Modeling- a multifaceted research method, one of the ways of knowledge. It provides research into real-life objects, phenomena, social processes, organic and inorganic systems. It covers all processes. Modeling is a specific multifunctional study. Its main task is to reproduce, based on similarity with an existing object, another object (model) that replaces it. The model is an analogue of the original. It should be similar to the original, but not repeat it, since in this case it is the modeling that loses its meaning. Free modeling is also unacceptable; in this case, it does not provide the necessary idea of ​​the original model, and also does not fulfill its function.

Functions of social modeling: deepening knowledge of existing systems and objects; determination of basic parameters, ways of their further application; conducting a comparative analysis of the original and the model, identified qualitative characteristics.

Modeling also performs important heuristic functions: it identifies negative trends, identifies positive ways to solve problems, and offers alternative options.

There are several types (types) of models: cognitive, heuristic; future models - predictive; models of the desired, given state.

Modeling goals: reflect the current state of the problem; identify the most acute “critical” moments, “knots” of contradictions; determine development trends and those factors whose influence can correct undesirable development; to intensify the activities of state, public and other organizations and individuals in search of optimal options for solving social problems.

The model must meet certain requirements:

1. To be simpler, more convenient, to provide information about the object, to contribute to the improvement of the object itself.

2. Contribute to the definition or improvement of the characteristics of an object, the rationalization of methods of its construction, management or knowledge of the object.

In general, the model must meet the following requirements: completeness, adequacy and evolution; be abstract to allow for the variation of a large number of variables; satisfy the conditions that limit the solution of the problem; focus on achieving tasks using available capabilities; provide new useful information about a social object or phenomenon; be built on the use of established terminology to cause the possibility of verifying its truth, compliance with a social object, process, phenomenon.

They define the basic principles for developing models of complex objects and phenomena used in social modeling: a compromise between the expected accuracy of modeling results and the complexity of the model, balance of accuracy, sufficient versatility of model elements, visibility of the model, block representation of the model, specialization of models, etc.

Modeling of social processes is carried out in the following forms: predictive model of income and wages; model of a social system.

The use of mathematical models of social forecasting is carried out in the direction of forecasting the budgets of families, which are divided into groups and composition; the use of probability theory and mathematical statistics to determine the level of well-being of the population.

Social models include: modeling of demographic processes; models of environmental safety; models of social adaptation of migrants, etc.

The system-functional approach leads to modeling of social processes at the regional level, management decisions, and the like.

Modeling as a technology of social work - modeling of social work subjects (systems, services, projects, programs, processes, specialist models); modeling ways and methods of solving problem situations; modeling positive behavior of an individual in various conditions of social life; directions of modern social work with various target groups and categories of the population.

Forecasting is a social theory of knowledge, which is in specific interaction with a number of groups of knowledge, which to one degree or another consider the future as the main object, carry out analysis at different levels - theoretical, psychological-intuitive, practical - problems of the near and distant future.

The object of the forecast is the processes (phenomena, events) that the study is aimed at in order to develop a forecast. The purpose of the forecast is to correctly evaluate everything new that now has a positive effect on social life, that from modern life can not only be stored, but also move into the future. This applies to various forms of social life, principles, content and methods of activity.

Forecasting serves to prepare pre-prepared proposals, projects, programs, recommendations and assessments, that is, it determines in what direction the development of objects in the area under study is desirable (culture, health care, education, agriculture), and how development can actually occur. In accordance with this, the types of forecasting tasks are also determined: determining and motivating development goals; determination of means, methods, ways to achieve goals.

Social Forecasting- this is a study of the social system at a deeper level, which allows us to foresee and predict the future, which at the same time acts as a synthesis of diverse knowledge about society.

There are several stages of social forecasting: analytical, research, program, organizational.

The analytical stage must determine the state and development trends of the forecast object and answer the question: what is the desired level of satisfaction of specific social needs, the achievement of which is associated with the development of the forecast object; what results of future development and in what industries and areas are desirable and necessary to achieve the desired level.

The experimental stage answers the following questions: what are the possible results of future development in the indicated areas of the object under study; what problems arise due to the discrepancy between the necessary and possible results of future development; allows you to clearly formulate the problem that arises as a result of the research and needs to be solved.

The program stage determines the receipt of answers to the question: what are the possible ways (options) of achieving desired and undesirable results; the period of time it will take to realize each of the possible results; what is the degree of confidence in the implementation of each of the possible solution options (paths).

The organizational stage is the personnel, material and technical financial resources necessary to implement each of the possible options; a set of organizational and technical measures that ensure obtaining certain results in achieving one or another option; determination of the most rational of them.

The system of forecasting methods and methods is called forecasting methodology, which covers the following stages: 1) pre-forecast orientation: determination of the object of research (health care, students, pensioners, etc.), the subject of research (for example, the level of economic security of students), problems, goals, objectives, time; putting forward working hypotheses, choosing methods; determining the structure and organization of the study; 2) forecast background - collection of data that influences the development of the object: decisions, new documents, immediate events, while processes in related areas are taken into account; 3) search model - a generalized vision of an object in a system of basic indicators, parameters that reflect its character and structure; 4) search forecast - projection of the original model into the future in accordance with the trend that is observed taking into account factors of the forecast background in order to identify problems to be solved; 5) normative forecast - projection of the original model into the future in accordance with specified goals and standards according to certain criteria; 6) assessing the degree of reliability and clarifying predictive models using an expert survey system; 7) development of recommendations for preparing optimal solutions based on comparison of predictive models.

Currently, there are more than 200 forecasting methods. Among them, the most common are extrapolation and examination methods, which are based on time and parametric series of the retrospective development of the forecast object. Other methods are based on the use of computer technology, the development of special algorithms and programs that require significant resources and higher qualifications of forecast developers: multi-level morphology, multi-level examination, matrix methods.

Associative methods- forecasting procedures based on the construction of specific analogue models of real objects and processes.

Games are a method used in direct pre-planning research, as well as for verification of forecasts.

Simulation is the construction of a mathematical model for the purpose of training and verification of solutions as the results of a predictive study.

An individual prediction by an expert is an assessment of a leading specialist-leader, an expert in a certain field of analysis or research.

Intuitive methods (predictions) - most widely used in the management system, as well as in forecasting various social phenomena, are based on the widespread involvement of the most competent experts and the constant improvement of their qualifications and responsibility for the examination.

Historical analogy is the transfer in time or from other areas of knowledge of identified patterns, trends in the development of similar events.

