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Introduction

1. The concept of system analysis

2. Methods of system analysis

2.1 Brainstorming

2.2 Method of expert assessments

2.3 Delphi Method

2.4 Target tree method

2.5 Morphological methods

Conclusion

Bibliography

Introduction

A systematic approach is a methodology of scientific knowledge and practical activity, as well as an explanatory principle, which are based on the consideration of an object as a system.

The systematic approach consists in the rejection of one-sided analytical, linear-causal research methods.

The main emphasis in its application is on the analysis of the integral properties of the object, the identification of its various connections and structure, features of functioning and development.

The systems approach seems to be a fairly universal approach in the analysis, research, design and management of any complex technical, economic, social, environmental, political, biological and other systems.

The purpose of a systematic approach is that it directs a person to a systematic vision of reality. It forces us to consider the world from a systemic standpoint, more precisely, from the standpoint of its systemic structure.

The system approach, being the principle of cognition, performs orientational and worldview functions, providing not only a vision of the world, but also orientation in it.

The system method implements cognitive and methodological functions. It acts as some integral set of relatively simple methods and techniques of cognition, as well as the transformation of reality.

The ultimate goal of any system activity is to develop solutions, both at the design stage of systems and in their management.

In this context, systems analysis can be considered a fusion of the methodology of general systems theory, systems approach and systems methods of justification and decision making.

The purpose of this work is to study the methods of system analysis.

In accordance with the goal, it is necessary to solve the following tasks:

To study the concept of system analysis,

Describe the methods of system analysis.

1. The concept of system analysis

System analysis is a scientific method of cognition, which is a sequence of actions to establish structural relationships between variables or elements of the system under study. It is based on a set of general scientific, experimental, natural science, statistical, and mathematical methods.

System analysis arose in the era of the development of computer technology. The success of its application in solving complex problems is largely determined by the modern capabilities of information technology Bashmakov, A.I. Theory of systems and system analysis: educational edition / A.I. Bashmakov. M., 2010. P.16.

N.N. Moiseev gives, in his words, a rather narrow definition of system analysis: “System analysis is a set of methods based on the use of computers and focused on the study of complex systems - technical, economic, environmental, etc.

The result of systematic research is, as a rule, the choice of a well-defined alternative: a plan for the development of the region, design parameters, etc.

Therefore, the origins of systems analysis, its methodological concepts lie in those disciplines that deal with decision-making problems: operations research and general control theory.

The value of a systems approach lies in the fact that consideration of the categories of systems analysis creates the basis for a logical and consistent approach to the problem of decision making. The effectiveness of problem solving with the help of systems analysis is determined by the structure of the problems being solved.

According to the classification, all problems are divided into three classes:

- well-structured (well-structured), or quantitatively formulated problems, in which the essential dependencies are clarified very well;

- unstructured (unstructured), or qualitatively expressed problems, containing only a description of the most important resources, features and characteristics, the quantitative relationships between which are completely unknown;

- ill-structured or mixed problems that contain both qualitative elements and little-known, indefinite sides that tend to dominate.

2. Methods of system analysis

Let's consider the main methods aimed at using the intuition and experience of specialists, as well as methods of formalized representation of systems.

2.1 Brainstorming method

Methods of this type pursue the main goal - the search for new ideas, their broad discussion and constructive criticism. The main hypothesis is the assumption that among a large number of ideas there are at least a few good ones. When conducting discussions on the issue under study, the following rules apply:

1) formulate the problem in basic terms, highlighting a single central point;

2) not to declare the idea false and not to stop the study of any idea;

3) support an idea of ​​any kind, even if its relevance seems doubtful to you at the moment;

4) provide support and encouragement to free the participants of the discussion from constraint.

Despite their apparent simplicity, these discussions give good results.

2.2 Method of expert assessments

The basis of these methods is various forms of expert survey followed by evaluation and selection of the most preferred option. The possibility of using expert assessments, the justification of their objectivity is based on the fact that an unknown characteristic of the phenomenon under study is interpreted as a random variable, the reflection of the distribution law of which is an individual assessment of the expert on the reliability and significance of an event. It is assumed that the true value of the characteristic under study is within the range of estimates received from the group of experts and that the generalized collective opinion is reliable. The most controversial point in these methods is the establishment of weight coefficients according to the estimates expressed by experts and the reduction of conflicting estimates to some average value. This group of methods is widely used in socio-economic research.

2.3 Delphi method

Initially, the Delphi method was proposed as one of the brainstorming procedures and was supposed to help reduce the influence of psychological factors and increase the objectivity of expert assessments. Then the method began to be used independently. It is based on feedback, familiarizing the experts with the results of the previous round and taking these results into account when assessing the significance of the experts.

2.4 Goal tree method

The term "tree" implies the use of a hierarchical structure obtained by dividing the overall goal into sub-goals. For cases where the tree-like order is not strictly maintained throughout the structure, V. I. Glushkov introduced the concept of a "predictive graph". The "tree of goals" method is focused on obtaining a relatively stable structure of goals, problems, directions. To achieve this, when constructing the initial version of the structure, one should take into account the patterns of goal formation and use the principles of forming hierarchical structures.

2.5 Morphological methods

The main idea of ​​the morphological approach is to systematically find all possible solutions to the problem by combining the selected elements or their features. In a systematic form, the method of morphological analysis was first proposed by F. Zwicky and is often called the "Zwicky method". There are three main schemes of the method:

- a method of systematic coverage of the field, based on the allocation of the so-called strong points of knowledge in the area under study and the use of certain formulated principles of thinking to fill the field;

- the method of denial and construction, which consists in formulating some assumptions and replacing them with opposite ones, followed by an analysis of the inconsistencies that arise;

- the method of the morphological box, which consists in determining all possible parameters on which the solution of the problem may depend. The identified parameters form matrices containing all possible combinations of parameters, one from each row, followed by the selection of the best combination.

One of the most complete classifications based on a formalized representation of systems, i.e. on a mathematical basis, includes the following methods:

- analytical (methods of both classical mathematics and mathematical programming);

- statistical (mathematical statistics, probability theory, queuing theory);

- set-theoretic, logical, linguistic, semiotic (considered as sections of discrete mathematics);

- graphic (graph theory, etc.).

The class of poorly organized systems corresponds in this classification to statistical representations. For the class of self-organizing systems, the most suitable models are discrete mathematics and graphical models, as well as their combinations.

Applied classifications are focused on economic and mathematical methods and models and are mainly determined by the functional set of tasks solved by the system.

The question of whether there is a problem is of paramount importance, since putting a lot of effort into solving problems that do not exist is by no means an exception, but a very typical case. Invented problems? disguise real problems. The correct and precise formulation of the problem is the first and necessary stage of a systematic study and, as you know, can be equivalent to half the solution of the problem Bakhusova, E.V. Theory of systems and system analysis: textbook / E.V. Bakhusov. Tolyatti, 2010. P.48.

