Chapter xii. methods of social forecasting

Among the main types of social forecasting, the following types are distinguished: search forecast, normative forecast.

The search forecast is understood as the designation of the potential states of the object of study in the future based on the analysis of its development trends in the past and present. When using a search forecast as a type of forecasting, the researcher tries to proceed from such a forecast, whose implementation will be an expression of the desired goals and include a number of the most probable states of a social object that can be analyzed from different positions. As a result of such a search, a “tree of possible results” should be formed with the definition of priorities in it, the significance of the key and derivative conditions for their achievement.

Normative forecasting refers to the determination of the necessary and sufficient means to achieve the possible states of an object or a desired goal. This type suggests a more unambiguous picture of a possible social future, and, consequently, more specific forms of opposition to those processes that will be dangerous for society.

Social forecasting itself in terms of lead time can be: 1) operational (conditionally within a year); 2) short-term (from one to five years); 3) medium-term (from five to fifteen years); 4) long-term (over fifteen years).

13 Generic forecasting methodology

A typical method of social forecasting contains 44 operations, summarized in seven procedures:

1. Development of a research program (pre-forecast orientation): definition and refinement of the object, subject, purpose, objectives, structure, working hypotheses, methodology and organization of the study.

2. Construction of the initial (basic) model and its analysis: specification of the parameters of the "innovation field", formulation of alternative options, ranking them on the basis of priority.

3. Building a model of the forecast background and its analysis: consideration of external factors affecting the fate of innovation, determination of the possible consequences of innovation for the system (the standard forecast background contains seven groups of data: 1) scientific, technical and environmental, 2) demographic, 3) economic, 4) sociological, 5) sociocultural, 6) domestic political, 7) foreign policy).

4. Search forecast: variable direct "weighing" of the consequences of the planned innovation with the definition of a "problem tree".

5. Normative forecast: determination of possible ways to solve problems identified by predictive search, ideal (without taking into account the limitations of the forecast background) and optimal (taking into account these limitations) state of the system into which the innovation is introduced; correction of the "weighting" data of the consequences obtained in the predictive search.

6. Verification of the forecast: determining the degree of its reliability, accuracy and validity.

14 Basic methods of social forecasting.

Social forecasting as a study with a wide scope of objects of analysis is based on many methods. When classifying forecasting methods, their main features are distinguished, allowing them to be structured according to: the degree of formalization; principle of action; way to get information.

The degree of formalization in forecasting methods, depending on the object of study, may be different; methods for obtaining predictive information are ambiguous, they should include: methods of associative modeling, morphological analysis, probabilistic modeling, questioning, interview method, methods of collective generation of ideas, methods of historical and logical analysis, writing scripts, etc. The most common methods of social forecasting are the methods of extrapolation, modeling and expertise.

Extrapolation means the extension of conclusions concerning one part of a phenomenon to another part, to the phenomenon as a whole, to the future. Extrapolation is based on the hypothesis that previously identified patterns will operate in the forecast period. For example, a conclusion about the level of development of any social group can be drawn from observations of its individual representatives, and about the prospects of culture - from the trends of the past.

The extrapolation method is diverse - it has at least five different options. Statistical extrapolation - the projection of population growth according to past data - is one of the most important methods of modern social forecasting.

Modeling is a method of studying objects of knowledge on their counterparts - real or mental.

An analogue of an object can be, for example, its layout, drawing, diagram, etc. AT social sphere mental models are used more often. Working with models allows you to transfer experimentation from a real social object to its mentally constructed duplicate and avoid the risk of an unsuccessful management decision, all the more dangerous for people. The main feature of a mental model is that it can be subject to any kind of tests, which practically consist in changing the parameters of itself and the environment in which it (as an analogue of a real object) exists. This is the great advantage of the model. It can also act as a model, a kind of ideal type, an approximation to which may be desirable for the creators of the project.

The most practiced forecasting method is peer review. According to E.I. Kholostova, “examination is a study of a problem that is difficult to formalize, which is carried out by forming an opinion (preparing a conclusion) of a specialist who is able to compensate for the lack or non-systematic information on the issue under study with his knowledge, intuition, experience in solving similar problems and relying on “common sense ".

There are such spheres of social life in which it is impossible to use other methods of forecasting, except for expert ones. First of all, this applies to those areas where there is no necessary and sufficient information about the past.

When an expert assessment of the state of either a separate social sphere, or its constituent element, or its components, a number of mandatory provisions and methodological requirements are taken into account. First of all, an assessment of the initial situation:

Factors predetermining unsatisfactory condition;

Directions, tendencies, the most characteristic for a given state of the situation;

Features, specifics of the development of the most important components;

The most characteristic forms of work, the means by which activities are carried out.

The second block of questions includes an analysis of the activities of those organizations and services that carry out this activity. Evaluation of their activities is to identify trends in their development, their rating in public opinion.

Expert assessment is carried out by special centers of expertise, scientific information and analytical centers, expert laboratories, expert groups and individual experts.

The methodology of expert work includes a number of stages:

The circle of experts is determined;

Problems are identified;

A plan and time for action is outlined;

Criteria for expert assessments are being developed;

The forms and methods in which the results of the examination will be expressed are indicated (analytical note, “ round table”, conference, publications, expert speeches).

So, social forecasting is based on various research methods, the main of which are extrapolation, modeling and expertise.

The concept of the future. Methods of social forecasting.

Future 1. One of the key functions of philosophy is the prognostic function, the meaning and purpose of which is to make reasonable predictions about the future.

2. Throughout history, the question has been actively discussed in philosophy: is it possible at all for any reliable forecasting, vision of the future.

Modern Philosophy The answer to this question is in the affirmative: it is possible. In substantiating the possibility of predicting the future, the following aspects are distinguished: ontological; epistemological; Logical; neurophysiological; Social.

Ontological aspect lies in the fact that foresight is possible from the very essence of being - its objective laws, cause-and-effect relationships. Proceeding from dialectics, the mechanism of development remains unchanged until each qualitative leap, and therefore it is possible to "trace" the future.

Gnoseological aspect is based on the fact that since the possibilities of cognition are unlimited (according to the domestic philosophical tradition), and forecasting is also a type of cognition, then forecasting itself is possible.

Logical aspect- on the fact that the laws of logic always remain unchanged, both in the present and in the future. neurophysiological aspect is based on the possibilities of consciousness and the brain to advance reflection of reality.

Social aspect lies in the fact that humanity strives, based on own experience development, to model the future.

3. In modern Western science, a special discipline stands out - futurology. Its creator is the German scientist Flechtheim (40s of the XX century), who proposed the term. G. Parsons, E. Hanke, I. Bestuzhev-Lada, G. Shakhnazarov and others are among the world-famous modern scientists and philosophers who deal with the problems of forecasting the future.