Causal modeling is the establishment of cause-and-effect relationships of known facts.

The classification characteristics of forecasting methods are specific differences in the degree of formatting, in the principle of operation and in the method of obtaining information.

Classification of methods - selection of methods adequate to the tasks being solved.

The Delphi method involves several stages of autonomous questioning of experts, who are grouped together. There are several special methods for processing and selecting the results of expert surveys.

Advanced information methods are a group of methods based on the properties of scientific and technical information to anticipate the practical implementation of scientific achievements.

Brainstorming is a collective assessment, regulated by special rules, which are based on stimulating the creative activity of experts through joint discussion of the problem.

Statistical modeling is the development and analysis of models that are created on the basis of statistical material of the past and present.

Scenario - development and description of the expected course of events in the area under study (environment, system) and its environment, starting from a specific initial stage and ending with the time of advance of the forecast.

Heuristic methods are based on the analysis of historical and systemic determining relationships. The prediction mechanism is based on extrapolation, scenario, probable forecasts, and statistical modeling.

Among the types and technologies of social forecasting there are: forecasting the standard of living and employment of the population, pensions, economic security (poverty, unemployment), forecasting environmental processes, etc.

Forecasting as a technology of social work is a study of a social system in order to predict the effectiveness of forms, methods, approaches, design and programming of social work with an individual client at the level of individual work, a group, community, society at the meso level, the activities of social services, organizations and institutions at the macro level of social work. The choice of forecasting methods depends on the content of social work, its specific direction, categories of clients, etc.

Main literature

Gershunsky B.S. Pedagogical prognostics: Methodology, theory, practice. - M., 1986.

Fundamentals of social forecasting: Textbook. method, manual / Ed. G.E. Shepitko. - M., 2001.

Safronova V.M. Forecasting and modeling in social work: Textbook. allowance. - M.: Publishing house. Center "Academy", 2002. -192 p.

Tyuptya L, T., Ivanova I.B. Social work (theory and practice): Proc. allowance. - M.: VMUROL "Ukraine", 2004. - P. 237-242.

Topics for discussion

1. The essence of social modeling as a multifaceted research method, a path of knowledge; social modeling functions.

2. Features of models in social work: cognitive, heuristic, prognostic, models of the desired or given state.

3. Goals of modeling, professional requirements for social modeling.

5. Stages of social modeling and forecasting.

6. Characteristics of types of social forecasting.

The experience of state regulation of various sectors of the national economy (including the social sphere) indicates that it should be based on systematic scientific planning and forecasting, which allows, on the basis of the information received about the past and present state of the economy, to suggest alternative ways of its development in the coming period .

The development of forecasting methodology took place in the process of systematized scientifically based planning and forecasting of industry development. Modeling and forecasting methodology allows, based on the analysis of retrospective data, exogenous and endogenous connections of passion, to derive judgments of a certain reliability regarding its future development.

Currently, there are methods of expert assessment, logical methods of modeling and forecasting, methods of input-output balance, mathematical, econometric and simulation methods of modeling.

Expert methods are based on information supplied by specialist experts in the process of systematized procedures for identifying and summarizing opinions. Expert forecasting methods have proven themselves well in cases where it is impossible to take into account the influence of many factors due to the significant complexity of the forecast object, in the presence of a high degree of uncertainty in the information available in the forecast database, or in the absence of information about the forecast object at all.

Expert methods include round table or commission methods, collective generation of ideas, or brainstorming, Delphi, expert classification method and some others.

The main disadvantages inherent in expert assessment methods :

Labor intensity of organizing examinations;
- vagueness of judgments due to fear of responsibility for them;
- influence of interpersonal relationships;
- compliance with obvious or hidden pressure from management;
- the desire to simplify complex multi-criteria tasks;
- insufficient orientation in related areas;
- inability to predict converging (intersecting) paths of development and (or) changes in competing systems;
- difficulty in presenting estimates in a task-appropriate form;
- extrapolation of past experience without comprehensive consideration of emerging and expected changes;
- the impossibility of building a holistic model of the problem; the structure and cause-and-effect relationships of the model are also not revealed with this approach.

Collective expert assessments are modern scientific methods and are widely used in forecasting. A natural area of ​​their application is the forecast of socio-economic development of the industry. In conditions of uncertainty and instability in the development of the socio-economic system of Russia, methods of expert assessments become of great importance.

Among logical methods The most widely used methods are the method of historical analogies and the method of developing scenarios.

Method of historical analogies effective in determining development paths based on constructing an analogy with patterns that have already taken place in history. This method is unlikely to be used in an unstable economic situation.

Development scenario development method , which combines qualitative and quantitative approaches can now be effectively used.

A scenario is a model of the future that describes the possible course of events indicating the probabilities of their implementation . The scenario identifies the main factors that must be taken into account and indicates how these factors might affect the hypothesized events.

As a rule, several alternative scenarios are compiled. The most likely scenario is considered as the base one, on the basis of which decisions are made.

Using the scenario analysis method in its pure form, without computer analysis, has one very big drawback - the results of certain proposed development scenarios are predicted and assessed by an expert, based on his understanding of the problem and ability to assess the impact of the proposed course of events on the final result, but this does not add trust this method.

Therefore, a promising development of this method in recent years has been its use in combination with methods such as mathematical and simulation modeling, which makes it possible to evaluate the result of the proposed sequence of actions and events using appropriate models.

The simplest type among mathematical forecasting models are trend models in which the approximating function is selected based on the best match with the available data. A trend model is a mathematical model that describes the change in a predicted or analyzed indicator only depending on time .

However, this approach does not take into account possible changes in the cause-and-effect relationships between model parameters. Therefore, it can only be used for forecasting for a relatively short period, during which one can assume the constancy of the existing conditions of economic development.

The main disadvantages of trend models are :

The assumption that relationships found in historical data will persist in the future is, in some cases, erroneous;
- do not reveal structural changes in the development of the industry;
- problems arise with meaningful interpretation of the results;
- short forecasting period;
- cannot be used on small samples and sparse data.

One of the most important tools for analysis and forecasting of socio-economic systems is econometric modeling method , which is most effective in the case of systems with stable, stable development trends. In general, an econometric model is a system of regression equations and identities.

Modern methods of socio-econometric forecasting make it possible to construct a detailed system of structural equations and consider them as a whole as a model of the socio-economic system. However, while being a convenient forecasting tool, econometric models do not improve the accuracy of forecasting development turning points. They are more suitable for extrapolating existing development trends than for recognizing changes in them.