To build a system, the problem must be decomposed into a set of clearly formulated tasks. At the same time, in the case of large systems (BS), the tasks form a hierarchy, in the case of complex systems (SS) - a spectrum, i.e. in relation to one object, completely different tasks will be solved in different languages. brain problem system graph goal

The position of the observer determines the criterion for solving the problem. In some cases, the definition of the object is the greatest difficulty for the researcher (as well as the definition of the national economic system and environment).

Arbitrariness in the selection of subsystems and the processes implemented in them inevitably dooms system analysis to failure.

The identification of chains and processes of development requires not only the rigor of logical thinking, but also the ability to find contact with management workers.

It is in no way possible to formulate the general goals of the organization and especially to construct a criterion for the effectiveness of the system, based only on public opinion.

It is a complex logical procedure within the framework of the concepts of general systems theory, which, however, requires a subtle knowledge of the specifics of the economy and technology of object research.

In large systems and complex systems, the goal of the system is so distant from the specific means of achieving them that the choice of a solution requires a lot of effort to link the chain with the means of its implementation through decomposition - chains. This important work is central to systems analysis. It gave birth to the goal tree method, which is the main, if not the only achievement of systems analysis.

In non-production systems (for example, systems of the social sphere), it is not logically possible to express the goal and criterion of development efficiency in an explicit way. Here, the analysis “from the natural needs of a person” is unacceptable in connection with their continuous development and change. It is necessary to follow the traditional path from the analysis of the current situation, the level reached and a consistent forecast.

System analysis, as a rule, has a depot with a development perspective. Therefore, any information about the future - situations, resources, discoveries and inventions - is of maximum interest. Therefore, forecasting is the most important and most difficult part of system analysis.

A number of social, political, moral, aesthetic and other factors that cannot be ignored in a system analysis (they are sometimes decisive) cannot be quantified.

The only way to take them into account is to obtain subjective assessments of experts. Since systems analysis tends to deal with unstructured or loosely structured problems, i.e. devoid of quantitative assessments, then obtaining assessments of specialists and their processing seem to be a necessary step in the system analysis of most problems.

The discrepancy between needs and means of satisfaction is the law and the most important stimulus for socio-economic development. Since the concepts of goals and means to achieve them are inseparable, the central point of decision-making in system analysis is the truncation of goals - cutting off those goals that are recognized as insignificant or having no means to achieve them, and selecting specific ones.

In systems research of the "engineering" type, the selection of alternatives is considered the most important, if not the only task of systems analysis.

The problems of national economic management, solved by the methods of system analysis, arise in real-life management bodies.

For the most part, the task of system analysis is not the creation of a new governing body, but the improvement of existing ones.

Therefore, there is a need for a diagnostic analysis of control bodies aimed at identifying their capabilities, shortcomings, etc. The new system will be effectively implemented if it facilitates the work of the governing body.

The results of system analysis are obtained within the framework of system concepts. For practical planning, they must be translated into the language of socio-economic categories. As a result of solving the problems of systemic analysis of major economic problems, comprehensive development programs are being created.

System analysis has a number of specific methods and techniques for designing effective goal-oriented controls, i.e. creation and use of a certain system in the national economy.

Most of these methods were developed long before the advent of system analysis and were used independently. However, in a number of cases, the system methodology makes it possible to more accurately outline the range of tasks most effectively solved by each method.

With regard to some methods, system analysis allowed us to somewhat overestimate and rethink their significance, the limits of applicability, to find typical formulations of problems solved by this method.

Conclusion

Thus, the following conclusions can be drawn from the above.

Any scientific, research and practical activity is carried out on the basis of methods (methods or methods of action), techniques (a set of methods and techniques for carrying out any work) and methodologies (a set of methods, rules for the distribution and assignment of methods, as well as work steps and their sequence) .

The most general concept that denotes all possible manifestations of systems is “systematic”, which is proposed to be considered in three aspects:

a) systems theory provides rigorous scientific knowledge about the world of systems and explains the origin, structure, functioning and development of systems of various nature;

b) a systematic approach - performs orientation and worldview functions, provides not only a vision of the world, but also orientation in it;

c) system method - implements cognitive and methodological functions.

System analysis is not something fundamentally new in the study of the surrounding world and its problems - it is based on a natural science approach. In contrast to the traditional approach, in which the problem is solved in a strict sequence of the above steps (or in a different order), the systems approach consists in the multiple-connectedness of the solution process.

The main feature of a systematic approach is the presence of a dominant role of a complex, not simple, whole, and not constituent elements. If, with the traditional approach to research, thought moves from the simple to the complex, from parts to the whole, from elements to the system, then with the systematic approach, on the contrary, thought moves from the complex to the simple, from the whole to its constituent parts, from the system to the elements. .

When analyzing and designing existing systems, various specialists may be interested in different aspects - from the internal structure of the system to the organization of control in it, which gives rise to the following approaches to analysis and design; system-element, system-structural, system-functional, system-genetic, system-communicative, system-management and system-information.

The methodology of system analysis is a set of principles, approaches, concepts and specific methods, as well as techniques.

Bibliography

1. Bakhusova, E.V. Theory of systems and system analysis: textbook / E.V. Bakhusov. - Tolyatti: TGU, 2010. - 211 p.

2. Bashmakov, A.I. Theory of systems and system analysis: educational edition / A.I. Bashmakov. - M.: Bashmakov A.I., 2010. - 199 p.

3. Kirillov, V.I. Qualimetry and system analysis: textbook / V.I. Kirillov. - M.: INFRA-M, 2011. - 439 p.

4. Mathematical models and system analysis in economics: collection of scientific papers / editorial board: D.L. Andrianov and others - Perm: Perm State. un-t, 2010. - 181 p.

5. System analysis and decision making: textbook / author: S.A. Barkalov and others - Voronezh: Publishing and Printing Center of the Voronezh State. un-ta, 2010. - 651 p.

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System analysis - this is the methodology of systems theory, which consists in the study of any objects represented as systems, their structuring and subsequent analysis. main feature

system analysis lies in the fact that it includes not only methods of analysis (from the Greek. analysis - dismemberment of an object into elements), but also methods of synthesis (from the Greek. synthesis - the connection of elements into a single whole).

The main goal of systems analysis is to detect and eliminate uncertainty when solving a complex problem based on finding the best solution from existing alternatives.

