physics is only one model that deals with certain aspects of reality. It cannot be monopoly and does not coincide with reality itself, as mechanistic methodology and metaphysics assumed. It clearly does not cover all aspects of the world and presents, as specific problems in biology and the behavioral sciences show, some limited aspect of reality. It is probably possible 'to introduce other models dealing with phenomena beyond the competence of physics.
All of this reasoning is very abstract. Therefore, apparently, it is necessary to introduce some personal point, telling how the author of this work came to problems of this kind.
General Provisions
Initial ideas about systems theory originated from research in the field of sociology<#"center">General Systems Studies
Many early researchers in systems science tried to find a general systems theory that could describe and explain an arbitrary system from a scientific point of view. The term "general systems theory" goes back to the work of the same name by L. Bertalanffy, whose goal was to bring together everything that he discovered in his work as a biologist. His desire was to use the word "system" to describe principles that are common to all systems. In his book, he wrote:
"... there are models, principles and laws that are applicable to generalized systems or their subclasses, independent of their special kind, the nature of their components, the types of connections between them. It seems that it is possible to create a theory that would not study systems of any particular kind , but gave an understanding of the principles of systems in general. "
Erwin Laszlo, in his introduction to Bertalanffy's book Perspectives on General Systems Theory, wrote:
"Thus, when Bertalanffy speaks of the" Allgemeine Systemtheorie "(German.<#"center">Cybernetics
Cybernetics studies feedback loops<#"justify">Fields of application of OTS according to Bertalanffy:
· Cybernetics, based on the principle of feedback, or circular causal chains, and revealing the mechanisms of purposeful and self-controlled behavior. · Information theory, which introduces the concept of information as a certain quantity, measured by means of an expression isomorphic to negative entropy in physics, and develops the principles of information transfer. · Game theory that analyzes, within the framework of a special mathematical apparatus, the rational competition of two or more opposing forces in order to achieve maximum gain and minimum loss. · Decision theory, which analyzes, similarly to game theory, rational choices within human organizations, based on a consideration of a given situation and its possible outcomes. · Topology, or relational mathematics, which includes non-metric areas such as network theory and graph theory. · Factor analysis, that is, isolation procedures - through the use of mathematical analysis - of factors in multivariable phenomena in psychology and other scientific fields. · General systems theory in a narrow sense, trying to deduce from the general definition of the concept of "system" as a complex of interacting components, a number of concepts characteristic of organized wholes, such as interaction, sum, mechanization, centralization, competition, finality, etc., and applying them to specific phenomena. Since systems theory in a broad sense is by its nature a fundamental fundamental science, it has its own correlate in applied science, sometimes acting under the general name of systems science, or Systems Science. This scientific movement is closely related to modern automation. In general terms, the following areas should be distinguished in systems science: · Systems Engineering, that is, the scientific planning, design, evaluation and construction of man-machine systems. · Operations research, that is, the scientific management of existing systems of people, machines, materials, money, etc. · Engineering psychology (Human Engineering), that is, the analysis of the adaptation of systems and, above all, machine systems, in order to achieve maximum efficiency with a minimum of money and other costs. Although the disciplines just named have much in common, they use different conceptual means. Systems engineering, for example, uses cybernetics and information theory, as well as general systems theory. Operations research uses methods of linear programming and game theory. Engineering psychology, which analyzes the abilities, psychological limitations and variability of human beings, makes extensive use of the means of biomechanics, industrial psychology, the analysis of human factors, etc. it is important to keep in mind that the systems approach, as some new concept in modern science, has a parallel in technology. The systems approach in science of our time stands in the same relation to the so-called mechanistic point of view, in which systems engineering is to traditional physical technology. All of these theories have certain features in common. At first,they agree that it is necessary to somehow solve problems that are characteristic of the behavioral and biological sciences and are not related to ordinary physical theory. Secondly,these theories introduce new concepts and models in comparison with physics, for example, the generalized concept of a system, the concept of information, comparable in meaning with the concept of energy in physics. Thirdly,these theories, as indicated above, deal primarily with problems with many variables. Fourth,the models introduced by these theories are interdisciplinary in nature, and they go far beyond the existing division of science. Fifthand, perhaps most importantly, concepts such as integrity, organization, teleology, and the direction of movement or functioning, which in mechanistic science were entrenched as unscientific or metaphysical, have now received full citizenship rights and are regarded as extremely important means of scientific analysis. Currently, we have at our disposal conceptual and, in some cases, even material models that can reproduce the basic properties of life and behavior. Basic concepts of general systems theory