Another important disadvantage of forecasting based on econometric models is the high cost of such research, which requires the use of data banks, computers, and qualified specialists in the development and operation of these models. In addition to trend and regression models, the econometric modeling method uses factor and structural models.

Budget modeling of the social sphere provides for obtaining reasonable forecast estimates characterizing the corresponding levels, dynamics, structure and relationships of budget revenues and expenses with each other and with general budget indicators.
The main problems of budget modeling are the lack of complete and reliable statistical information characterizing the real state of social sectors, as well as the low level of analysis and forecasting of dependencies between the volume of allocated budget funds and the dynamics of development of social sectors, incomplete assessments of the consequences and lost benefits due to the reduced approach to the development of the social sphere.

Complex systems, which include systems in the social sphere, are characterized by the presence of a huge number of feedback chains, positive and negative, between the elements of the systems influencing each other. Each given state of any element is determined by almost the entire history of the system’s existence, by the entire set of mutual connections of other elements that influence the state of this element.

Changes in states do not occur directly under the influence of one or several processes, not immediately, but with some delay. These circumstances do not allow us to use for research the well-developed analytical apparatus of modern mathematics, which is more suitable for studying linear dependencies inherent in simple systems.

That's why Dynamic computer modeling comes to the fore , which involves process automation based on modern information technologies. Simulation modeling is one of the most powerful tools used for the analysis and synthesis of complex systems. Recently, it has become widespread in the creation of systems for sustainable socio-economic development of regions, cities and entire sectors of the national economy.

Simulation models can take into account informal connections and characteristics of the predicted system, so they are able to most adequately reflect its development. However, it is the description of such non-formalized characteristics that poses the main difficulty in constructing simulation models.

The main problem for the successful construction of a dynamic model is the task of adequately determining the essence of the key elements of the system, the most important characteristics and parameters of their dynamics, as well as establishing connections between them that affect the dynamics of the development of the process.

In simulation modeling, there are several methodological approaches to describing complex systems :

Modeling of dynamic systems;
- discrete-event modeling;
- system dynamics;
- agent-based modeling, etc.

Analyzing traditional methods of forecasting and modeling complex socio-economic systems, we can say that the existing practice of forecasting and analytical activities does not allow obtaining a balanced forecast for the entire set of social decisions and economic indicators. Certain methods are applicable under certain conditions and have both advantages and disadvantages.

Thus, forecasting and subsequent planning from a management point of view means a set of works that prepare for making management decisions related to future events.

We are talking about reconciling the set goals and developing a set of measures necessary to achieve them within the framework of available opportunities and existing restrictions. Planning, therefore, is the systematic formation of the future of a system for a certain period of time.

So, When modeling and forecasting such complex processes that are observed in modern sectors of the national economy, the most effective are combined methods based on the integration of the simulation modeling method , as a system-forming method of decision-making in the study of socio-economic systems, as well as traditional forecasting methods .

comparison table

Tool name	Scope of application	Implemented models		Ready for use
	general purpose		basic knowledge of statistics
Statistica, SPSS, E-views	research			boxed product
Matlab			special mathematics education	programming required
SAP APO	business forecasting	algorithmic
ForecastPro, ForecastX	business forecasting	algorithmic	no deep knowledge required	boxed product
Logility	business forecasting		no deep knowledge required	requires significant modification (for business processes)
ForecastPro SDK	business forecasting	algorithmic
		imitation

Forecasting success

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Main directions and methods of forecasting

The main forecasting methods include:

Statistical methods

• Statistics is a branch of knowledge that deals with general issues of collecting, measuring and analyzing mass statistical (quantitative or qualitative) data. Statistics as a science includes sections: theoretical statistics (general theory of statistics), applied statistics, mathematical statistics, economic statistics, econometrics, legal statistics, demography, medical statistics, technometrics, chemometrics, biometrics
, scientometrics, other industry statistics, etc.

Expert methods

• Application area. Economic conditions. Solving problems of scientific and technological progress. Development of objects of great complexity.
• For an object whose development does not lend itself to substantive description or mathematical formalization. In the absence of reliable statistics regarding the control object. In conditions of great uncertainty. In the absence of a computer. In extreme situations.
• Features of application. According to expert estimates, 7-9 specialists. Development of a collective opinion of a group of experts. It takes a lot of time to poll and process data.

Expert assessment- the procedure for obtaining an assessment of the problem based on the group opinion of specialists (experts). A joint opinion is more accurate than the individual opinion of each specialist. This method can be recommended for obtaining qualitative assessments and rankings - for example, for comparing several projects according to their degree of compliance with a given criterion.

Expert judgment involves creating a mind that has greater capabilities than the individual. The source of the superpowers of multimind is the search for weak associations and assumptions based on the experience of an individual specialist. The expert approach has great potential for solving problems that cannot be solved in the usual analytical way:

Selecting the best solution option among the available ones.

Forecasting the development of the process.

Searching for possible solutions to complex problems.

Collective idea generation

• Application area. Obtaining a block of ideas for forecasting and decision making.
• Purpose, tasks to be solved. Determination of the entire possible range of development options for the managed object. Determination of an alternative range of factors affecting the forecast object. Obtaining a development scenario for the control object
• Features of application. Synthesis of the forecast object, multifactor analysis of events from the factors determining this event.

Morphological analysis

• Application area. When there is a small amount of information about the problem being studied, to obtain systematization of all possible solutions.
• Purpose, tasks to be solved. Forecasting the possible outcome of basic research. When opening new markets, creating new needs.
• Features of application. Structural relationships between objects, phenomena and concepts. Universality presupposes the use of the complete body of knowledge about an object. A necessary requirement is the complete absence of preliminary judgments. Contains the following stages: problem formulation; parameter analysis; construction of a “morphological box” containing all solutions; exploring all solutions.

Forecasting by analogy

• Application area. Resolving situations that are familiar to decision makers.
• Purpose, tasks to be solved. Solving situational management problems.
• Features of application. Using the method in the presence of analogues of objects and processes. Application of the method requires special skills.

Operations research

Operations Research (OR) is a discipline that deals with the development and application of methods for finding optimal solutions based on mathematical modeling, statistical modeling and various heuristic approaches in various areas of human activity. Sometimes the designation mathematical methods of operations research is used. Here are some examples of challenges that EOs have to face:

The knapsack problem

The traveling salesman problem

Transport task,

Container packing problem

Dispatch tasks such as Open Shop Scheduling Problem, Flow Shop Scheduling Problem, Job Shop Scheduling Problem, etc.