4. A special type of forecasting is social forecasting, which deals with the foresight of the processes taking place in society.

Among them are processes in the field of: industrial relations; science and technology; education; health care; literature, construction; space exploration; international relations. This direction is called prognostics and differs from futurology in greater concreteness (it studies social processes, their future, and not the future in general).

Social Prediction Methods

Based on three ways to get information about the future. First, this extrapolation(-logical and methodological procedure for disseminating (transferring) conclusions made regarding a part of objects or phenomena to the entire set (set) of these objects or phenomena, as well as to any other part of them) into the future of observed trends, patterns, the development of which in the past and the present are quite well known. Secondly, this grade possible or desirable future state of a particular phenomenon. Thirdly, this modeling predicted events. All three methods stand out conditionally, because they form organic. unity: any extrapolation, logical. or statistical, is, in fact, a predictive estimate and a kind of predictive model. Any predictive estimate is, first of all, an extrapolation in one or another model representation, any predictive model includes extrapolation and an estimate. All forecasting methods are essentially different. combinations of elements of the above ways of obtaining information about the future. Several methods are general scientific, FOR EXAMPLE, forecasting Similarly. Predictive estimates of deductive or inductive, etc. Practically in the arsenal of M.s.p. includes all methods sociology, research - the study of documentary sources and literature, observation, polls population and experts, experiment staging and experiment after the fact, modeling schematic. and mathematical. Many methods are inter- or interscientific, used in a number of scientific. disciplines, for example, methods of regression or factorial analysis, full-time and part-time collective and individual surveys of experts, simple and formalized forecast scenarios, etc. Some methods are private scientific, that is, they relate only to k.-l. one scientific discipline, eg. population surveys in sociology, projective tests in psychology, etc. According to the accepted classification of forecasting methods (covering the methods of scientific-technical and socio-economic forecasting, without taking into account the specifics of agro-, hydrometeorological and a number of other natural-science forecasts), all methods are divided by the degree of formalization into intuitive (expert) and formalized (factual).

A forecast is a prediction, a prediction based on certain data. A plan is a plan of work planned for a certain period. Forecasting and planning are the conditions for the successful operation of any organization.

Forecasting allows you to reveal stable trends, or, conversely, significant changes in socio-economic processes, assess their likelihood for the future planning period, identify possible alternatives, accumulate scientific and empirical material for a reasonable choice of one or another development concept or planned decision.

Forecasting and planning methods have been enriched and improved at an accelerating pace since the 1970s. Two factors play a special role in this:

1) economic crises of the last quarter of the 20th century. forced economists and managers in different countries to find new adequate methods of management;

2) rapid spread information technologies and computer technology, these tools have made prospective analysis and forecasting available to the public.

Is it possible to foresee, predict the onset of crises? Is it possible to prepare for them or avoid them altogether? Is it possible to identify the factors that determine the success of the economic development of the state? How to act in order to achieve prosperity and success? It is almost always possible to answer these questions: the success of any business is half ensured by effective forecasting and planning.

Forecast is a scientific and analytical stage of the planning process. The forecast determines the possibilities within which realistic tasks of planning the development of the economy or the work of an enterprise can be set.

The relevance of the topic of the work is determined by the fact that forecasting is necessary for realistic planning of economic and social processes: it is impossible to develop an effective system of actions without short-term and long-term forecasts of the political and economic situation.

The aim of the work is to study the essence and methods of socio-economic forecasting.

To achieve this goal, the following tasks were solved in the work:

The essence, principles, functions of socio-economic forecasting are considered;

The classification is given and the main methods of socio-economic forecasting are described;

The issue of forecasting social phenomena based on the time series of Wolf numbers is considered.

The subject of this work is forecasting methods, their essence and classification.

Chapter 1. Essence and methods of social forecasting

1.1. Concept, classification of forecasts

Forecast is a probabilistic scientifically based judgment about the prospects, possible states of a particular phenomenon in the future and (or) about alternative ways and timing of their implementation.

A typology of forecasts can be built on various grounds, depending on the goals, objects, problems, lead time, nature, etc.

The problem-target criterion is fundamental: what is the forecast for? There are two types of forecasts:

1. Search (exploratory, trend, genetic). The search forecast answers the question: what is most likely to happen if the development trends of the object continue. The search forecast is built on a certain scale (field) of possibilities, on which the degree of probability of the predicted state of the object is then established.

2. Normative (target, regulatory). A normative forecast is a determination of the ways and timing of achieving the desired states of an object or phenomenon, taken as a goal, and answers the question: in what ways to achieve the desired. With normative forecasting, the same probability distribution occurs, but in the reverse order: from a given state to observed trends. A normative forecast is a probabilistic description of alternative ways to achieve the desired states of an object, including the development of measures to implement these states.

According to the object, forecasts are divided into:

1) social - determining future changes in:

A person, his needs, interests, social status, health, education;

In relations between social groups, layers;

The state of the social sphere;

2) economic are used for foresight general condition economy, industries, enterprises, changes in the structure of reproduction, in labor markets, demand for professions, in management;

3) political - defining changes in the alignment political forces, in the relations of social groups to parties and leaders, in political orientations; political forecasts are used to forecast election results and other political events;

4) scientific and technical - determining the dynamics of the productive forces, discoveries and inventions, the change of generations and models of technology, the change in technology;

5) environmental, allowing to predict the dynamics of natural processes, disasters, their consequences, areas of activity for the protection environment and reproduction of natural resources and others.

Forecasts can have a different lead time - from short-term (for example, daily, associated with stock fluctuations) to long-term (by tens) and ultra-long-term. The first are more detailed. The longer the forecast horizon, the greater value have theoretical studies and the duration of the retrospective (foundation time) of the forecast.

In countries with economies in transition, short-term forecasts are most in demand both by analysts and government agencies(government, parliament) at different levels of government.

Medium-term forecasts for the period correspond to the time of functioning of government bodies. An example of a long-term forecast is the results of population projections for the period up to 2050, according to which India will surpass China in terms of population.

The nature of the forecast is not the same. It can determine any one characteristic of an object (indicator) or be complex for an enterprise, city, region, country.

1.2. The concept of socio-economic forecasting

Forecasting is a science-based prediction of the most probable state, trends and features of the development of a controlled object in the prospective period based on the identification and correct assessment of stable links and dependencies between the past, present and future.

As Antokhonova I.V. notes, distinguishing feature forecasting lies in the fact that it substantiates the emergence of such processes and forms of the material and spiritual life of society, which in this moment inaccessible to direct perception, as well as verification in practice.