A problem in systems analysis is a complex theoretical or practical issue that needs to be resolved. At the heart of any problem lies the resolution of some contradiction. For example, the choice of an innovative project that would meet the strategic goals of the enterprise and its capabilities is a certain problem. Therefore, the search for the best solutions when choosing innovative strategies and tactics of innovative activity should be carried out on the basis of system analysis. The implementation of innovative projects and innovative activities is always associated with elements of uncertainty that arise in the process of non-linear development, both of these systems themselves and of the environment systems.

The system analysis methodology is based on the operations of quantitative comparison and selection of alternatives in the process of making a decision to be implemented. If the requirement of quality criteria for alternatives is met, then their quantitative estimates can be obtained. In order for quantitative estimates to allow comparison of alternatives, they must reflect the criteria for choosing alternatives involved in the comparison (result, efficiency, cost, etc.).

In systems analysis, problem solving is defined as an activity that maintains or improves the characteristics of a system or creates a new system with desired qualities. Techniques and methods of system analysis are aimed at developing alternative solutions to the problem, identifying the extent of uncertainty for each option and comparing options according to their effectiveness (criteria). Moreover, the criteria are built on a priority basis. System analysis can be represented as a set of basic logical elements:

• - the purpose of the study is to solve the problem and obtain a result;
• - resources - scientific means of solving the problem (methods);
• - alternatives - solutions and the need to choose one of several solutions;
• - criteria - a means (sign) of assessing the solvability of the problem;
• - a model for creating a new system.

Moreover, the formulation of the goal of system analysis plays a decisive role, since it gives a mirror image of the existing problem, the desired result of its solution and a description of the resources with which this result can be achieved (Fig. 4.2).

Rice. 4.2.

The goal is concretized and transformed in relation to the performers and conditions. A higher order goal always contains an initial uncertainty that needs to be taken into account. Despite this, the goal must be specific and unambiguous. Its staging should allow the initiative of the performers. "It's much more important to choose the 'right' target than the 'right' system," said Hall, author of a book on systems engineering; "Choosing the wrong goal is solving the wrong problem; and choosing the wrong system is simply choosing a suboptimal system."

If the available resources cannot ensure the achievement of the set goal, then we will get unplanned results. The goal is the desired result. Therefore, appropriate resources must be selected to achieve the goals. If resources are limited, then it is necessary to adjust the goal, i.e. plan the results that can be obtained with a given set of resources. Therefore, the formulation of goals in innovation activity should have specific parameters.

Main tasks system analysis:

• decomposition problem, i.e. decomposition of the system (problem) into separate subsystems (tasks);
• the task of analysis is to determine the laws and patterns of system behavior by detecting system properties and attributes;
• The task of synthesis is reduced to the creation of a new model of the system, the determination of its structure and parameters based on the knowledge and information obtained in solving problems.

The general structure of system analysis is presented in Table. 4.1.

Table 4.1

Main tasks and functions of system analysis

System Analysis Structure
decomposition
Definition and decomposition of a common goal, main function	Functional structural analysis	Development of a new system model
Separating the system from the environment	Morphological analysis (analysis of the relationship of components)	Structural synthesis
Description of influencing factors	Genetic analysis (analysis of background, trends, forecasting)	Parametric synthesis
Description of development trends, uncertainties	Analysis of analogues	Evaluation of the new system
Description as "black box"	Performance analysis
Functional, component and structural decomposition	Formation of requirements for the system being created

In the concept of system analysis, the process of solving any complex problem is considered as a solution to a system of interrelated problems, each of which is solved by its own subject methods, and then a synthesis of these solutions is made, evaluated by the criterion (or criteria) for achieving the solvability of this problem. The logical structure of the decision-making process in the framework of system analysis is shown in fig. 4.3.

Rice. 4.3.

In innovative activity, there cannot be ready-made decision models, since the conditions for implementing innovations can change, a methodology is needed that allows at a certain stage to form a decision model that is adequate to the existing conditions.

To make "weighted" design, management, social, economic and other decisions, a broad coverage and a comprehensive analysis of the factors that significantly affect the problem being solved are necessary.

System analysis is based on a set of principles that determine its main content and difference from other types of analysis. It is necessary to know, understand and apply this in the process of implementing a system analysis of innovation activity.

These include the following principles :

• 1) the ultimate goal - the formulation of the goal of the study, the definition of the main properties of the functioning system, its purpose (goal setting), quality indicators and criteria for assessing the achievement of the goal;
• 2) measurements. The essence of this principle is the comparability of the system parameters with the parameters of the higher-level system, i.e. external environment. The quality of functioning of any system can only be judged in relation to its results to the supersystem, i.e. to determine the effectiveness of the functioning of the system under study, it is necessary to present it as part of a higher-level system and evaluate its results in relation to the goals and objectives of the supersystem or the environment;
• 3) equifinality - determination of the form of sustainable development of the system in relation to the initial and boundary conditions, i.e. determining its potential. The system can reach the desired final state regardless of time and determined solely by the system's own characteristics under different initial conditions and in different ways;
• 4) unity - consideration of the system as a whole and a set of interrelated elements. The principle is focused on "looking inside" the system, on dismembering it while maintaining integral ideas about the system;
• 5) relationships - procedures for determining relationships, both within the system itself (between elements) and with the external environment (with other systems). In accordance with this principle, the system under study, first of all, should be considered as a part (element, subsystem) of another system, called a supersystem;
• 6) modular construction - the allocation of functional modules and a description of the totality of their input and output parameters, which avoids excessive detail to create an abstract system model. The allocation of modules in the system allows us to consider it as a set of modules;
• 7) hierarchies - defining the hierarchy of the functional and structural parts of the system and their ranking, which simplifies the development of a new system and establishes the order of its consideration (research);
• 8) functionality - joint consideration of the structure and functions of the system. In the case of introducing new functions into the system, a new structure should also be developed, and not include new functions in the old structure. Functions are associated with processes that require the analysis of various flows (material, energy, information), which in turn affects the state of the elements of the system and the system itself as a whole. Structure always limits flows in space and time;
• 9) development - determining the patterns of its functioning and the potential for development (or growth), adaptation to changes, expansion, improvement, embedding new modules based on the unity of development goals;
• 10) decentralization - a combination of the functions of centralization and decentralization in the management system;
• 11) uncertainties - taking into account uncertainty factors and random factors of influence, both in the system itself and from the external environment. Identification of uncertainty factors as risk factors allows them to be analyzed and a risk management system to be created.

The principle of the ultimate goal serves to determine the absolute priority of the final (global) goal in the process of conducting a system analysis. This principle dictates the following regulations:

• 1) first, it is necessary to formulate the objectives of the study;
• 2) the analysis is carried out on the basis of the main goal of the system. This makes it possible to determine its main essential properties, quality indicators and evaluation criteria;
• 3) in the process of synthesis of solutions, any changes must be evaluated from the standpoint of achieving the final goal;
• 4) the purpose of the functioning of an artificial system is set, as a rule, by a supersystem in which the system under study is an integral part of .