A characteristic feature of operations research is a systematic approach to the problem and analysis. The systems approach is the main methodological principle of operations research. It is as follows. Any problem that is solved must be considered from the point of view of its impact on the criteria for the functioning of the system as a whole. It is a characteristic of operations research that with every problem solved, new problems may arise. An important feature of operations research is the desire to find the optimal solution to a given problem (the principle of “optimality”). However, in practice, such a solution cannot be found for the following reasons: 1) the lack of methods that make it possible to find a globally optimal solution to the problem; 2) limited existing resources (for example, limited computer time), which makes it impossible to implement precise optimization methods. In such cases, they are limited to searching not optimal, but rather good, from a practical point of view, solutions. We have to look for a compromise between the effectiveness of solutions and the costs of finding them. Operations research provides a tool for finding such trade-offs.

AI is closely related to management science, systems analysis, mathematical programming, game theory, optimal decision theory, heuristic approaches, metaheuristic approaches and artificial intelligence methods such as constraint satisfaction theory and neural networks.

Operations usually refer to purposeful, controlled processes. Their nature can be different - it can be military operations, production processes, commercial events, administrative decisions, etc., etc., etc., etc... What’s interesting is that these operations (completely dissimilar in nature ) can be described by the same mathematical models (!); moreover, the analysis of these models allows us to better understand the essence of a particular phenomenon and even predict its further development.

The basic method of operations research is a systematic analysis of operations, as well as an objective comparative assessment of the potential results of these actions.

So, for example, increasing production at a plant requires the simultaneous and interrelated solution of a large number of individual problems:

reconstruction of the enterprise;

ordering equipment, raw materials and supplies;

preparation of the sales market;

technology optimization;

changing the operational production planning and dispatching system;

organizational restructuring, etc.

When analyzing the potential results of decisions made, it is necessary to take into account such components as uncertainty, randomness and risk. Such problems are solved by specialists in the fields of economics, mathematics, statistics, engineering, sociology, psychology, etc.

Thus, among the specific features of operations research, one can highlight the interdisciplinary nature.

AI is used mainly by large Western companies in solving production planning (controlling) problems

Logistics, marketing) and other complex tasks. The use of artificial intelligence in economics makes it possible to reduce costs or, to put it differently, to increase the productivity of an enterprise (sometimes several times!). IO is actively used by the armies and governments of many developed countries to solve complex problems of supplying armies, promoting armies, developing new types of weapons, developing war strategies, developing interstate trade mechanisms, forecasting developments (for example, climate), etc. Solving complex problems of increased importance is produced by AI methods on supercomputers, but development is carried out on simple PCs. AI methods can also be used in small enterprises using a PC.

Operations research is carried out mainly to provide a preliminary quantitative justification for the solutions used, since they are complex and involve high costs. Solutions are implemented in various ways or so-called strategies/alternatives. Operational research also provides a comparison of possible options for organizing an operation, allows one to assess the possible influence of particular factors on the result, identify vulnerable areas, that is, those components of the system, the improper functioning of which can have a direct negative impact on the success of the operation, etc.

From the above, the basis of the tasks of operations research seems obvious, which is expressed in the search for ways to optimally use available resources to achieve a certain goal.

Rice. 1. Types or techniques of modeling.

Material methods include those modeling methods in which research is carried out on the basis of a model that reproduces the basic geometric, physical, dynamic and functional characteristics of the object being studied.

Ideal modeling is fundamentally different from subject modeling, which is based not on a material analogy of an object and a model, but on an ideal, conceivable analogy. Ideal modeling is theoretical in nature.

Physical modeling is usually called modeling, in which a real object is contrasted with its enlarged or reduced copy, which allows research (usually in laboratory conditions) using the subsequent transfer of the properties of the processes and phenomena being studied from the model to the object based on the theory of similarity.

An example of a physical model: planetarium in astronomy.

Analog modeling is based on the analogy of processes and phenomena that have different physical natures, but are described in the same way formally (by the same mathematical equations, logical circuits, etc.). The simplest example is the study of mechanical vibrations using an electrical circuit described by the same differential equations. Here, the oscilloscope, invented in the 50s of the last century, brought an invaluable service.

By intuitive we mean modeling based on an intuitive idea of ​​the object of study that cannot be formalized or does not need it. In this sense, for example, the life experience of each person can be considered his intuitive model of the world around him.

Sign modeling is modeling that uses sign transformations of any kind as models: diagrams, graphs, drawings, formulas, sets of symbols, etc., as well as a set of laws by which you can operate with selected sign formations and their elements.

Game models

A business game is a simulation model consisting of a sequence of a number of interconnected real situations and symbolic actions of participants, defined by the goals and given rules of the game. Business games are used mainly for processing and decision making.

Main directions and methods of forecasting and modeling in management

Management (from English management, from the main English manage, from Italian maneggiare - handle a tool, from Latin manus - hand) is a function of an organization, which consists in coordinating the efforts of a group of people to achieve set goals with effective and efficient use available resources.

Management (philosophy) is the activity of a subject to change an object to achieve a certain goal. Management (in an organization) is a synonym for the concept of management. Namely, the processes of planning, control over execution, optimization, organization of processes, including possible changes in structure, motivation of performers. It is often said that management is a function of an organization (institution).

Forecast (from the Greek πρόγνωσις - foresight, prediction) - predicting the future using scientific methods or the result of the prediction itself.

A forecast is a scientific model of a future event, phenomena, etc.).

Forecasting, forecast development; in a narrow sense - a special scientific study of specific prospects for the development of a process.

Forecasting is united by a single goal: determining the nature of the process in the future. Many methods for solving the forecasting problem have one common idea: discovering connections between the past and the future, between information about the process in a controlled period of time and the nature of the process in the future. The accuracy of prediction will depend on how accurately the relationships being studied are described.

Forecasting is one of the most important human activities today. Even in ancient times, forecasts allowed people to calculate periods of drought, dates of solar and lunar eclipses and many other phenomena.
With the advent of computer technology, forecasting received a powerful impetus for development. One of the first uses of computers was to calculate the ballistic trajectory of projectiles, that is, in fact, to predict the point at which the projectile would hit the ground. This type of forecast is called a static forecast.