A system is a complex of interacting components. A system is a set of interconnected operating elements. And although the concept of a system is defined in different ways, it usually means that the system is a certain set of interrelated elements that form a stable unity and integrity, which has integral properties and laws. We can define a system as something whole, abstract or real, made up of interdependent parts. Systemcan be any object of animate and inanimate nature, society, a process or a set of processes, a scientific theory, etc., if elements are defined in them that form a unity (integrity) with their connections and interrelationships between them, which ultimately creates a set of properties, inherent only in this system and distinguishing it from other systems (property of emergence). System
(from the Greek SYSTEMA, meaning "whole, made up of parts") is a set of elements, connections and interactions between them and the external environment, forming a certain integrity, unity and purposefulness. Almost every object can be viewed as a system. System -
it is a set of material and non-material objects (elements, subsystems), united by any connections (informational, mechanical, etc.), designed to achieve a specific goal and achieve it in the best way. System
is defined as a category, i.e. its disclosure is carried out through the identification of the basic properties inherent in the system. To study the system, it is necessary to simplify it while retaining the basic properties, i.e. build a model of the system. An important means of characterizing the system are its properties
.
The main properties of the system are manifested through the integrity, interaction and interdependence of the processes of transformation of matter, energy and information, through its functionality, structure, connections, and the external environment. Property -this is the quality of the parameters of the object, i.e. external manifestations of the way by which knowledge about the object is obtained. Properties make it possible to describe system objects. Moreover, they can change as a result of the functioning of the system. Properties -these are external manifestations of the process by which knowledge about an object is obtained, and it is being observed. Properties provide the ability to describe the objects of the system quantitatively, expressing them in units that have a certain dimension. The properties of system objects can change as a result of its action. The following main properties of the system are distinguished: Any system has a purpose and limitations .
The goal of the system can be described by the goal function F (x, y, t), where U1 is the extreme value of one of the performance indicators of the system. The behavior of the system can be described by the law Y = F (x), which reflects changes in the input and output of the system. This determines the state of the system. The state of the system is an instant photograph, or a cut of the system, a stop in its development. It is determined either through input interactions or output signals (results), or through macro parameters, macro properties of the system. It is a set of states of its n elements and connections between them. The assignment of a specific system is reduced to the assignment of its states, starting with inception and ending with death or transition to another system. A real system cannot be in any state. Restrictions are imposed on her condition - some internal and external factors (for example, a person cannot live for 1000 years). Possible states of a real system form in the state space of the system a certain subdomain ZSD (subspace) - a set of admissible states of the system. Equilibrium is the ability of a system in the absence of external disturbing influences or under constant influences to maintain its state for an arbitrarily long time. Stability is the ability of a system to return to a state of equilibrium after it has been brought out of this state under the influence of external or internal disturbing influences. This ability is inherent in systems when the deviation does not exceed a certain set limit. The structure of a system is a set of elements of a system and connections between them in the form of a set. System structuremeans the structure, location, order and reflects certain relationships, the interposition of the components of the system, i.e. its structure and does not take into account the set of properties (states) of its elements. The system can be represented by a simple enumeration of elements, however, most often, when examining an object, such a representation is not enough, since it is required to find out what the object is and what ensures the fulfillment of the set goals. External environment System element concept .