The process of implementing system analysis in solving any problem can be characterized as a sequence of main stages (Fig. 4.4).

Rice. 4.4.

At the stage decomposition carried out:

• 1) definition and decomposition of the general goals of solving the problem, the main function of the system as a limitation of development in space, the state of the system or the area of ​​\u200b\u200bpermissible conditions for existence (a tree of goals and a tree of functions are defined);
• 2) selection of the system from the environment according to the criterion of participation of each element of the system in the process leading to the desired result based on the consideration of the system as an integral part of the supersystem;
• 3) definition and description of influencing factors;
• 4) description of development trends and various types of uncertainties;
• 5) description of the system as a "black box";
• 6) decomposition of the system according to a functional feature, according to the type of elements included in it, but structural features (by type of relationship between elements).

The level of decomposition is determined based on the goal of the study. Decomposition is carried out in the form of subsystems, which can be a series (cascade) connection of elements, a parallel connection of elements and a connection of elements with feedback.

At the stage analysis A detailed study of the system is carried out, which includes:

• 1) functional and structural analysis of the existing system, allowing to formulate requirements for the new system. It includes clarification of the composition and patterns of functioning of elements, algorithms for the functioning and interaction of subsystems (elements), separation of controlled and unmanaged characteristics, setting the state space, time parameters, analysis of the integrity of the system, formation of requirements for the system being created;
• 2) analysis of the interrelations of components (morphological analysis);
• 3) genetic analysis (prehistory, reasons for the development of the situation, existing trends, making forecasts);
• 4) analysis of analogues;
• 5) analysis of the effectiveness of the results, the use of resources, timeliness and efficiency. The analysis includes the choice of measurement scales, the formation of indicators and performance criteria, the evaluation of results;
• 6) formulation of requirements for the system, formulation of criteria for evaluation and limitations.

In the process of analysis, various methods of solving problems are used.

At the stage synthesis :

• 1) a model of the required system will be created. This includes: a certain mathematical apparatus, modeling, evaluating the model for adequacy, efficiency, simplicity, errors, a balance between complexity and accuracy, various implementation options, block and system construction;
• 2) the synthesis of alternative structures of the system is carried out, allowing to solve the problem;
• 3) a synthesis of various system parameters is performed in order to eliminate the problem;
• 4) the options of the synthesized system are evaluated with the substantiation of the evaluation scheme itself, the processing of the results and the choice of the most effective solution;
• 5) assessment of the degree of problem solving is carried out upon completion of the system analysis.

As for the methods of system analysis, they should be considered in more detail, since their number is quite large and implies the possibility of their use in solving specific problems in the process of problem decomposition. A special place in system analysis is occupied by the modeling method, which implements the principle of adequacy in systems theory, i.e. description of the system as an adequate model. Model - this is a simplified likeness of a complex object-system, in which its characteristic properties are preserved.

In system analysis, the modeling method plays a decisive role, since any real complex system in research and design can only be represented by a specific model (conceptual, mathematical, structural, etc.).

In systems analysis, special methods simulation:

• – simulation modeling based on statistical methods and programming languages;
• – situational modeling, based on the methods of set theory, theory of algorithms, mathematical logic and representation of problem situations;
• – information modeling, based on mathematical methods of the theory of the information field and information chains.

In addition, methods of induction and reduction modeling are widely used in system analysis.

Induction modeling is carried out in order to obtain information about the specifics of the object-system, its structure and elements, ways of their interaction based on the analysis of the particular and bringing this information to a general description. The inductive method of modeling complex systems is used when it is impossible to adequately represent the model of the internal structure of an object. This method allows you to create a generalized model of an object-system, preserving the specifics of organizational properties, relationships and relationships between elements, which distinguishes it from another system. When constructing such a model, the methods of logic of probability theory are often used, i.e. such a model becomes logical or hypothetical. Then the generalized parameters of the structural and functional organization of the system are determined and their regularities are described using the methods of analytical and mathematical logic.

Reduction modeling is used to obtain information about the laws and patterns of interaction in a system of various elements in order to preserve the whole structural formation.

With this method of research, the elements themselves are replaced by a description of their external properties. The use of the reduction modeling method allows solving problems of determining the properties of elements, the properties of their interaction and the properties of the structure of the system itself, in accordance with the principles of the whole formation. This method is used to search for methods for decomposing elements and changing the structure, giving the system as a whole new qualities. This method meets the goals of synthesizing the properties of the system based on the study of the internal potential for change. The practical result of using the synthesis method in reduction modeling is a mathematical algorithm for describing the processes of interaction of elements in the whole formation.

The main methods of system analysis are a set of quantitative and qualitative methods that can be presented in the form of a table. 4.2. According to the classification of V. N. Volkova and A. A. Denisov, all methods can be divided into two main types: methods of formal representation of systems (MFPS) and methods and methods for activating the intuition of specialists (MAIS).

Table 4.2

Methods of system analysis

Consider the content of the main methods of formal representation of systems that use mathematical tools.

analytical methods, including methods of classical mathematics: integral and differential calculus, search for extrema of functions, calculus of variations; mathematical programming; methods of game theory, algorithm theory, risk theory, etc. These methods make it possible to describe a number of properties of a multidimensional and multiply connected system, displayed as a single point moving in n -dimensional space. This mapping is done using the function f (s ) or by means of an operator (functional) F (S ). It is also possible to display two systems or more or parts of them with dots and consider the interaction of these dots. Each of these points moves and has its own behavior in n -dimensional space. This behavior of points in space and their interaction are described by analytical patterns and can be represented as quantities, functions, equations, or a system of equations.

The use of analytical methods is due only when all system properties can be represented in the form of deterministic parameters or dependencies between them. It is not always possible to obtain such parameters in the case of multicomponent, multicriteria systems. To do this, it is necessary to first establish the degree of adequacy of the description of such a system using analytical methods. This, in turn, requires the use of intermediate, abstract models that can be investigated by analytical methods, or the development of completely new systemic methods of analysis.

Statistical Methods are the basis of the following theories: probabilities, mathematical statistics, operations research, statistical simulation, queuing, including the Monte Carlo method, etc. Statistical methods allow you to display the system using random (stochastic) events, processes that are described by the corresponding probabilistic (statistical) characteristics and statistical regularities. Statistical methods are used to study complex non-deterministic (self-developing, self-managing) systems.

set-theoretic methods, according to M. Mesarovich, they serve as the basis for the creation of a general theory of systems. With the help of such methods, the system can be described in universal terms (a set, an element of a set, etc.). When describing, it is possible to introduce any relationship between elements, guided by mathematical logic, which is used as a formal descriptive language of relationships between elements of different sets. Set-theoretic methods make it possible to describe complex systems in a formal modeling language.