One of the important directions of forecasting community development is socio-economic forecasting - a scientific discipline that has as its object a socio-economic system, and the subject is the knowledge of the possible states of functioning objects in the future, the study of patterns and methods for developing economic forecasts.

Socio-economic forecasting is based on the achievements of science in the field of knowledge of the laws of development of society, clarification of trends in socio-economic and technological progress.

For predictable social objects, the intensity of the relationship between foresight and control can be so high that it can change the predicted state through actions taken based on managerial decisions. In other words, managerial decisions lead to “self-fulfillment” or “self-destruction” of the forecast. In prognostication, this is called the “Oedipus effect.”

An important role in improving economic forecasting, increasing the reliability of the developed forecasts, also belongs to the applied scientific discipline that studies the patterns and methods for developing forecasts for the development of objects of any nature - forecasting, including economic forecasting.

Forecasting is closely related to statistics and is largely based on statistical data and methods for studying mass phenomena.

Of particular importance at present is applied statistics, which adapts the methods of multivariate statistical analysis to the solution of socio-economic problems. In this case, the following tasks are solved: typology (classification) or the identification of classes that are homogeneous in a certain sense; reducing the dimension of the data space under study and restoring (forecasting) the values ​​of dependent indicators based on the values ​​of a certain set of independent features.

Econometrics is a scientific discipline that combines a set of theoretical results, techniques, methods and models designed to, on the basis of economic theory, economic statistics and mathematical-statistical methods to give a specific quantitative expression to the general patterns due to economic theory.

Thus, the above disciplines are closely related to each other, the essential point is the obligatory methodological component in the form of economic theory.

1.3. Main functions and principles of forecasting

We list the methodological principles that form the constructive basis for the development and use of applied forecasting methods:

1. The principle of consistency. This principle requires considering the object of forecasting as a system of interrelated characteristics of the object and the forecast background in accordance with the goals and objectives of the study.

This principle also implies the construction of a forecast based on a system of methods and models characterized by a certain hierarchy and sequence.

2. The principle of adequacy of the forecast to objective regularities characterizes not only the process of identification, but also the assessment of stable trends and relationships in the development of the economy and the creation of a theoretical analogue of real economic processes with their complete and accurate imitation.

3. The principle of alternative forecasting is associated with the possibility of developing the object of study and its individual elements along different trajectories, with different relationships and structural relationships.

Alternativeness comes from the assumption of the possibility of qualitatively different options for the development of the economy.

4. The principle of validity or reliability. A necessary condition for the development of a reliable forecast is the knowledge of the objective laws of the development of processes, the identification of stable trends on their basis. This knowledge should be based on a deep study of the achievements of applied development of forecasts. The implementation of this principle in practical research is ensured by the appropriate quality of the forecast and the assessment of the reliability and accuracy of the result.

5. The principle of observability. The choice of a specific forecasting method largely depends on the availability and quality of the information base (sufficient and reliable statistical data)

The main forecasting functions are:

Analysis of processes and trends;

Study of the objective relationships of socio-economic phenomena in the development of the object of forecasting in specific conditions in a certain period;

Evaluation of the forecasting object;

Identification of development alternatives;

Assessment of the consequences of decisions made;

Accumulation of scientific material for a reasonable choice of solutions.

When implementing certain forecasting functions, it is necessary to determine the approaches that form the basis of forecasting.

1.4. Forecasting methods and their classification

According to some scientists, there are more than 150 forecasting methods. There are much fewer basic methods, many of the "methods" rather refer to separate forecasting methods and procedures, or represent a set of separate techniques that differ from the basic methods in the number of particular techniques and the sequence of their application.

The forecasting method is understood as a set of techniques and ways of thinking that allow, based on the analysis of retrospective data, exogenous (external) and endogenous (internal) connections of the object of forecasting, as well as their measurement within the framework of the phenomenon or process under consideration, to derive judgments of a certain reliability regarding the future development of the object.

In many cases, none of the methods by itself can provide the required degree of reliability and accuracy of the forecast, but, when used in certain combinations with others, it turns out to be very effective - the advantages of one method compensate for the shortcomings of another, or they are used in development.

An objective need to combine different methods often arises when developing forecasts for the development of processes characterized by the presence of complex relationships. Using a combination of forecasting methods is one of the ways to solve the problem of forecast verification, which is considered as a generalized assessment of their reliability, accuracy and validity.

The coincidence of the prediction results obtained by different methods is one of the evidences of their reliability.

Although the choice and use of the method is the main step in the development of the forecast, they do not guarantee the final reliable results. The development procedure also involves other stages of activity, among which the following can be distinguished:

1. Predictive justification, i.e. formulation of goals, objectives, initial data on the structure of the object and the analyzed processes, main factors, relationships, development of preliminary hypotheses about the patterns of development, methods and organization of forecasting procedures.

2. Description of the external environment (forecast background), identification of external influences on the development of the facility and internal management, specification of development criteria and management parameters.

3. Development of a predictive model, i.e. determination of its structure and constituent elements, establishing relationships between them, which will allow us to trace the patterns of process change.

4. Develop, if possible, an alternative forecast based on the application of suitable forecasting methods.

5. Assessment of the reliability, accuracy and validity of the developed forecast, the consequences of its implementation. Comparison of forecast results with alternative forecast options.

7. Formulation of the task of developing a new version of the forecast, taking into account the analysis of the results obtained and the new information received.

From the standpoint of a general approach, a set of forecasting methods aimed at solving applied problems of analyzing the state of an object and predicting its development in the modern dynamic world can be systematized in the following classification (Figure 1).

According to the degree of formalization, forecasting methods are divided into intuitive and formalized. If the set of causal relationships is projected into the future, then the use of methods based on formalized thinking has advantages over intuitive methods.

Consider intuitive forecasting methods. They are used when the forecasting object is either too simple or so complex and unpredictable that it is practically impossible to analytically take into account the influence of many factors. The individual and collective expert assessments obtained in such cases are used as final forecasts or as initial data in complex forecasting systems.

Figure 1 - Classification of forecasting methods

Intuitive methods include:

1. Method "interview" - is an individual expert assessment, formulated impromptu without prior analysis of issues and therefore excluding ambiguous interpretation.

2. The analytical method is associated with the expression of an individual point of view of an expert in an article or analytical notes on the development trends of the phenomena and processes under study.

3. When constructing scenarios, a logical sequence of hypothetical events is established, connected with each other by cause-and-effect relationships; it is a model of the process, not just the end result.

4. The method of psycho-intellectual generation of ideas should be based on motivating creative motives, however, like all individual assessments, it is subjective. The final solution is determined by the analysis of expert data directly by the researcher.

5. The method of commissions represents the unification of the work of experts in the development of documents on the prospects for the development of the object of forecasting. Sociological surveys serve as an information base.