A-priory elementis an integral part of a complex whole. In our concept, a complex whole is a system that is an integral complex of interconnected elements. Element -
a part of a system that is independent in relation to the entire system and is indivisible with a given method of allocating parts. The indivisibility of an element is considered as the inexpediency of accounting within the model of a given system of its internal structure. The element itself is characterized only by its external manifestations in the form of connections and relationships with other elements and the external environment. Communication concept .
Connection- a set of dependencies of the properties of one element on the properties of other elements of the system. To establish a connection between two elements means to reveal the presence of dependencies of their properties. The dependence of the properties of elements can be one-way and two-way. Relationships- a set of two-way dependences of the properties of one element on the properties of other elements of the system. Interaction- a set of interconnections and interrelationships between the properties of elements, when they acquire the nature of interaction with each other. The concept of the external environment .
The system exists among other material or non-material objects that have not entered the system and are united by the concept of "external environment" - objects of the external environment. The input characterizes the impact of the external environment on the system, the output characterizes the impact of the system on the external environment. In essence, the delineation or identification of a system is the division of a certain area of the material world into two parts, one of which is considered as a system - an object of analysis (synthesis), and the other - as an external environment. The external environment is a set of objects (systems) existing in space and time, which are supposed to have an effect on the system. The external environment is a set of natural and artificial systems for which this system is not a functional subsystem. Conclusion
"A system is a set of interacting elements," von Bertalanffy said, emphasizing that a system is a structure in which the elements somehow act on each other (interact). Is this definition sufficient to distinguish a system from a non-system? Obviously not, because in any structure, passively or actively, its elements somehow act on each other (press, push, attract, induce, heat, act on the nerves, get nervous, deceive, absorb, etc.). Any set of elements always acts in one way or another, and it is impossible to find an object that would not perform any action. However, these actions can be random, without a goal, although accidental, but not predictable, they can contribute to the achievement of any goal. For example, a fork launched by a naughty grandson can get into the grandmother's eye and rip off the old eyesore, but in such a way that the eye itself is not damaged and its vision is restored (the case described in the novel is theoretically possible). In this case, although a beneficial effect was obtained, the fork in combination with the grandson is not a system for removing the thorn, and this strange incident was accidental and not predictable. Thus, although the sign of action is the main one, it does not define the concept of a system, but one of the necessary conditions of this concept. "A system is a complex of selectively involved elements that mutually contribute to the achievement of a given useful result, which is accepted by the main systemic factor," Anokhin said at the time. Obviously, this definition is closer than the rest to the correct understanding, because the concept "What can a given object do?" the concept of a goal is embedded. You can only contribute to the achievement of a certain goal, and a given useful result can only be a goal. It remains only to find out who or what determines the usefulness of the result. In other words, who or what sets the goal for the system? OTS should provide answers to all conceivable questions about the existence of our World and, perhaps, someday the answers to all these questions will be found, but not today. In this work, only an attempt was made to answer a very small number of these very complex and controversial questions, and it was not the author's task to find all the answers. System analysis greatly facilitates our understanding of the processes that occur in the world. But most importantly, systems analysis transforms science from experimental to analytical. The difference between them is huge and fundamental. Empiricism gives us facts, but does not explain them in any way. Analysis combined with empiricism can give us facts, explanations and predictions. The practical benefits of this are enormous. The world is one and knowledge about it should be linked to one another. The general theory of systems is "general" for that, because it affects all aspects of our life, and connects them into a single whole. Bibliography
1. General theory of systems - a critical review, Bertalanfi [Electronic resource] / On the principles of systems research, V.A. Lektorsky, V.N. Sadovsky [Electronic resource] / http://vphil.ru. Systems theory [Electronic resource] / http://traditio.ru General systems theory (systems and systems analysis), Mark Gaides Aronovich [Electronic resource] / http://www.medlinks.ru
An Austrian biologist living in Canada and the United States, Ludwig von Bertalanffy, in 1937 first put forward a number of ideas, which he later combined into one concept. He called it General Systems Theory. What is it? It is a scientific concept for the study of various objects considered as a system.