It is expedient to use such methods in cases where complex systems cannot be described by methods of one subject area. Set-theoretic methods of system analysis are the basis for the creation and development of new programming languages ​​and the creation of computer-aided design systems.

Boolean Methods are a language for describing systems in terms of the algebra of logic. The most widely used logical methods are under the name of Boolean algebra as a binary representation of the state of computer element circuits. Logical methods make it possible to describe the system in the form of more simplified structures based on the laws of mathematical logic. On the basis of such methods, new theories of formal description of systems in the theories of logical analysis and automata are being developed. All these methods expand the possibility of using system analysis and synthesis in applied informatics. These methods are used to create models of complex systems that are adequate to the laws of mathematical logic to build stable structures.

linguistic methods. With their help, special languages ​​are created that describe systems in the form of thesaurus concepts. Thesaurus is a set of semantic units of a certain language with a system of semantic relations given on it. Such methods have found their application in applied informatics.

Semiotic Methods are based on the concepts: symbol (sign), sign system, sign situation, i.e. used to symbolically describe content in information systems.

Linguistic and semiotic methods have become widely used when it is impossible to formalize decision making in poorly formalized situations for the first stage of the study and analytical and statistical methods cannot be used. These methods are the basis for the development of programming languages, modeling, automation of the design of systems of varying complexity.

Graphic methods. They are used to display objects in the form of a system image, and also allow you to display system structures and relationships in a generalized form. Graphic methods are volumetric and linear-planar. They are mainly used in the form of a Gantt chart, bar charts, charts, diagrams and drawings. Such methods and the representation obtained with their help make it possible to visualize the situation or the decision-making process in changing conditions.

Alekseeva M. B. System approach and system analysis in economics.

Alekseeva M. B., Balan S. N. Fundamentals of systems theory and system analysis.

System Analysis Methodology

System analysis is a science that deals with the problem of decision making in the conditions of analyzing a large amount of information of various nature. systemic foreign trade agro-industrial Russian

It follows from the definition that the purpose of applying system analysis to a specific problem is to increase the degree of validity of the decision being made, to expand the set of options among which the choice is made, while indicating the methods of rejection that are obviously inferior to others. In system analysis, there are:

· methodology;

· hardware implementation;

practical applications.

The methodology includes definitions of the concepts used and the principles of a systematic approach.

Let us give the main definitions of system analysis.

An element is a certain object (material, energetic, informational) that has a number of important properties for us, but the internal structure (content) of which is irrelevant to the purpose of consideration.

Communication - important for the purposes of consideration of the exchange between elements of matter, energy, information.

System - a set of elements that has the following features:

links that allow, by means of transitions along them from element to element, to connect any two elements of the collection;

a property that is different from the properties of individual elements of the population.

Almost any object from a certain point of view can be considered as a system. The question is how reasonable such a view is.

A large system is a system that includes a significant number of elements of the same type and links of the same type. An example is a pipeline. The elements of the latter will be the areas between the seams or supports. For strength calculations using the finite element method, small sections of the pipe are considered as elements of the system, and the connection has a force (energy) character - each element acts on neighboring ones.

A complex system is a system that consists of elements of different types and has heterogeneous connections between them. An example is a computer, a forest tractor or a ship.

An automated system is a complex system with a decisive role of elements of two types:

in the form of technical means;

as a human action.

For a complex system, automated mode is considered more preferable than automatic.

The structure of the system is the division of the system into groups of elements, indicating the links between them, unchanged for the entire time of consideration and giving an idea of ​​the system as a whole. This division may have a material, functional, algorithmic or other basis. An example of a material structure is a structural diagram of a prefabricated bridge, which consists of individual sections assembled on site and indicates only these sections and the order in which they are connected. An example of a functional structure is the division of an internal combustion engine into power, lubrication, cooling, and torque transmission systems. An example of an algorithmic structure is an algorithm of a software tool that indicates a sequence of actions or an instruction that determines actions when finding a malfunction of a technical device.

The structure of a system can be characterized by the types of connections it has. The simplest of these are series, parallel, and feedback.

Decomposition - division of the system into parts, convenient for any operations with this system. Examples are: division of the object into separately designed parts, service areas; consideration of a physical phenomenon or a mathematical description separately for a given part of the system.

Hierarchy - a structure with the presence of subordination, i.e. unequal links between elements, when impacts in one of the directions have a much greater impact on the element than in the other. The types of hierarchical structures are diverse, but there are only two hierarchical structures important for practice - tree-like and diamond-shaped.

The tree structure is the easiest to analyze and implement. In addition, it is always convenient to single out hierarchical levels in it - groups of elements located at the same distance from the top element. An example of a tree structure is the task of designing a technical object from its main characteristics (upper level) through the design of the main parts, functional systems, groups of units, mechanisms to the level of individual parts.

The principles of the systems approach are provisions of a general nature, which are a generalization of the experience of a person working with complex systems. They are often considered the core of the methodology. About two dozen such principles are known, a number of which it is advisable to consider:

· the principle of the final goal: the absolute priority of the final goal;

The principle of unity: joint consideration of the system as a whole and as a set of elements;

the principle of connectedness: consideration of any part together with its connections with the environment;

The principle of modular construction: it is useful to select modules in the system and consider it as a set of modules;

the principle of hierarchy: it is useful to introduce a hierarchy of elements and (or) their ranking;

· principle of functionality: joint consideration of structure and function with priority of function over structure;

The principle of development: taking into account the variability of the system, its ability to develop, expand, replace parts, accumulate information;

· the principle of decentralization: a combination of centralization and decentralization in decisions and management;

· the principle of uncertainty: accounting for uncertainties and randomness in the system.

The hardware implementation includes standard techniques for modeling decision making in a complex system and general ways of working with these models. The model is built in the form of connected sets of individual procedures. Systems analysis examines both the organization of such sets and the kind of individual procedures that are most suited to making consistent and managerial decisions in a complex system.

The decision model is most often depicted as a diagram with cells, links between cells, and logical transitions. Cells contain specific actions - procedures. The joint study of procedures and their organization follows from the fact that without taking into account the content and characteristics of the cells, the creation of schemes is impossible. These schemes define the decision-making strategy in a complex system. It is from the study of the associated set of basic procedures that it is customary to begin solving a specific applied problem.

Separate procedures (operations) are usually classified into formalizable and non-formalizable. Unlike most scientific disciplines that strive for formalization, systems analysis admits that in certain situations, non-formalizable decisions made by a person are more preferable. Consequently, system analysis considers formalizable and non-formalizable procedures in aggregate, and one of its tasks is to determine their optimal ratio.