6. The "Delphi" method represents a series of successively implemented procedures aimed at preparing and substantiating the forecast.

7. The method of collective generation of ideas, called "brainstorming" or "brainstorming", differs from the "Delphi" method in the joint nature of obtaining a decision during a special meeting and subsequent analysis of its results. The method is recommended to be used in critical situations, characterized by the absence of real, fairly obvious options for the development of processes in the future.

8. If the "brainstorming" is primarily aimed at collecting new ideas, then the method of controlled generation of ideas is a method of exchanging opinions, as a result of which it is supposed to reach an agreement between experts.

9. The synoptic method is a consolidated, overview approach to the analysis of the object and the writing of separate scenarios for different areas, followed by their integration through iteration.

Formalized methods are divided by general principle actions into four groups: extrapolation (statistical), system-structural, associative and advanced information methods.

In the practice of forecasting economic processes, statistical methods have been predominant, at least until recently. This is mainly due to the fact that statistical methods are based on the apparatus of analysis, the development and practice of which have a fairly long history.

Let's consider extrapolation methods, which are one of the most common forecasting methods.

Extrapolation is the extension to the future of trends observed in the past. The simplest and most well-known is the moving average method, which performs mechanical alignment of the time series. The essence of the method is to replace the actual levels of the series with calculated averages, in which fluctuations are canceled.

For short-term forecasting purposes, the exponential smoothing method can also be used. The average level of the series at the moment t is equal to the linear combination of the actual level for the same moment and the average level of past and current observations.

Trend extrapolation is possible if the dependence of the levels of the series on the time factor t is found.

A model of a stationary process that expresses the value of the indicator y t as a linear combination of a finite number of previous values ​​of this indicator and an additive random component is called an autoregressive model.

The methods discussed above, with the exception of trend extrapolation, are adaptive, because the process of their implementation consists in calculating successive values ​​of the predicted indicator in time, taking into account the degree of influence of previous levels.

The morphological method is able to solve three types of problems:

How much information about a limited range of phenomena can be obtained using this class of techniques?

What is the complete chain of effects following from a particular cause?

What are all the possible methods and techniques for solving this particular problem?

The answer to the second question is the construction of a goal tree based on graph theory. The answer to the third question is provided by exploratory forecasting.

Works on system analysis are distinguished by the fact that they always offer a methodology for conducting research, organizing the decision-making process, an attempt is made to identify the stages of research or decision-making and suggest approaches to the implementation of these stages in specific conditions.

The methods of normative technological forecasting include matrix approaches used to check the agreement with various horizontally acting factors. Two-dimensional matrices provide a quick method for assessing the priority of one or another of the proposed options. This principle corresponds to the widely used SWOT analysis method in management, i.e. taking into account the weak and strengths object, threats and advantages in the external environment.

From the point of view of methodology, matrix methods include methods and models of game theory. They are used in forecasting socio-economic processes in the analysis of situations that arise as a result of certain relationships between the system under study and other opposing systems.

Statistical modeling methods include regression equations that describe the relationship between time series of independent features and effective features. Predictive levels are calculated by substituting the predictive values ​​of the trait-factors into the regression equation, which can be obtained, for example, on the basis of extrapolation.

Econometric models are a forecasting tool that takes into account the requirements of a systematic approach to an object and its quantitative characteristics. The area of ​​their application is macroeconomic processes at the level of the national economy, its sectors and industries, the economy of the territories.

Functional-hierarchical modeling represents the coordination of a distant goal with the actions (functions) that must be taken to achieve it in the present and future. Metric goal trees are used as decision aids and are called decision trees in this case.

Network modeling is widely used in normative technology forecasting. The critical path method, which is based on the use of network diagrams that reflect the various stages of each part of the project, and analyzes them in order to choose the optimal path between the initial and final stages, has become most famous. The criterion is cost or time. Network modeling uses a goal tree as an auxiliary tool.

The simulation method is based on the idea of ​​maximizing the use of all available information about the system. The goal is to analyze and predict the behavior of a complex system with many functions, not all of which are quantified. Simulation modeling has found wide application in predicting processes, the analysis of which is impossible on the basis of a direct experiment.

The possibility of a systematic use of similarity in the development of various objects underlies the method of historical analogies. Historical analogy has always played some conscious or unconscious role in forecasting.

The group of advanced information methods refers to technological forecasting and is associated with monitoring the latest research, results and breakthroughs in various fields of knowledge and evaluating the accumulated achievements. The methods are based on the property of scientific and technical information to be ahead of the implementation of achievements in production. For such activities, there are great opportunities in connection with high level development of information technologies.

Chapter 2

2.1. The concept of a time series of Wolf numbers

The Wolf number is one of the important numerical characteristics of the Sun's sunspot activity. The index was introduced in 1848 by the Swiss astronomer Rudolf Wolf.

Wolf, with the help of his combined sunspot index, which was called the Wolf numbers, built their time series from 1700 to 1848 and after that it is constantly updated, and in the 20th century with daily data.

Back in 1843, the outstanding German astronomer Heinrich von Schwabe first determined the periodicity of sunspot cycles at 10 years. In 1852, this figure was corrected by Wolf as the arithmetic mean of their period of 11.1 years, although in reality the cycle varies from 8.5 to 14 years between adjacent lows and from 7.3 to 17 years between highs.

For many years, Rudolf Wolf (1816-1896) collected data on sunspot observations. He has collected and verified both published and unpublished information since the invention of the telescope. As a result, he received a series of data starting from 1610. At the same time, he introduced the concept of the "relative number" of spots, which is now called the Wolf number. This number (W) is calculated using the following formula:

where f is the number of sunspots visible on the Sun;

g is the number of groups of these spots;

k is a normalization coefficient, depends on the observer and on the astronomical instrument used by him, it allows you to compare observations made under different conditions.

Wolf numbers usually range from 0 to 10 in years of minimum, and from 50 to 100 in years of maximum.

The so-defined Wolf number is called the relative Wolf number, since there is no concept of a universal, absolutely exact number of spots and their groups. Wolf numbers obtained from different observations are first compared with each other (using series of parallel observations), in order to then derive a normalizing conversion factor.

The meaning of the coefficient 10 when calculating the Wolf number is that the significance of the characteristic g (the number of groups of spots) is assumed to be 10 times greater than the significance of the characteristic f (the number of single spots). This coefficient was introduced by Wolf himself and is rather arbitrary.

But since it is more convenient to write down only the values ​​of the number W, and not its components, then in order to be able to compare the Wolf numbers over large time intervals, modern astronomers continue to use this particular weighting factor of 10. However, astronomers have long understood that the choice coefficient 10 is arbitrary, and they tried to find other numerical characteristics of solar activity that would not contain such arbitrary parameters. One of these characteristics is undoubtedly the total area S of sunspots visible on the Sun. Other characteristics of this kind have been proposed, but only one of them, the area S, suffices for us here.