The main idea of the proposed theory was that the laws governing system objects are the same, the same for different systems. In fairness, it must be said that the basic ideas of L. Bertalanffy were laid down by various scientists, including the Russian philosopher, writer, politician, doctor, in his fundamental work "Tectology", written by him in 1912. A.A. Bogdanov actively participated in the revolution, however, in many respects he did not agree with V.I. Lenin. did not accept, but, nevertheless, continued to cooperate with the Bolsheviks, organizing the first blood transfusion Institute in Russia at that time and putting on a medical experiment. He died in 1928. Few people know today that at the beginning of the twentieth century the Russian scientist-physiologist V.M. Bekhterev, regardless of A.A. Bogdanov, described more than 20 universal laws in the field of psychological and social processes.
General systems theory studies various types, structure of systems, processes of their functioning and development, organization of components of structural-hierarchical levels, and much more. L. Bertalanffy also investigated the so-called open systems that exchange free energy, matter and information with the environment.
General systems theory currently explores such system-wide patterns and principles as, for example, the hypothesis of semiotic feedback, organizational continuity, compatibility, complementary relationships, the law of necessary diversity, hierarchical compensations, the principle of monocentrism, the principle of least relative resistance, the principle of external complement, the theorem on recursive structures, the law of divergence and others.
The modern state of the systems sciences owes much to L. Bertalanffy. General systems theory is in many respects similar in goals or research methods to cybernetics - the science of the general laws of the process of control and transfer of information in different systems (mechanical, biological or social); information theory - a branch of mathematics that defines the concept of information, its laws and properties; game theory, which analyzes with the help of mathematics the competition of two or more opposing forces in order to obtain the greatest gain and the smallest loss; decision theory, which analyzes rational choices among various alternatives; factor analysis, using the procedure for identifying factors in phenomena with many variables.
Today the general theory of systems receives a powerful impetus for its development in synergetics. I. Prigogine and G. Haken investigate nonequilibrium systems, dissipative structures and entropy in open systems. In addition, from L. Bertalanffy's theory such applied scientific disciplines as systems engineering - the science of systems planning, design, evaluation and construction of systems of the "man-machine" type; engineering psychology; field behavior theory operations research - the science of managing the components of economic systems (people, machines, materials, finance, and others); SMD-methodology, which was developed by G.P. Shchedrovitsky, his staff and students; V. Merlin's theory of integral individuality, which was largely based on the general theory of Bertalanffy systems considered above.
GENERAL SYSTEM THEORY – with special-scientific and logical-methodological concept of research of objects that are systems ... General systems theory is closely related to systematic approach and is a concretization and logical and methodological expression of its principles and methods. The first version of general systems theory was put forward L. von Bertalanffy , however, it had many predecessors (in particular, A.A. Bogdanov ). General systems theory arose in Bertalanffy in line with the "organismic" worldview he defended as a generalization of the one he developed in the 1930s. "Theory of open systems", in which living organisms were considered as systems constantly exchanging matter and energy with the environment. According to Bertalanffy's idea, the general theory of systems was supposed to reflect the significant changes in the conceptual picture of the world that the 20th century brought. Modern science is characterized by: 1) its subject - organization; 2) to analyze this subject, it is necessary to find means of solving problems with many variables (classical science knew problems with only two, at best with several variables); 3) the place of mechanism is taken by the understanding of the world as a multitude of heterogeneous and irreducible spheres of reality, the connection between which is manifested in the isomorphism of the laws operating in them; 4) the concept of physicalist reductionism, which reduces all knowledge to the physical, is replaced by the idea of perspectivism - the possibility of building a unified science based on the isomorphism of laws in various fields. Within the framework of the general theory of systems, Bertalanffy and his collaborators have developed a special apparatus for describing the "behavior" of open systems, based on the formalism of the thermodynamics of irreversible processes, in particular, on the apparatus for describing the so-called. equifinal systems (capable of reaching a predetermined final state regardless of changes in the initial conditions). The behavior of such systems is described by the so-called. teleological equations, expressing the characteristic of the system's behavior at each moment of time as a deviation from the final state, to which the system seems to "strive".