The formalized aspects of individual operations lie in the field of applied mathematics and the use of computers. In a number of cases, a connected set of procedures is studied by mathematical methods and the decision-making itself is modeled. All this allows us to talk about the mathematical basis of system analysis. Such areas of applied mathematics as operations research and system programming are closest to the system formulation of questions.

The practical application of system analysis is extremely extensive in content. The most important sections are scientific and technical developments and various tasks of the economy.

Basic Concepts of Operations Research

An operation is any event (system of actions) united by a single plan and directed towards the achievement of some goal.

The purpose of operations research is a preliminary quantitative justification of optimal solutions.

Any definite choice of parameters depending on us is called a solution. Solutions are called optimal if they are preferred over others for one reason or another.

The parameters, the totality of which forms a solution, are called elements of the solution.

The set of admissible solutions are given conditions that are fixed and cannot be violated.

Performance indicator - a quantitative measure that allows you to compare different solutions in terms of efficiency.

All decisions are always made on the basis of information available to the decision maker (DM).

Each task in its formulation should reflect the structure and dynamics of the decision maker's knowledge about the set of feasible solutions and about the performance indicator.

A task is called static if the decision is made in a known and unchanging information state. If the information state in the course of decision-making replace each other, then the task is called dynamic.

The information states of the decision maker can characterize his physical state in different ways:

· If the information state consists of a single physical state, then the task is called definite.

· If the information state contains several physical states and the decision maker, in addition to their set, also knows the probabilities of each of these physical states, then the problem is called stochastic (partially indeterminate).

· If the informational state contains several physical states, but the decision maker, apart from their set, knows nothing about the probability of each of these physical states, then the problem is called indeterminate.

Statement of problems of making optimal decisions

Despite the fact that decision-making methods are universal, their successful application largely depends on the professional training of a specialist who must have a clear understanding of the specific features of the system under study and be able to correctly set the task. The art of problem setting is learned from examples of successfully implemented developments and is based on a clear understanding of the advantages, disadvantages and specifics of various optimization methods. As a first approximation, we can formulate the following sequence of actions that make up the content of the problem setting process:

setting the boundary of the system to be optimized, i.e. representation of the system as some isolated part of the real world. Expanding the boundaries of the system increases the dimension and complexity of a multicomponent system and, thereby, makes it difficult to analyze. Consequently, in engineering practice one should decompose complex systems into subsystems that can be studied separately without oversimplifying the real situation;

Definition of a performance indicator, on the basis of which it is possible to evaluate the characteristics of the system or its project in order to identify the “best” project or the set of “best” conditions for the system to function. In engineering applications, economic (costs, profits, etc.) or technological (productivity, energy intensity, material consumption, etc.) indicators are usually chosen. The “best” variant always corresponds to the extreme value of the system performance indicator;

selection of intra-system independent variables that should adequately describe acceptable projects or conditions for the functioning of the system and help ensure that all the most important technical and economic decisions are reflected in the formulation of the problem;

· building a model that describes the relationship between the variables of the task and reflects the influence of independent variables on the value of the performance indicator. In the most general case, the structure of the model includes the basic equations of material and energy balances, relationships associated with design decisions, equations that describe the physical processes occurring in the system, inequalities that determine the range of acceptable values ​​of independent variables and set the limits of available resources. The elements of the model contain all the information that is usually used in the calculation of the project or the prediction of the characteristics of the engineering system. Obviously, the process of building a model is very time-consuming and requires a clear understanding of the specific features of the system under consideration.

Despite the fact that models for making optimal decisions are universal, their successful application depends on the professional training of the engineer, who must have a complete understanding of the specifics of the system under study. The main purpose of considering the examples given below is to demonstrate the variety of formulations of optimization problems based on the generality of their form.

All optimization problems have a common structure. They can be classified as problems of minimization (maximization) of the M-vector efficiency indicator W m (x), m = 1, 2, ..., M, N-dimensional vector argument x = (x 1 , x 2 , ..., x N), whose components satisfy the system of equality constraints hk (x) = 0, k = 1, 2, ..., K, inequality constraints gj (x) > 0, j = 1, 2, ..., J, regional restrictions x li< x i < x ui , i = 1, 2, ..., N.

All optimal decision-making problems can be classified according to the type of functions and dimension W m (x), h k (x), g j (x) and the dimension and content of the vector x:

single-purpose decision-making - W m (x) - scalar;

· multipurpose decision-making - W m (x) - vector;

decision-making under conditions of certainty - initial data - deterministic;

· Decision-making under conditions of uncertainty - initial data - random.

The most developed and widely used in practice is the apparatus of single-purpose decision-making under conditions of certainty, which is called mathematical programming.

Consider the decision-making process from the most general positions. Psychologists have established that the decision is not the initial process of creative activity. It turns out that the act of decision is immediately preceded by a subtle and extensive process of the brain, which forms and predetermines the direction of the decision. This stage, which can be called “predecision”, includes the following elements:

Motivation is the desire or need to do something. Motivation determines the purpose of any action, using all past experience, including results;

the possibility of ambiguous results;

· the possibility of ambiguity of ways to achieve results, that is, freedom of choice.

This preliminary stage is followed by the actual decision-making stage. But the process does not end there, because. usually after the decision is made, the evaluation of the results and the adjustment of actions follow. Thus, decision-making should not be seen as a one-time act, but as a sequential process.

The provisions put forward above are of a rather general nature, usually studied in detail by psychologists. Closer from the engineer's point of view is the following diagram of the decision-making process. This circuit includes the following components:

Analysis of the initial situation;

analysis of the possibilities of choice;

Choice of a solution

Evaluation of the consequences of the decision and its adjustment.

Methodology, as a science of methods, includes three main parts: concepts, principles and methods - formed inductively (from experience and practical needs).

The subject of study of methodology and theory is the same (in this case, systems). Theory, by definition, covers the whole set of statements about the subject of study. What then is the role of methodology?

In developed theories (t.): t. mathematical analysis, t. theories). Consequently, the means of methodology can compensate for the absence or insufficient development of theory.

In the field of systems research, the entire set of problems and methods for solving them should be determined by theory (see the diamond-shaped and pyramidal structures of system analysis, Fig. 14, 16). However, the insufficient level of development of the theory ("hole-lattice" type of rhomboid and pyramidal structures, Fig. 15) requires the involvement of methodological tools. We have already used some of the methodological means in the synthesis of GTS, these are the conceptual apparatus and separate principles. So, integrity principle is embedded in the definition of a system in the form of a function, the principle of system dynamics is embedded in the stages of existence of systems, the principle of modeling - in the space of display (modeling) of systems, the principle of qualitative and quantitative research - in the "mirror" of form and content, etc. (For a retrospective of principles, see e.g. at work).