There are two main categories of forecasts: static and dynamic. The key difference is that dynamic forecasts provide information about the behavior of the object under study over any significant period of time. In turn, static forecasts reflect the state of the object under study only at a single point in time and, as a rule, in such forecasts the time factor in which the object undergoes changes plays a minor role.
Today, there are a large number of tools that allow you to make forecasts. All of them can be classified according to many criteria:

comparison table

Tool name	Scope of application	Implemented models	Required user training	Ready for use
Microsoft Excel, OpenOffice.org	general purpose	algorithmic, regression	basic knowledge of statistics	requires significant improvement (implementation of models)
Statistica, SPSS, E-views	research	a wide range of regression, neural network	special mathematics education	boxed product
Matlab	research, application development	algorithmic, regression, neural network	special mathematics education	programming required
SAP APO	business forecasting	algorithmic	no deep knowledge required	requires significant modification (for business processes)
ForecastPro, ForecastX	business forecasting	algorithmic	no deep knowledge required	boxed product
Logility	business forecasting	algorithmic, neural network	no deep knowledge required	requires significant modification (for business processes)
ForecastPro SDK	business forecasting	algorithmic	basic knowledge of statistics required	programming required (integration with software)
iLog, AnyLogic, iThink, Matlab Simulink, GPSS	application development, modeling	imitation	special mathematics education required	programming required (for specific areas)

Forecasting success depends on the following conditions: the volume and quality of information about the predicted process, the control object; the correctness of the formulation of the forecasting problem and the validity of the choice of method for solving it; availability of necessary computing facilities and computing apparatus in accordance with the chosen method. Without these conditions, forecasting may become impossible. The most important of them is the formulation of the problem, since it determines the requirements for the volume and quality of information, the mathematical apparatus and the accuracy of the forecast. Information about the predicted object (process) is drawn from the results of activity monitoring and statistics.

Modern forecasting technologies are based on the use of various mathematical theories: functional analysis, series theory, extrapolation and interpolation theory, probability theory, mathematical statistics, theory of random functions and random processes, correlation analysis, pattern recognition theory. To justify the choice of a particular forecasting tool, it is necessary to be able to quantify its quality.

Sources of information for forecasts are verbal and written texts obtained in the process of communications between people or in the open press. To obtain the necessary information, individual private business structures organize industrial espionage. Information from the open press is obtained using the following techniques: structural and morphological; definitions of public activity; identifying groups of patent documents; analysis of indicators; terminological and lexical analysis.

For forecasting in practice, various quantitative and qualitative methods are used.

· (structured) Quantitative methods (techniques) are based on information that can be obtained by knowing the trends in changes in parameters or having statistically reliable dependencies characterizing the production activities of the control object. Examples of these methods are time series analysis, causal (cause-and-effect) modeling.

· (unstructured) Qualitative methods are based on expert assessments of specialists in the field of decision making, for example, methods of expert assessments, jury opinions (averaging the opinions of experts in relevant areas), consumer expectation models (customer surveys).

· (loosely structured) Mixed methods contain qualitative and quantitative elements with dominance, as a rule, of qualitative and uncertain components.

(To predict crime, depending on specific conditions, a wide variety of methods are used, both general scientific and specific scientific ones. The most widely used methods are the method of extrapolation, modeling, expert assessments; comparative methods and methods of social experimentation.)

MOSCOW STATE SOCIAL UNIVERSITY

HIGHER EDUCATION

V.M. SAFRONOVA

FORECASTING AND MODELING

IN SOCIAL

Educational and methodological association of Russian universities

in Social Work Education

as a teaching aid for students

institutions of higher education

UDC 303.733.4

The publication was published within the framework of the State program of scientific and methodological support for the specialty “Social work”

Scientific adviser - V.I.Zhukov

Reviewers:

Doctor of Historical Sciences, Professor L.G. Zakharov;

Doctor of Historical Sciences, Professor A.N. Khoroshilov

Safronova V.M.

C 21 Forecasting and modeling in social work: Textbook. Benefit

for students higher schools, institutions - M.: Publishing Center "Academy",

ISBN 5-7695-0834-5

The manual examines basic issues of methodology, theory and organization of scientific forecasting and modeling of social processes, various types and types of forecasts and models. Particular attention is paid to developing the skills to implement the theoretical and methodological principles of forecasting and modeling in social practice. Extensive experimental material is provided to illustrate the theoretical principles.

The book can be useful to scientists and practitioners, as well as to anyone interested in the problems of forecasting social processes.

UDC 303.733.4

ISBN 5-7695-0834-5© Safronova V.M., 2002

© Publishing center "Academy", 2002

Recent social transformations in our country have actualized the problem of predictive research and modeling in the social sphere.

Russia's recovery from the crisis, the justification of the social development strategy, the definition of immediate and long-term programs require innovative actions and broad modern thinking based on the integration of sciences. Forecasting and modeling occupy a particularly important place here as high-tech methods of scientific analysis and foresight.

The essence of this scientific and educational direction is the systematic analysis of social processes through the prism of theoretical and methodological principles to identify problems and trends in social development, and determine ways to solve social problems.

In modern conditions, the ability to foresee and predict the future, and therefore influence social processes, is also becoming one of the most valuable qualities of a young specialist.

The university system of the Russian Federation is now acquiring both the right and the opportunity to teach social forecasting and modeling as a general professional discipline for specialists of any profile. At the Moscow State Social University 10 years ago, the department “Social Forecasting and Modeling” was first created under the leadership of the author of this textbook, which acts as a scientific and methodological center for departments being created in universities of the Russian Federation, teaching courses, lecture cycles through a system of electives, courses for training, retraining and advanced training of personnel.

The learning process is built according to the following scheme: study of theory - analysis of practice -> experimental testing -> implementation. In close cooperation with the Ministry of Labor and Social Development (Department of Analysis and Forecasting of Social and Economic Development of the Russian Federation), with other federal structures, employees of the department participate in federal research programs, which contributes to the enrichment of the educational process and a deeper study of problems during social transformations in society. Often, forecasts and models developed by students and graduate students become commercial products and help graduates more actively adapt to market conditions, increasing their competitiveness and relevance.

For the theoretical development of a new scientific direction, the department has an international certificate and was awarded first place in a university scientific competition among researchers of social practice.