Astronomers' studies have shown that if you build series of the values ​​of W and S, then there is a fairly strong correlation between them. Mathematically, for this, the cross-correlation coefficient was calculated, which has already been mentioned above (in the particular case of one time series, when autocorrelation is calculated - the correlation of the series with itself).

It turned out that this coefficient is always very close to 1, which indicates a fairly close relationship between these two values. In fact, it turned out that the areas of the spots are approximately proportional to the Wolf number. Substantially, this fact means that the information available in a number of values ​​of the Wolf number can be largely extracted from a number of values ​​of the area of ​​spots (and vice versa). Thus, astronomers have shown that they have at their disposal a correctly defined numerical characteristic solar activity and established its connection with a widespread characteristic - the Wolf number. As for the Wolf number, its correlation with many phenomena on Earth was clearly demonstrated in the works of A.L. Chizhevsky.

2.2. Social prediction based on Wolf numbers

If we compare the graphic representation of the identified historiometric cycles and subcycles with the spot formation curves, we can find the following pattern: cycles and subcycles, as a rule, begin in the years of extremums of the spot of educational activity or in the years adjacent to them.

At the same time, depending on whether the maximum or minimum of solar activity corresponded to a critical year, success or failure depended on the forces that entered the historical arena. Suffice it to note the following years and events:

1789 - the year of the beginning of the great French bourgeois revolution;

1905, 1917 - the years of Russian revolutions;

1928 - the year of the collapse of the Western financial system;

1937-1938, 1947-1949 - repressions of the Stalinist regime;

1986 - Chernobyl disaster;

1991 - the collapse of the Union, etc.

Figure 1 shows the observations of the relative Wolf numbers, averaged over a month, for the period from 1900 to 1924;

Rice. 1. Distribution of Wolf numbers in time

Table 1 - Analysis of the relationship between Wolf numbers and social phenomena

	Wolf number
	(average annual)	Revolution, coming to power of the Bolsheviks
1937-1938		Repressions of the Stalinist regime
March 1947		The "Truman Doctrine" was proclaimed, aimed at fighting the forces of socialism, the communist ministers were eliminated from the Belgian government
May 1947	(monthly average)	For the first time the world was on the brink of nuclear war; Removed communist ministers from the governments of Italy and France and banned the Communist Party of Brazil.
late October - early November 1956		Israeli-Arab war started which did not completely subside even after 42 years; The population of Hungary began to protest against the existing regime authorities and Nikita Khrushchev, with the consent of the members of the Presidium of the Central Committee of the CPSU, ordered the immediate suppression of the uprising in Budapest
May 1968	(monthly average)	in Paris, in support of the demands of the students, mass unrest began with erecting barricades
August 1968		Suppression of the "Prague Spring" by Soviet tanks
December 1979		Start of the war in Afghanistan
	(average annual)	The Armenian-Azerbaijani war for Karabakh began; earthquake in Armenia
		Start of the war in Yugoslavia
		August coup, the beginning of the collapse of the USSR

Thus, catastrophic socio-political events in the reference years of the maximum increase in solar activity moved geographically depending on the endogenous socio-psychological readiness of societies in certain areas of the Earth to participate in bifurcation events at the peak of solar activity.

At the same time, the mechanism of influence of the increase in solar activity on drastic changes in the socio-psychological environment in one or another region of the Earth is very similar to the mechanisms that work in its depths, causing earthquakes.

The method of analyzing the influence of the Sun's sunspot activity on the social activity of society is retrospective and unacceptable for long-term forecasting, since the state of the Sun can only be judged by actual data at each individual moment of time.

Conclusion

Forecasting allows you to reveal stable trends, or, conversely, significant changes in socio-economic processes, assess their likelihood for the future planning period, identify possible alternatives, accumulate scientific and empirical material for a reasonable choice of one or another development concept or planned decision.

Socio-economic forecasting methods are a set of techniques and ways of thinking that allow, based on the analysis of retrospective data, exogenous (external) and endogenous (internal) relations of the object of forecasting, as well as their measurements within the framework of the phenomenon or process under consideration, to derive judgments of a certain reliability regarding it ( object) future development.

Intuitive forecasting methods are used in cases where it is impossible to take into account the influence of many factors due to the insignificant complexity of the forecasting object. In this case, expert estimates are used. At the same time, individual and collective expert assessments are distinguished.

The group of search methods includes subgroups: extrapolation and modeling, mapping, scripting, forecasting by analogy. The first subgroup includes methods: least squares, exponential smoothing, moving averages. To the second - structural, network and matrix modeling.

Normative forecasting methods consist in determining the necessary and sufficient means to achieve the possible state of the object under study and answer the question: “what will happen?”, “what do we want to see?”, “by what means to achieve this?”. Normative methods include a tree of goals, morphological models, block diagrams.

In the second part of the work, a method for analyzing the influence of the sun's sunspot activity on the social activity of society is considered. The influence of the increase in solar activity on drastic changes in the socio-psychological environment in one or another region of the Earth has been established.

This one is retrospective in nature and is unacceptable for long-term forecasting, since the state of the Sun can only be judged by actual data at each individual moment in time.

Literature

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2. Antokhonova I.V. Methods for forecasting socio-economic processes: Textbook. - Ulan-Ude: Publishing House of the ESGTU, 2004. - 212 p.

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6. Mamikonov A.G. Decision making and information. M.: Nauka, 2002. - 275 p.

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9. Tein A. G. Land Informatics. Yekaterinburg: Ural University Press. Teacher's House Publishing House, 2002. - 254 p.

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15. http://www.yax.su/finlab/ir170/21/index.shtml

1. Expert review.

2. Extrapolation.

3. Modeling.

4. Analogies.

5. Writing scripts.

6. Complex methods.

It is easy to see that, in essence, forecasts are based on intuition specialist scientist, analogies since already known phenomena and processes and, finally, on a straight line extrapolation some kind of processes in the future. It is clear that in the field of complex phenomena, when it comes to specific predictive estimates, all this gives only a relatively limited effect.

Under the intuition of a scientist in this case it does not mean the mysterious area of ​​the subconscious, but a flair, a guess based on accumulated observations, on a person’s life experience, which allow him to judge in general terms about the prospects of a phenomenon well known to him. Thus, an experienced architect, looking at the project of a building under construction, intuitively feels the main positive and negative aspects of the consequences of the implementation of this project. An experienced economist, having become acquainted with the peculiarities of the organization of labor in an institution or enterprise, is able to immediately give a general assessment of the prospects for the work of this institution or enterprise.