Another part of the methodological means of system analysis has remained unclaimed so far. It includes a number of principles and almost all traditional methods. Such a large range of methods is explained by their particular scientific or interdisciplinary nature, while we carried out the synthesis of GTS in an original way, relying on classical sciences and theories (dialectical logic, propositional calculus, elements of set theory, topology, probability theory, etc.), leaving the methods and a number of principles of traditional systems analysis in reserve.

Thus, in tandem "OTS-methodology of system analysis" we will use: from the OTS - concepts, definition of the subject of research, structure of the research area, classification of problems, basic patterns, methods of propositional calculus, algebra of logic, probabilistic logic, etc.; from the methodology, we will supplement them with a number of principles and numerous traditional methods.

5.2. General principles of traditional system analysis.

In general principles, we can single out a number of principles (hypotheses) that have already been used in the synthesis of OTS. Another part of the general principles can be used to deepen and refine the OTS. In addition to general principles, private principles are possible, for example, those characteristic of individual stages, classes, types, types of systems, etc.

CENTRAL HYPOTHESIS 1 or integrity principle systems.

HYPOTHESIS 2 or the principle of organization of a real object.

HYPOTHESIS 3 or the principle of the internal structure of a real object.

PRINCIPLE 1. The basis of the similarity and difference of systems is the type of properties of material objects. This principle is used to classify systems.

PRINCIPLE 2. Function, as a distinctive feature of the system, can reflect the relationship of the system with the system itself, with the base and with the external environment. This principle is used in determining the external functional structure of the system.

PRINCIPLE 3. The functions of systems differ in the degree of stationarity and stability. This principle is used to classify systems.

PRINCIPLE 4. The source of systems can be inanimate nature, wildlife and man. This principle is used to classify systems.

HYPOTHESIS 4 or the principle of the finiteness of the existence of systems.

PRINCIPLE 5. The analysis of systems is based on their modeling. This principle is used in the definition of system space.

PRINCIPLE 6. Time has a complex structure. This principle is used in defining the subspace of time and system time.

PRINCIPLE 7. Increasing the stability of the system is achieved by complicating its structure, including through hierarchical constructions.

PRINCIPLE 8. An effective direction in the development of hierarchical structures is the alternation of rigid and discrete construction of its levels.

“In biological systems, as we move from more elementary to higher levels, we observe a regular alternation of these two levels. So in a haploid organism, the loss of even one gene can threaten it with death. However, haploid organisms are rare and, as a rule, in each cell nucleus there are two haploid set of chromosomes capable of mutual replacement and compensation - the case of the simplest discrete system.The ratio of the nucleus and plasma again has the character of a rigid mutual complement with separation of functions and the impossibility, as a rule, of separate existence.Similar cells of the same tissue again represent a discrete system with the possibility of mutual replacement cells. Different tissues in one organ rigidly complement each other. Paired and multiple organs again represent a case of a statistical discrete system. Organ systems (nervous, circulatory, excretory, etc.) are again rigidly interconnected in a whole organism. Such an alternation of discrete and hard systems we're in let's move on."

PRINCIPLE 9. The properties of the system have a dual character: they strengthen the relations of its parts or destroy them.

"The duality of properties is the source of the richness of the system's behavior," its stabilization or decay. One of the forms of duality is the presence in the systems of positive (increasing the initial impact) and negative (weakening the initial impact) feedbacks.

PRINCIPLE 10. Each task of system analysis is first probed by qualitative methods, and then by formal ones.

PRINCIPLE 11. Along with qualitative and formal methods, when solving problems of system analysis, it is advisable to make maximum use of graphic, tabular and simulation methods and tools.

PRINCIPLE 12. The concepts of system analysis can be in the following relationships: subordination, subordination, crossing, outsideness.

This principle is used in the formation of a complete and consistent system of GTS concepts.

PRINCIPLE 13. When solving any problem of system analysis, the model of the system as a whole, compiled with the required degree of accuracy, should be primary.

This principle is implemented by introducing the space of mapping (modeling) systems.

PRINCIPLE 14. The tasks of system analysis can be solved by methods of iteration, detailing, enlargement, analogies.

PRINCIPLE 15. Primary in the system is integrity. Elements in the system can be discrete, continuous, blurry, coincide with the system, absent.

PRINCIPLE 16. The system is not a set, it can be considered as a set under the appropriate conditions.

We have taken this principle into account by abandoning the set-theoretical basis of GTS and placing dialectical logic and propositional calculus at the basis of GTS.

PRINCIPLE 17. System analysis can be strengthened by functioning analysis, evolution forecasting, system synthesis.

We took this principle into account by including the entire area of ​​system research in the area of ​​system analysis.

PRINCIPLE 18. System analysis has at its disposal the possibility of using the similarity (isomorphism) of regularities at various structural levels, determined primarily by the interconnection and unity of opposites, the transition of quantity into quality, development, as the negation of negation, and cycles.

We took this principle into account when forming the structure and rules for the withdrawal of OTC.

PRINCIPLE 19. Each qualitatively specific class of systems has its own specific system properties, called speciomorphisms.

PRINCIPLE 20. In a hierarchical system, the strength of the connection between levels is determined not only by their proximity. The systemic-hierarchical subordination of expediencies is rather rigid: the conflict between expediencies of different structural levels, as a rule, is resolved in favor of "superior" levels.

PRINCIPLE 21. The external environment of the system is not the system.

PRINCIPLE 22. External relations of the system are determined by function, internal - by composition and structure.

The listed general principles characterize a fairly large, but not all, number of aspects of system research. These principles do not form a system; the general theory of systems developed here organizes them into a system.

In the future, in the sections devoted to individual stages of systems, we will give or formulate additional particular principles.

System analysis involves: the development of a systematic method for solving a problem, i.e. a logically and procedurally organized sequence of operations aimed at choosing the preferred solution alternative. System analysis is implemented practically in several stages, however, there is still no unity regarding their number and content, because. There is a wide variety of applied problems in science.

Here is a table that illustrates the main patterns of system analysis of three different scientific schools . (Slide 17)

In the process of system analysis, various methods are used at its different levels. System analysis plays the role of a methodological framework that combines all the necessary methods, research techniques, activities and resources for solving problems. In essence, systems analysis organizes our knowledge of an object in such a way as to help select the desired strategy or predict the results of one or more strategies that seem appropriate to those who have to make decisions. In the most favorable cases, the strategy found through systems analysis is "best" in some specific sense.

Consider the methodology of system analysis on the example of the theory of the English scientist J. Jeffers. To solve practical problems, he proposes to distinguish seven stages, which are reflected in Slide 18.