The main goal of this manual is not only to introduce students to the basics of scientific forecasting and modeling of social processes, various types and types of forecasts and models, but also to consider a number of current theoretical and practical problems. Some of them involve identifying the role of forecasting and modeling in substantiating conceptual approaches to the prospects for social development and equipping everyone interested in the methodology of forecasting research to identify trends, “fields of opportunity” in social transformations, and optimal ways to achieve social results in accordance with the goals set.

The second part of the tasks provides knowledge in the field of developing forecasts and models, develops the ability to implement the theoretical and methodological principles of forecasting and modeling in social practice, and contributes to mastering the skills of development, experimental testing and implementation of the most modern modeling and forecasting technologies.

Solving these problems is aimed at creating a prognostic culture of personnel as an indispensable and important condition for increasing the efficiency of their activities, ensuring the competitiveness of specialists of any profile, at any level.

The methodology for presenting the material is based on the principle of “acquisition through simplification.” Sections of the book indicate conceptual boundaries: methodologies, technologies, techniques, scenario-game modeling. At the same time, each chapter, subsections, and paragraphs include specific and visual material that is of practical importance for a wide range of readers, including practical social workers.

A list of references for in-depth study and a dictionary of basic terms increase the practical significance of the publication. A large amount of experimental material is provided as illustrations.

The appendices contain auxiliary and educational material.

When analyzing the problems of socio-economic development of the Russian Federation, materials from the Ministry of Labor and Social Development (Department of Analysis and Forecasting of Socio-Economic Development of the Russian Federation) were used to develop a forecast for the period up to 2004.

Some technologies for modeling and forecasting socio-ecological problems are the result of research by A. S. Gosporyan, who has been developing this topic for several years.

The use of certain provisions of A. V. Markova’s scientific research in the textbook enriched the content of the textbook with relevant information and analytical material at the federal level.

In the third section of the manual, in the development of the main provisions of the system-functional approach, fragments of research from the scientific school led by the author “XXI Century: Forecasts and Models” are used.

When analyzing demographic processes, various technological modeling and forecasting approaches used by other researchers (T.V. Kuzminova, A.I. Panteleev, E.A. Nazarova) were used, which allows readers to compare existing methods and determine their own attitude towards them.

All comments and suggestions sent will be gratefully accepted and taken into account by the author.

Section I

BASICS OF FORECASTING AND

SIMULATION

Methodological aspects of forecasting

and modeling of social processes

A distinctive feature of the modern world, despite the measures and efforts taken, is its imbalance, the increase in economic, political, religious, and social cataclysms. The international community and the states of the world have come to the conclusion that the existing paradigm for the development of civilization is flawed and disastrous for the future; humanity needs a change in the conceptual approach. But in order to decide which, most humane, development model to choose, it is necessary to see some general picture of technological transformations, driving forces, and cultural consequences. There are still more questions than answers to the most pressing problem: what is the information society, to which there are supposedly no alternatives? And which seemed designed to resolve the most pressing social issues.

Yes, these are global problems (including international terrorism), the study of which is the subject of interdisciplinary and international scientific research, but without their forward-looking vision and understanding, the most energetic practical activities in any country, in various aspects of the social sphere, at all levels are futile management.

For Russian society, which is experiencing an acute socio-economic and spiritual-moral crisis, ensuring the effectiveness of managing social processes, the need to develop a forecast vision of development and prospects has become one of the urgent tasks in the field of both theoretical research and scientific substantiation of social practice and one of the most important conditions of survival, recovery and development.

Nowadays, scientists and practitioners face the need to realize the possibilities of human influence on the development of society and the world as a whole; to clarify the relationship between objective processes, on the one hand, and human influence on them, on the other. The conceptual vision of the future and its forecasting depend on this: either it is just a designation of developing trends and a forecast based on them, or it is a forecast taking into account the possibilities and necessity of human influence on the emerging development trends in accordance with modern ideas and beliefs.

Time allows us to answer many questions, based on the achievements of science, and instills some optimism in our views on the future. However, the awareness of more and more new limitations is becoming more and more obvious, which, in turn, is also an achievement of science and indicates complex contradictions.

In this regard, we will further consider the following questions: Can everything be predicted? With what degree of reliability? Are the approaches to forecasting global and local systems and situations the same? What processes and phenomena can we attribute to linear dynamics, and therefore, make a forecast with a greater degree of reliability, identify trends and propose solutions? And which ones are related to nonlinear dynamics, due to which the role of random factors increases? And how, in what way will this be reflected in social practice?

This is a wide range of issues of theory and methodology.

Methodology rightfully considered as general system of principles and regulations of human activity- processes of cognition and philosophical justification of methods and techniques for organizing the entire variety of types of human activity - and how teaching about this system. Its basis is dialectics, which performs heuristic, axiological, ideological and orienting functions. The methodological aspect is objectively inherent, in principle, in any activity, leadership, management, social work due to the organic and multifaceted interconnection of various spheres of public life, as well as the continuous strengthening of interaction between them.

The concept of forecasting and modeling (social processes) that we propose is based on two main methodological principles. The first of them is the recognition of the objective nature of social processes. The second principle is the recognition of the prevailing role in the social development of the subjective factor, i.e., the reasonable purposeful activity of people, based on accumulated scientific potential and certain ethical and moral values, and in connection with this - their ability to choose, determine guidelines for social development and ways to achieve the designated goals.

Today, more than ever, an integrated system analysis of social development is needed, allowing us to see and trace trends, the course and dynamics of social processes, while trying to separate real events from subjective reactions, emotions, intentions, and assumptions. The most important aspect of this problem is to identify the role and importance of leaders in politics and social life, their understanding of the depth of social processes and their ability to influence the course of events; explore ideals, the means by which they involve the achievement of social results.

All attempts to realize and understand the world only through one’s own experience, at the level of everyday practice, are futile, and if we are talking about public administration, they are detrimental to the country, since the outside world (“visible”) has another side, hidden from us, requiring deep scientific analysis, forecasting of social processes, based on taking into account many factors and presupposing a certain level of philosophical, intellectual, methodological culture.

It is necessary to take into account the totality of the immediate and long-term consequences of the decisions made, not only in this study area, but also in related areas; otherwise, when we are talking about social processes, as a result of interference in them, such negative phenomena may arise, a kind of chain reaction, which will negatively affect the state of affairs both in other spheres of social life and on society as a whole.

In order to look into the future, you also need to know the past. The lack of an objective analysis of the past entails both an incorrect interpretation of the present and an inability to “look” into the future, much less predict it. The past, present, and future are organically interconnected: in the name of the future, one cannot reject everything that not only predetermines it, but also ensures its stability and reliability.