The analogy with phenomena and processes that have already taken place in reality helps to make such forecasts more detailed and accurate, to include more clearly defined alternative options. So, for example, an engineer can recall from the experience of the past what price one has to pay for the technical lack of culture of other business executives. And vice versa, referring, for example, to the positive experience of cleaning industrial gases will make it possible to refine the forecast by referring to the conditions under which undesirable consequences can be avoided.

Finally, extrapolation is essentially the basis of any forecast. Predicting, we always mentally continue something into the future. The simplest example: in 1900, 1500 million people lived on Earth, in 1950 - 2500 million, in 1960 - almost 3000 million. Every year the population of the Earth increases by 70 million people (two percent of the total existing population). At this rate of growth, the number of people on Earth will double approximately every 35 years. Continuing mentally this line of development into the future, and you will receive 6-7 billion by the year 2000, and by the 30s of the 21st century. - 12-14 billion, etc.

All of these are proven methods. They successfully "worked" in the past and will serve people in the future. They cannot be neglected. But only for any detailed long-term forecasts, and even in difficult modern conditions, they are clearly not enough. Intuition fails all the time. Any analogy is very relative and without significant adjustments can lead to erroneous conclusions. As for extrapolation, it is capable of producing an effect only when considering not especially complex process and even then over a relatively short period of time.

Try to continue the process of world population growth into the distant future. You'll get a hundred people for each square meter the earth's surface, and this hundred will continue to double at an increasing pace. No projects of settling in space will change the situation: after all, we are talking about the appearance every year of new tens and hundreds of billions of people. It is clear that such a process simply cannot go on, not only indefinitely, but in general for any length of time. Ahead, apparently, significant changes in the very course of the predicted process.

In the same way, the growth in the number of students or, say, researchers, cannot go on infinitely at the same pace as now, otherwise the entire adult population of the Earth would become them! The amount of information (everything from books and newspapers to radio and television broadcasts) cannot increase at the same pace. Now this volume is doubling every 10-15 years. But after all, the ability of a person to “absorb” information is not unlimited! It is quite obvious that some serious qualitative shifts and changes in current processes are ahead.

Is it possible to foresee such qualitative changes, given the extremely complex nature of social phenomena? Yes, you can. But only if we take into account the need for a stochastic approach to such phenomena, use the already accumulated extensive experience in predicting natural, biological, and technical processes.

Just 10-15 years ago the number special techniques for predicting social phenomena was measured in units. Now there are more than a hundred of them only for forecasts in the field of science and technology. The number of special techniques is measured in hundreds. Within the framework of this work, it is impossible even to briefly list all the available developments. Moreover, it is impossible to give any detailed description of the numerous groupings of techniques. Their complexity can be judged from one of the attempts to classify the methods of scientific and technical forecasting alone. Therefore, referring the reader to the specialized literature, we confine ourselves to general overview the main directions in the field of methods of forecasting social phenomena. These areas include peer review, extrapolation (more precisely, interpolation and extrapolation) and modeling. A special area is made up of forecasts based on the analysis of patents. Let us dwell briefly on these directions.

Expert review prospects for the development of a particular process by individual specialists-experts or collective expert assessment. For such an assessment, a questionnaire is used, i.e., obtaining answers from experts to the questions contained in the questionnaires about the future states of the predicted objects. Sometimes there are surveys large groups experts on a certain program, in several rounds, and then, based on the averaging of the opinions of specialists, the most probable answer to the questions posed is displayed. One variation of this technique is Delphi method(Method of the Delphic oracle), which involves the compilation of special questionnaires, a complex procedure for interviewing specialists, processing the data obtained on electronic computers. The Delphi method is based on a consistent individual survey of experts and reducing their opinions to a single one by averaging them. Typically, expert responses are anonymous; answers are received from the questionnaire; the exchange of opinions takes place through the person conducting the survey of experts. The group opinion is the result of combining the opinions of experts in the last round of the survey.

Of course, the forecasts obtained through an expert survey are based not so much on an objective study of the current real situation and objective data on its development trends, but on the already accumulated knowledge and intuition of specialists, i.e., on the identification of objective scientific truth with the opinion of the majority of the surveyed specialists, whose knowledge themselves are limited. In this case, they try to get a forecast without an in-depth study of the object, based only on collecting the opinions of specialists about the future, on identifying the prevailing (averaged) assessment. Therefore, like all others, this method has limited opportunities in developing reliable scientific predictions.

Extrapolation. Suppose that in the first year the n-th plant produced 100 cars, in a year - 200, in another year - 300, and in 5 years - 500. The question is: how many cars did this plant produce in the fourth year of its existence?

We build a digital series: 100-200-300-?-500. Having peered into it, we answer without any complicated calculations: most likely 400. Life, as a rule, confirms this kind of conclusion. Why? Because there is a certain regularity in the construction of a series, and if this regularity is revealed, then it is not difficult to determine the quantitative value of each intermediate point even in very complex series - to perform interpolation, as this kind of mathematical action is called.

It would seem, what relation can interpolation have to forecasting?

Remember the Periodic system of elements of D. I. Mendeleev. These are also series of numbers - atomic weights of elements arranged in the strictest pattern according to the charge of the nucleus of their atoms: sodium (22.9) - magnesium (24.3) - aluminum (26.9) - silicon (28.0) - phosphorus (30 ,9), etc. If zinc (65.3) and arsenic (74.9) are known in this series, then two more elements with atomic weights of approximately 68 and 72 should be located between them. Having made this kind of interpolation, D. I. Mendeleev suggested the existence of two, then unknown chemical elements. He named them ekaaluminum and ekasilicon, predicting in general terms their chemical properties. A few years later, gallium (69.7) and germanium (72.6) were indeed discovered, the properties of which corresponded to ekaaluminum and ekasilicon (in total, Mendeleev pointed to four elements discovered later). It was one of the most brilliant predictions in the history of science.

It is clear that such an approach can produce an effect not only in the natural sciences. Such interdependence (correlation) between various phenomena and processes reveals more complex relationships than direct cause-and-effect ones. They are called correlated. Special equations (the so-called correlation and regression equations) have been developed to predict some phenomena or processes based on the data of other phenomena that are in a correlative relationship with the first. If there are many such phenomena, multiple regression equations are used, which allow quantifying the predicted process by a special combination of initial values. All this greatly increases the efficiency of the forecast.