Stage 1 "Problem selection". Realizing that there is some problem that can be investigated with the help of systems analysis, important enough to study in detail, is not always a trivial step. The very understanding that a truly systematic analysis of the problem is needed is as important as choosing the right research method. On the one hand, one can tackle a problem that is not amenable to system analysis, and on the other hand, one can choose a problem that does not require the full power of system analysis for its solution, and it would be uneconomical to study by this method. This duality of the first stage makes it critical to the success or failure of the entire study. In general, the approach to solving real problems really requires a lot of intuition, practical experience, imagination and what is called "flair". These qualities are especially important when the problem itself, as often happens, is rather poorly studied.

Stage 2 "Statement of the problem and limitation of its complexity." Once the existence of the problem is recognized, it is required to simplify the problem so that it is likely to have an analytical solution, while retaining all those elements that make the problem interesting enough for practical study. Here again we are dealing with a critical stage in any systems research. The conclusion about whether to consider this or that aspect of a given problem, as well as the results of comparing the significance of a particular aspect for an analytical reflection of the situation with its role in complicating the problem, which may well make it unsolvable, often depends on the accumulated experience in applying systems analysis. It is at this stage that you can make the most significant contribution to solving the problem. The success or failure of the whole study depends largely on a delicate balance between simplification and complexity - a balance that retains all the links to the original problem that are sufficient for the analytical solution to be interpretable. Not a single tempting project turned out to be, in the end, unrealized due to the fact that the accepted level of complexity made subsequent modeling difficult, not allowing to obtain a solution. And, on the contrary, as a result of many systematic studies carried out in various areas of ecology, trivial solutions to problems were obtained, which in fact constituted only subsets of the original problems.

Stage 3 "Establishing a hierarchy of goals and objectives." After setting the task and limiting the degree of its complexity, you can begin to set the goals and objectives of the study. Usually these goals and objectives form a certain hierarchy, with the main objectives being successively subdivided into a number of secondary ones. In such a hierarchy, it is necessary to prioritize the various stages and correlate them with the efforts that need to be made to achieve the goals set. Thus, in a complex study, it is possible to assign relatively low priority to those goals and objectives that, although important from the point of view of obtaining scientific information, have a rather weak influence on the type of decisions made regarding the impact on the system and its management. In another situation, when this task is part of the program of some fundamental research, the researcher is deliberately limited to certain forms of management and concentrates maximum efforts on tasks that are directly related to the processes themselves. In any case, for the fruitful application of systems analysis, it is very important that the priorities assigned to the various tasks are clearly defined.

Stage 4 "Choosing ways to solve problems." At this stage, the researcher can usually choose several ways to solve the problem. As a rule, families of possible solutions to specific problems are immediately visible to an experienced systems analyst. In the general case, he will look for the most general analytical solution, since this will allow him to make maximum use of the results of studying similar problems and the corresponding mathematical apparatus. Each specific problem can usually be solved in more than one way. Again, the choice of the family within which to search for an analytical solution depends on the experience of the systems analyst. An inexperienced researcher can spend a lot of time and money trying to apply a solution from any family, not realizing that this solution was obtained under assumptions that are unfair for the particular case with which he is dealing. The analyst, on the other hand, often develops several alternative solutions and only later settles on the one that best suits his task.

Stage 5 "Modeling". Once suitable alternatives have been analyzed, an important step can begin - modeling the complex dynamic relationships between various aspects of the problem. At the same time, it should be remembered that the processes being modeled, as well as the feedback mechanisms, are characterized by internal uncertainty, and this can significantly complicate both the understanding of the system and its controllability. In addition, the modeling process itself must take into account a complex set of rules that will need to be observed when deciding on an appropriate strategy. At this stage, it is very easy for a mathematician to get carried away by the elegance of the model, and as a result, all points of contact between the real decision-making processes and the mathematical apparatus will be lost. In addition, when developing a model, unverified hypotheses are often included in it, and it is rather difficult to predetermine the optimal number of subsystems. It can be assumed that a more complex model better takes into account the complexities of a real system, but although this assumption seems intuitively correct, additional factors must be taken into account. Consider, for example, the hypothesis that a more complex model also gives higher accuracy in terms of the uncertainty inherent in model predictions. Generally speaking, the systematic bias that occurs when a system is decomposed into several subsystems is inversely related to the complexity of the model, but there is also a corresponding increase in uncertainty due to errors in measuring individual model parameters. Those new parameters that are introduced into the model must be quantified in field and laboratory experiments, and there are always some errors in their estimates. After going through the simulation, these measurement errors contribute to the uncertainty of the resulting predictions. For all these reasons, in any model it is advantageous to reduce the number of subsystems included in the consideration.

Stage 6 "Assessment of possible strategies". Once the simulation has been brought to the stage where the model can be used, the stage of evaluating the potential strategies derived from the model begins. If it turns out that the underlying assumptions are incorrect, you may have to return to the modeling stage, but it is often possible to improve the model by slightly modifying the original version. It is usually also necessary to investigate the “sensitivity” of the model to those aspects of the problem that were excluded from the formal analysis at the second stage, i.e. when the task was set and the degree of its complexity was limited.

Stage 7 "Implementation of results". The final stage of the system analysis is the practical application of the results obtained in the previous stages. If the study was carried out according to the above scheme, then the steps that need to be taken for this will be quite obvious. However, systems analysis cannot be considered complete until the research reaches the stage of practical application, and it is in this respect that much of the work done has been left unfulfilled. At the same time, just at the last stage, the incompleteness of certain stages or the need to revise them may be revealed, as a result of which it will be necessary to go through some of the already completed stages again.

Thus, the purpose of multi-stage systems analysis is to help choose the right strategy for solving practical problems. The structure of this analysis is intended to focus the main effort on complex and usually large-scale problems that cannot be solved by simpler methods of research, such as observation and direct experimentation.

SUMMARY

1. The main contribution of system analysis to the solution of various problems is due to the fact that it makes it possible to identify those factors and interrelations that may later turn out to be very significant, that it makes it possible to change the method of observation and experiment in such a way as to include these factors in consideration, and highlights weak places of hypotheses and assumptions.

2. As a scientific method, systems analysis, with its emphasis on testing hypotheses through experiments and rigorous sampling procedures, creates powerful tools for understanding the physical world and integrates these tools into a system of flexible but rigorous study of complex phenomena.

3. Systematic consideration of the object involves: the definition and study of systemic quality; identification of the totality of elements forming the system; establishing links between these elements; study of the properties of the environment surrounding the system, important for the functioning of the system, at the macro and micro levels; revealing the relationships connecting the system with the environment.

4. The system analysis algorithm is based on the construction of a generalized model that reflects all the factors and relationships of the problem situation that may appear in the solution process. The system analysis procedure consists in checking the consequences of each of the possible alternative solutions for choosing the optimal one according to any criterion or their combination.

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