In particular, many of the problems that have arisen during the current reform of Russian society cannot be considered in terms of the inevitable replacement of “old forms” with more advanced, “new” ones - as a movement forward. After all, society has been thrown back half a century; the majority of the population is below the poverty line. A tendency towards spiritual degradation of society has also emerged. It is these problems that should become the main ones; efforts should be directed towards their scientific and practical constructive solution.

Prognostics How system of scientific knowledge about the future close is connected and interacts with history and mathematics, philosophy and sociology, psychology and jurisprudence.

Forecasting- This social theory of knowledge. It is in specific interaction with a number of theoretical doctrines, concepts, systems, which to one degree or another consider the future as the main object, carry out research on the problems of the near and distant future at different levels - theoretical, psychological-intuitive, practical - and try to penetrate into the unknown.

Forecasting is fruitful only if and only if it is based on scientific systems of knowledge that make it possible to foresee the course of processes, social phenomena, development trends and the social consequences of practical measures taken.

Forecasting, which is widely used for political purposes, is often biased; the truth here is sacrificed to the proclaimed political views and concepts. Thus, the very possibility of a successful scientific forecast is largely discredited.

It should also be taken into account that for successful forecasting and modeling of social processes a certain level of theoretical thinking and a culture of thinking is required. Otherwise, it is impossible to correctly build the logic of practical actions, model options for the development of social situations, predict trends in their development, and take into account all the possible consequences of the actions taken for a particular subsystem of the social sphere and for society as a whole.

We will begin to consider the problem with the basic concepts of the course: “prognostics”, “forecast”, “forecasting”, principles of “social forecasting”, “forecasting in social practice”, etc.

Prognostics- the science of the system of our thinking about the future, of the ways and methods of studying the future. The methodology of prognostic research is based on the most valuable theoretical achievements of many sciences: historical, mathematical, philosophy, sociology. Forecasting - This scientific research method, aiming to provide possible options for those processes and phenomena that are chosen as the subject of analysis.

The forecast research methodology is based on the principle of a holistic, systematic, comprehensive consideration of the object, taking into account its hierarchical subordination, its interrelations both vertically (by level) and horizontally (with adjacent areas), dependence on external factors and internal changes.

No less important the principle is a clear definition of the status and features of the object forecasting research, a preliminary theoretical analysis of its essence based on the existing level of scientific knowledge, which will allow at all stages of the study to adhere to uniformity in the categorical conceptual apparatus and terminology, and in the process of generalizing the results to achieve the highest possible objectivity, reliability and accuracy.

The practical purpose of forecasting is the preparation of substantiated proposals, projects, programs, recommendations and assessments about:

In what direction is it desirable to develop objects in the area under study (social protection, culture, healthcare, education, youth problems, spiritual and moral processes, etc.);

How development can actually proceed;

What is the mechanism for overcoming negative trends.

In general terms, we can talk about two types of tasks: defining and motivating development goals; determination of means, methods, ways to achieve goals.

Full cycle of predictive research includes: studying the problem situation in theory and practice; analysis of pre-forecast and forecast background; defining goals and objectives; putting forward hypotheses; selection of research methods and techniques that have the necessary predictive potential; conducting experimental testing of hypotheses and verification of research results; formulating conclusions and proposals.

Forecast There is a multivariate hypothesis about possible results and development paths of the object under study (sphere, industry, type of activity, etc.).

For example, when developing a forecast for the activities of social services at the local government level to ensure targeted social protection of the population, the main hypotheses can be:

a) extensive development of social infrastructure and a corresponding increase in full-time social workers with this professional training. This is the most likely way to ensure targeted social protection of the population;

b) creating the necessary conditions for self-sufficiency of those in need of social protection who have the necessary creative and physical potential. This can help change the dynamics of the transition of this category of citizens from those in need to the level of social sufficiency.

The purpose of the forecast is to strive to provide answers to the range of questions that constitute the essence of the problem.

Social Forecasting(“social” from Latin “public, connected with society, with social relations”) - forecasting everything social, everything connected with society, with social relations, at the center of which is a person.

Foreign experience (in particular, the USA) indicates that forecasting social systems occupies a leading place (53%) among other areas of research.

In terms of time parameters, the percentage of research is as follows: for 5-10 years - 52%; for 5 -25 years - 64%; for 10 - 25 or more years - 26%.

Depending on the time period for which the forecast is made, forecasts are:

short-term (with a lead time from 1 month to 1 year);

medium-term (from 1 year to 5 years);

long-term (from 5 years to 15 years);

long-term (over 15 years).

The forecasting process itself involves:

Conducting a brief retrospective analysis of the predicted object;

Description of the current state of the object (comparative analysis of observed trends in domestic and foreign experience);

Troubleshooting:

already solved, but their implementation and implementation is just beginning;

those problems that have been solved but have not found practical use;

expert assessments of leading scientific research in this field.

Predictive research can rely on a range of methods. For example, in a predictive study of educational problems, various methods are used in order to identify trends: mathematical modeling, the Delphi method, the “naive extrapolation” method, etc.

Due to the multifactorial nature and exceptional complexity of the research object, prognostic recommendations are of a variant nature. The education strategy takes into account various development scenarios for society as a whole.

Therefore, when predicting education, the principle of variability and multi-criteria evaluation of strategic decisions is taken as a basis; various technologies of organizational forms are used on a competitive basis, allowing for an alternative vision of emerging problems and ways to overcome them. In this case, public expertise is of particular importance.

The essence of these studies in the most general form is to anticipate:

“socio-economic, scientific and technical conditions in which the education system will develop in the future;

The changing role and place of the human personality in social progress;

The dynamics of the development of educational needs of the population, the prestige of relevant professions and specialties;

The study of interethnic conflicts can also be carried out using a number of forecasting methods: the widespread use of analytical methods and computers, the use of simulation models, deep retrospection and pre-forecast background, the use of scenarios for the probable presentation of forecast information.

When conducting any prognostic study, the following factors are taken into account and carefully developed: methodological And organizational characteristics, and specific features of the prognosis and recommendations for borrowing its positive features.

Each of the provisions can be specified. Methodological aspects include, for example, the use of a systematic approach, analysis of the problem based on a retrospective study of historical analogies.