But the methods mathematical statistics are not something like a universal master key for forecasting. They are usually used in conjunction with other methods. As an example of such integrated approach can be called the method of forecasting using envelope curves proposed by R. Ayres, an employee of the Hudson Institute (USA). Briefly, it boils down to the following. The most characteristic parameters of the system are determined, the analysis of changes in which makes it possible to predict the course of changes in the entire system as a whole. For each parameter, a development curve is constructed for years in the past and present, which can be extrapolated into the future. An analysis of the system's past can give good model its future - writes the author of the technique. By extrapolating the envelope curves according to the most characteristic current values ​​of the system parameters, we automatically take into account the continuous improvement of the system. Of course, it is difficult to take into account the effects of rare and extraordinary discoveries, but the sequence and continuity of ordinary improvements will be taken into account with a fairly good approximation. According to the author of this technique, it allows you to reduce the likelihood of deviations caused by shortcomings. modern technology forecasting, makes it possible to foresee the limiting values ​​of the predicted parameters, increases the degree of stability of the forecast.

Modeling. A model in the broad sense of the word is a simplified image (scheme, description) of a phenomenon or process for the convenience of analyzing the latter. According to the layout model, for example, of a planned or existing residential area, it is easier to understand its advantages and disadvantages. According to the model-scheme or map of the city, it is easier to develop, for example, a system of urban transport. If the map is accompanied by a description or mathematical calculations relating to the same urban transport (these are also models), the accuracy and quality of the developments increase even more.

Models are successfully applied in many sciences. In form, they are very different: subject (for example, layouts, they are called models in everyday life), physical (what is usually called a working model), logical (description of an object), mathematical (description of it using a system of equations), etc. . If we talk about social phenomena, then it is especially convenient to model them logically (for example, by analogy with a similar, but easier to analyze phenomenon, or a scenario scheme that simply reproduces the main points of the process) or mathematically (for example, in the form of quantitative characteristics of the process, in in the form of a system of equations that reproduce the functioning of certain phenomena). The most complex types of models are built on such a vast and complex array of information that, as a rule, they can be operated only with the help of electronic computers.

Modeling has long been successfully used in concrete economics and sociology. There are no reasons that would prevent its equally successful use in social forecasting. True, in the latter case we are talking about a special type of social models - about predictive models, that is, about modeling not existing, but expected, upcoming phenomena and processes. Predictive models have their own specifics, and their development is characterized by significant additional difficulties. But in principle, this is nothing more than a variety of social models that are generally built using the same methods and are capable of producing a fairly high effect in the course of scientific research.

Generally speaking, a model is an organic component of any forecast. We begin any forecast with the fact that we mentally transfer (continue) some phenomenon into the future, and end with the fact that we reproduce this phenomenon in the future, if necessary, in a more or less simplified form - in the form of a model, as long as in reality such a phenomenon still exists. does not exist. In other words, any forecast essentially always starts with an extrapolation (in the broad sense of the word) and ends with a predictive model. But in this case, we are not talking about predictive models in general, but about modeling as one of the main areas of social forecasting methodology. Typically, the full predictive modeling process includes:

1. Establishment of the task and determination of the lead time of the model (for so many years ahead).

2. Establishment of the initial data necessary for the development of the model (construction of the initial model).

3. The main operation is the calculation of the change in these data by the lead time according to the observed and expected trends in their development (usually using the methods of interviewing experts and (or) extrapolation).

4. Determination of the minimum and maximum, as well as the most probable or desirable levels of change and, in accordance with this, the establishment of the main variants of the model.

5. Possibly more detailed description one, several or all options by a system of equations or in any other way.

6. Refinement of the developed development by attracting additional data in order to correct possible errors.

7. The development of the so-called predictive scenario of the model, i.e., the determination of the features of its functioning under certain (specially stipulated) conditions of the lead time. In other words, the transformation of a static model (or a set of models) into a working system.

8. Manipulation ("game") with elements of this kind of operating functional model by simulating its functioning (on a computer or manually) with various combinations of elements in accordance with the expected changes in the situation. Often this stage of work is called modeling in the narrow sense of the term.

9. Prediction on the basis of the received data of certain changes in the predicted object. This is what model-based forecasting is - the end product of predictive modeling.

10. Determination of the degree of reliability of the forecast, its adjustment and appropriate recommendations for planning, programming, design, management in order to improve their scientific level. This, in fact, is the practical effect of all the operations performed.

The last stage deserves special attention, since predictive modeling of complex social phenomena is a laborious and expensive thing. According to American experts, it takes an average of 2 to 6 years to build a full-scale model of socio-economic or military-political processes within the country (ie, the development of several hundred complex equations).

Any model characterizes the real object only approximately; the degree of this approximation depends on the type of model used, on the methodology and technique of modeling tools. In their form, models of social systems can act as verbal descriptions in terms of humanities like graphs, diagrams, mathematical and cybernetic systems.

Models of social systems can be divided into the following main types: statistical models, i.e. reflecting the state of social systems at a certain point in time; simple dynamic models reflecting not only the structure, but also the process of functioning of social systems; complex dynamic models reflecting not only the structure and process of functioning, but also the process of development of social systems, i.e. the process of their qualitative change. Thus, the model can reflect the objective structure of the social system, as well as the patterns of its functioning and development.

It should be noted that modeling is closely related to experiment. The specificity of a model experiment in comparison with a conventional experiment is that the process of cognition of an object includes an intermediate link-model, acting, on the one hand, as a means, and on the other, as an object of experimental research, replacing a "replacement" object of research. Due to this, the possibilities of experimental research are greatly expanded, since many objects can be reproduced and studied on models.

Analogies. Very common in social forecasting is the method of analogy, i.e., comparing the predicted process with something similar to those social processes that took place in the past. The analogy method, like the extrapolation method, may not take into account the possibility of transition to another stage of qualitative changes occurring in the system.

Writing scripts. One of the methods of social forecasting is the method of compiling scenarios. Scenarios are a descriptive reproduction of the alleged future picture of the world as a whole or various areas of public life in a particular country or area of ​​activity. Scenario preparation usually includes a description of the logical sequence of events and processes in order to identify development alternatives, prospects and possible options for changing social systems. Thus, the scenario focuses on those causal relationships that the predictor considers most important.

When developing a scenario, various goals can be set, in particular, the assessment of the social situation, the identification of possible options and directions for its development, the identification of some probable accidents and the consequences of making certain decisions, as well as different ways actions in various social situations. Thus, the scenario is designed to help find answers to questions about how the social situation can develop and what opportunities should be used at various stages of its development in order to accelerate the onset of some events and prevent others.

Complex methods. Naturally, we could only mention those methods of social forecasting that seem to be the most general and indicative. In addition to them, there are dozens of others, each of which can be effectively used for the same purposes. For example, approaches are proposed that use the "black box" principle (analysis of the ratio of the "input" and "output" elements of a functioning system), the principle of "decision matrices" (system tables characterizing the state of various phenomena and processes), the principle of accounting for "accompanying events" (prediction of one process based on the results of another, already observed), the principle of "trial and error" (successive approximation from a priori plausible proposals to an accurate forecast) and many others.