In the 1950s and 70s. a number of other approaches to the construction of a general theory of systems have been proposed (M. Mesarovich, L. Zade, R. Ackoff, J. Kleer, A. I. Uemov, Yu. A. Urmantsev, R. Kalman, E. Laslo, etc.). At the same time, the main attention was paid to the development of the logical-conceptual and mathematical apparatus of systems research. In the 1960s. (under the influence of criticism, as well as as a result of the intensive development of systems of scientific disciplines close to the general theory) Bertalanffy made clarifications to his concept, and in particular distinguished two meanings of the general theory of systems. In a broad sense, it acts as a fundamental science, covering the entire set of problems associated with the study and design of systems (the theoretical part of this science includes cybernetics, information theory, game and decision theory, topology, network theory and graph theory, as well as factor analysis) ... General systems theory in the narrow sense of the general definition of a system as a complex of interacting elements seeks to derive concepts related to the organismic whole (interaction, centralization, finality, etc.), and applies them to the analysis of specific phenomena. The applied area of general systems theory includes, according to Bertalanffy, systems engineering, operations research, and engineering psychology.
Considering the evolution that the understanding of the general theory of systems has undergone in the works of Bertalanffy et al., It can be stated that over time there has been an ever-increasing expansion of the tasks of this concept, with virtually unchanged state of its apparatus and means. As a result, the following situation was created: a strictly scientific concept (with the appropriate apparatus, means, etc.) can only be considered a general theory of systems in a narrow sense; as for the general theory of systems in a broad sense, it either coincides with the general theory of systems in a narrow sense (in particular, in terms of apparatus), or is a real extension and generalization of the general theory of systems in a narrow sense and similar disciplines, but then the question arises about a detailed presentation of its means, methods and apparatus. In recent years, attempts have been made to apply specific applications of general systems theory, for example, to biology, systems engineering, organization theory, etc.
General systems theory is important for the development of modern science and technology: without replacing special systems theories and concepts dealing with the analysis of certain classes of systems, it formulates general methodological principles of systems research.
Literature:
1. General systems theory. M., 1966;
2. V.I. Kremyanskiy Some features of organisms as "systems" from the point of view of physics, cybernetics and biology. - "VF", 1958, No. 8;
3. Lektorsky V.Α., Sadovsky V.N. On the principles of systems research. - "VF", 1960, No. 8;
4. M. I. Setrov The significance of L. Bertalanffy's general theory of systems for biology. - In the book: Philosophical problems of modern biology. M. - L., 1966;
5. Sadovsky V.N. Foundations of general systems theory. M., 1974;
6. Blauberg I.V. Integrity problem and systems approach. M., 1997;
7. Yudin E.G. Methodology of Science. Consistency. Activity. M., 1997;
8. Bertalanffy L. Das biologische Weltbild, Bd. 1. Bern, 1949;
9. Idem. Zu einer allgemeinen Systemlehre. - Biologia generalis, 1949, S. 114-29;
10. Idem. An Outline of General System Theory. - "British Journal Philosophy of Science", 1950, p. 134-65;
11. Idem. Biophysik des Fliessgleichgewichts. Braunschweig 1953;
12. General Systems, Yearbook of the Society for General Systems Research, eds. L. Bertalanffy and A. Rapoport. Michigan, 1956 (ed. Ongoing);
13. Zadeh L.O. The Concept of State in System Theory. - Views on General System Theory, ed. by M.D. Mesarovic. N. Y., 1964.
V.N.Sadovsky