The basic experimental models of social protection of the population created by a team of MGSU scientists and social work practitioners in the context of society’s transition to market relations are one of the local approaches to research and practical implementation activities in this area in modeling and forecasting. An example could be: systemic modeling of social protection of the population in individual regions, in particular in the South-Western District of Moscow - in the Ramenki microdistrict with a population of 54 thousand people, in the Khanty-Mansiysk Autonomous District with a population of 1.5 million people, in Astrakhan and other regions. The creation of basic models and the development of forecasts is preceded by a hypothesis about the possibility of using experimentally verified models, their broad expert assessment and testing.

Basic tasks, logic analysis of the situation and development of forecasts in social processes in the regions are as follows:

Contribute to the optimal functioning of government structures;

Develop predictive support for management decisions in the field of social protection of the population;

Prevent the occurrence of adverse events and processes;

Explore the development of the social consequences of the transition to the market for families of different types (young, large families, complete, single-parent families, refugees, military personnel, the elderly), contribute to the development of positive changes;

Develop scenarios and models for the development of such families and recommendations to the government of Russia and the administrations of the constituent entities of the Federation on their social protection and support;

Conduct forecast studies of the socio-demographic composition of youth, social problems of adolescents, the socio-economic situation of working youth, interethnic relations among youth and develop practical recommendations for the social protection of youth;

Research the social consequences of privatization (in the regions of Moscow, in a number of other cities of Russia), develop forecasts and recommendations on this basis.

An integral part of forecasting is its organizational issues, such as:

Creation of a temporary creative team (TCT) and determination of the functions of it and each member individually;

Determination of methods, objects of research;

Development of forecasting methods;

Determination of computer research methods, sociological research.

Each predictive study has its own specific characteristics.

Characteristic features may include: the presence of a large array of factual material; forms of presentation of initial information; using a set of scenarios before forecasting; clear visual presentation of forecast information; widespread use of modeling and the possibility of using the original model for assessments in management activities.

Questions and tasks for self-test

1. What are the essence, content and features of prognostics as a science? What is its role and place in the system of other sciences?

2. Name the basic principles of “social forecasting”.

3. Expand the content of the basic categories of the subject: “forecasting”, “forecasting methodology”, “forecast”.

4. Determine the range of social phenomena that require long-term forecasts and give your justification.

Literature

Khukov V.I. Russia: state, prospects, contradictions. - M., 1995.

Zagladin V., Frolov I. Global forecasting of modern times: Scientific and social aspects. - M., 1981.

Kapitsa S. P., Kurdyumov S. P., Malinetsky G. G. Synergetics and future forecasts. - M., 1997.

Safronova V. M. On trends in social development in the 21st century: Through the prism of forecast: Sat. public lectures. - M., 2001.

Social forecasting and modeling / Ed. V.M. Safronova: Textbook. - M., 1995.

Forecasting in decision making

The uncertainty of the external environment puts the organization in such conditions that when making decisions, forecasting becomes necessary.

Definition 1

Forecasting– this is the development of forecasts (scientifically based judgments about the future states of the object under study, development alternatives, life spans, etc.).

Forecasting when making decisions means assessing the prospects for the development of the situation that may arise after the implementation of the decision. Forecasting is based on an analysis of the current situation in the organization and in the external environment. The purpose of forecasting is to identify trends that impact the organization and the market. Depending on the area of ​​consideration, forecasting is divided into the following types:

• economic(describe the general state of the economy for a certain period);
• technological(describe future technologies, innovations in terms of efficiency, labor intensity, cost-effectiveness, etc.);
• competitive(describe the strategy of competitors’ behavior in the market, their market share, sales level, new products, etc.);
• about the state of the commodity market(describe the market situation in terms of the influence of politics, economics, ecology, consumer income level, demographics, etc.);
• social(describes the attitude of consumers towards the organization, product).

Definition 2

Sources for making forecasts are information obtained from financial statements, statistical data, operational data, scientific and technical documentation, licenses, patents, external sources of information (mass media, Internet).

Main stages of forecasting are presented in the diagram.

Picture 1.

There are many types of forecasting; all existing methods are usually divided into three groups:

• quantitative;
• quality;
• informal.

Figure 2.

Quantitative methods include:

• mathematical methods (extrapolation, time series analysis, time series analysis),
• Causal modeling.

Qualitative methods are used when there is no complete information about the situation. The basis of this group of methods is expert assessments. These include:

• heuristic, expert methods;
• forecasting by analogy;
• logical forecasting;
• functional-logical forecasting.

Expert methods are applied in all categories of management. Experts are professionals in a particular field and evaluate a situation based on their experience and intuition.

Forecasting by analogy used very often. If there is an analogy between the current situation and the previous one, you can predict how the current situation will develop.

Informal methods forecasting is based on information that is collected in different ways: verbal, written, obtained as a result of espionage.

Modeling during decision making

Simulation of situations is a widely used method to help make management decisions. Modeling involves studying a problem by building a model, studying its properties and behavior. After a comprehensive analysis of the model, the information obtained is transferred to the real situation. A model is an abstract object that is brought into line with the situation being studied.

When making decisions, use the following types of modeling:

• conceptual (models are diagrams that reflect ideas about which variables in a situation are most significant for decision-making and how they interact, what are the connections between them);
• mathematical (the situation is presented in the form of a formula, a set of mathematical symbols and expressions; such models are convenient for quantitative analysis, they show the influence of elements within the situation on the final decision);
• imitation (with the help of a computer, the algorithm of operation of complex systems or objects is reproduced in time, their behavior and constituent elements are imitated; at the same time, the structure of the object is preserved, the sequence of processes is also observed).

The construction of any model includes several stages:

1. Description of the object. This is a preliminary description that is as close as possible to real parameters. This stage is the basis for subsequent descriptions.
2. Formalization of the object. Based on the description, the most important characteristics of the object that affect its operation are identified. Then the controllable parameters and those that cannot be controlled are determined. A system of constraints is identified, a diagram or mathematical function is constructed. Thus, the verbal description is replaced by an abstract (formal) and ordered one. 3. Adequacy check. Calculations are carried out, and based on their results, a decision is made on whether to apply the model in practice or to adjust the model.
3. Adjustment. Information about the object is clarified and the parameters of the abstract model are adjusted. Then the adequacy assessment is carried out again.
4. Optimization. While maintaining the adequacy parameters, they try to simplify the model. In this way, you can get a simpler model, but working on the same principles. The form of the model changes, but not the content. Main indicators for optimization: resource costs, time for research, time to make a decision using the model.