We do not consider them not only because it would lead away from the topic of this section, but also because modern specific forecasting methods are, as a rule, complex in nature, covering a significant part of the listed (and not listed) approaches and reflecting in one way or another at least all the main directions of the techniques mentioned above.

The complex nature of the methods is confirmed, for example, by the fact that the expert assessment in the forecast is organically combined with extrapolation and modeling, the development of a predictive model usually includes expert assessments and extrapolation, etc. The rapid development of forecasting, which began several years ago, is precisely due to the effective use of the entire range of methods. It turned out that if you combine different forecasting methods in such a way that they seem to reinforce each other, giving a cumulation effect, then you can achieve a significant increase in the accuracy, range and reliability of the forecast.

In the very general view principle model of this kind of complex methodology as follows:

1. The theoretical concept of the forecast and a description based on it of the initial situation (mainly by means of questionnaires with the final compilation of the initial model of the predicted sawing or process).

2. Identification and modeling of the leading development trends (mainly by extrapolation techniques).

3. Compilation of options for a predictive model tied to a specific time.

4. A survey of experts to clarify the developed models (again, a survey).

5. Identification of specific discrepancies between the variants of the model.

6. Formulation of problems, the solution of which is necessary for the maximum convergence of the most probable and desirable (optimal) model.

7. Another survey of experts to refine the proposed model.

9. Compilation of post-probability forecasting models (modeling the likely consequences of the implementation of the recommended plans, programs, projects, decisions).

10. Again, a survey of experts to assess the forecast as a whole. Identification of errors, shortcomings, contradictions, undesirable consequences, proposed recommendations, etc.

11. Critical revision of the forecast in accordance with the comments received and the accumulated experience.

12. Again, a description of the situation, the identification of new trends, and so on without end, i.e. entering a new cycle of forecasting, because the forecasting process can and should be endless, continuous, only this allows you to conduct predictive research systematically, makes it possible to constantly improve them scientific level and hence efficiency.

But this is, so to speak, an ideal model, fully implemented in practice. As for practically applied methods of this kind, as an example, we can refer to the PATTERN system (according to initial letters: Planning Assistance Though Technical Evaluation of Relevance Numbers - the rationale for planning through a quantitative assessment of scientific and technical developments), proposed by specialists from the American company Honeywell. Briefly, it boils down to the following:

· expert assessment of the factors influencing the development of the projected object, identification of the main development trends by extrapolation; preliminary models of development prospects;

development of a hierarchy of goals, problems and subproblems, the solution of which is necessary to achieve them (the so-called "tree of goals");

· development of the current model (scenario) of possible development taking into account the data of the "goal tree";

· expert assessment of the relative significance of the predicted phenomena with an indication of the possible period of their implementation (according to a specially developed scale of significance coefficients);

· determination of ways to solve problems, sub-problems and sub-sub-problems of the "tree of goals" with an assessment of the degree of their complexity on a special scale of coefficients;

analysis of the "mutual usefulness" of the solution various problems and assessment on a scale of "utility factors" that determine the degree of applicability of solving problems of a certain type to similar or related problems;

· final, generalizing assessment, consolidated forecast and recommendations for the development of plans, projects, decisions.

The general logic of forecasting according to the PATTERN system looks something like this. Based on the scenario, areas of interest are determined - political interests, Scientific research, development work. In accordance with the established goals for each area, the necessary measures are developed and the levels of tasks to be solved are outlined:

a) determining the purpose of the forecast;

b) an assessment of the possible timing of the implementation of the intended prospect;

c) comparison of the scientific and technical level to date and to the date of lead in the forecast;

d) the same comparison with other countries of the world;

e) determination of the expected impact of some areas of scientific and technological progress on others;

f) assessment of possible social consequences;

g) definition necessary conditions for the implementation of predicted prospects (new forms of economic relations and structures, methods of stimulation, etc.).

h) stage of implementation of forecasts; at the same stage, the forecasting methods and the results obtained are tested for the first time, the thoroughness of the justification of all the key positions of the forecast is checked.

The stage of implementation of forecasts to a decisive extent ensures the possibility of using forecast data in planning and directive decisions. It is at this stage that scientific and technical forecasts practically "join" with a broad system of socio-economic forecasting.

social forecasting- this is one of the methodologically most complex forms of studying the prospects of processes and phenomena. In the natural sciences, forecasting is used to prepare for the consequences of a given phenomenon. For example, the identification of a high probability of an earthquake or is followed by informing and evacuating people outside the relevant territory. social forecasting are social processes, the outcome of which can be influenced, therefore the value of this type of research of prospects is not only in preparation for future circumstances, but also in the ability to model them.

In practice, apply following methods social forecasting:

Method of expert assessments

This method consists in collecting and studying the opinions of experts on the prospects of the studied social phenomenon. The effectiveness of this path is determined by the competence of the experts, the correctness of the questions put to them and the quality of processing the answers received.

The method of the Delphic oracle - a kind of method of expert assessments - is distinguished by a complex scheme for questioning experts: to exclude the influence of the group on the opinion of each specialist, the names of other qualified respondents are not disclosed to the experts, each answers questions independently. Next, the responses are analyzed and the dominant position is determined. After that, the respondents receive the same survey, the arguments of specialists whose opinions are very different from the majority, and the opportunity to change their position. The procedure is repeated until a consensus is reached.

The main advantage of the method is the exclusion of group influence on individual opinion, since it cannot be implemented until a consensus has been reached.

This method can be compared with the last elections. The decision was made by an anonymous vote from the third time. It is obvious that during the elections, none of the candidates managed to perform a “good deed” that could change the opinion of voters. According to custom, the procedure cannot be completed until one of the candidates receives 77 votes. It is logical to assume that long-term social forecasting by the Delphi method is similar to the definition of "hospital average temperature".

Social Modeling. Basic moments

Social forecasting can be done through mathematical modeling. This method allows you to consider many options for the development of events in their correlation with various factors. As in the case with here, there are some difficulties with long-term forecasting. But the advantage of this method is that the expert makes a conclusion, guided not only by his own judgments, but also by the results of "machine" data processing - the variety of options for the future object under study.

extrapolation method

The advantage is the identification of patterns of the phenomenon under study based on the analysis of its history and the consideration of these data in the forecasting process. Social forecasting through extrapolation is the use of complex formulas that allow you to achieve valuable results, which, however, do not guarantee one hundred percent reliability.

Social forecasting is an effective tool for managing social processes in the hands of those who have the opportunity to influence them.