Forecasting includes the following steps. Stages of forecasting. Forecasting methods in politics

Appendix 1. METHODS OF STATISTICAL ANALYSIS AND FORECASTING IN BUSINESS

3. Main stages of forecasting and types of forecasts

The construction of a forecast and the associated construction and experimental verification (verification) of a probabilistic-statistical model are usually based on the simultaneous use of two types of information:
- a priori information about the nature and substantive essence of the analyzed phenomenon, presented, as a rule, in the form of certain theoretical laws, restrictions, hypotheses;
- source statistics characterizing the process and results of the functioning of the analyzed phenomenon or system.

The following main stages of forecasting can be distinguished.

1st stage(staged) includes the definition of the final applied goals of forecasting; a set of factors and indicators (variables), the description of the relationships between which we are interested in; the role of these factors and indicators - which of them, within the framework of the specific task, can be considered input(i.e. fully or partially regulated, or at least easily registerable and predictable; such factors carry a semantic load explaining in the model), and which weekend(these factors are usually difficult to predict directly; their values ​​are formed, as it were, in the process of functioning of the modeled system, and the factors themselves carry a semantic load explained).

2nd stage (a priori, premodel) consists in the analysis of the content essence of the process or phenomenon under study, the formation and formalization of the available a priori information about this phenomenon in the form of a number of hypotheses and initial assumptions (the latter should be supported by theoretical arguments about the mechanism of the phenomenon under study or, if possible, experimental verification).

3rd stage (information and statistical) is to collect the necessary statistical information, i.e. registration of the values ​​of the factors and indicators involved in the analysis at various time and (or) spatial cycles of the functioning of the simulated system.

4th stage (model specification) includes a direct derivation (based on the hypotheses and initial assumptions adopted at the 2nd stage) of the general form of model relationships that connect the input and output variables of interest to us. Speaking of general view model relationships, we mean the fact that at this stage only the structure of the model will be determined, its symbolic analytical record, in which, along with known numerical values ​​(represented mainly by the initial statistical data), there will be quantities whose meaningful meaning is determined, and numerical values ​​- no (they are usually called model parameters, the unknown values ​​of which are subject to statistical estimation).

5th stage (identifiability study and model identification) consists in carrying out a statistical analysis of the model in order to “adjust” the values ​​of its unknown parameters to the initial statistical data that we have. When implementing this stage, the "forecaster" must first answer the question, is it possible in principle to unambiguously recover the values ​​of the unknown parameters of the model according to the available initial statistical data with the structure (method of specification) of the model adopted at the 4th stage. This constitutes the so-called problem of identifiability models. And then, after a positive answer to this question, it is necessary to decide already identification problem models, i.e. propose and implement a mathematically correct procedure for estimating unknown values ​​of model parameters using the available initial statistical data. If the problem of identifiability is solved negatively, then they return to the 4th stage and make the necessary adjustments to the solution of the model specification problem.

6th stage (model verification) consists in using various procedures for comparing model conclusions, assessments, consequences and conclusions with reality. This stage is also called the stage of statistical analysis of the accuracy and adequacy of the model. If the results of this stage are pessimistic, it is necessary to return to stage 4, and sometimes to stage 1. If the model verification stage gives positive results, then the model can be directly used to build a forecast in accordance with the above general scheme (10).

In the description of the content of the 1st stage of the forecasting procedure, it was, in particular, the need to determine the final applied goals of forecasting. This implies, in particular, the definition of the required forecast type. The type of forecast is determined by two factors:
forecast horizon and
hierarchical level of the predicted indicator.

According to the forecasting horizon, forecasts are divided into short-term(1-2 ticks of time ahead), medium-term(for 3-5 bars) and long-term(more than 5 clock ticks ahead).

According to the level of the predicted indicator, it is advisable to single out macro-, meso- and micro forecasts. Everything related to the forecasting of indicators characterizing the activities of firms, companies and enterprises belongs to the micro level. Meso- (regional and sectoral levels) and macro forecasts are used to describe the external environment.

It should be emphasized that in reality a businessman, a leader enterprises can, of course, to successfully conduct business and not own the methods of building mathematical forecasting models. However, in the conditions of tougher competition, knowledge of these methods sometimes provides a businessman and his business with no less significant competitive advantages than winning a certain market share or getting a good loan.

Previous

A huge number of forecasts developed in various sciences in economics, social sphere, ecology, necessitates their typology, classification and systematization according to characteristic features. There are various classifications of geographic forecasts depending on approaches, time depth (lead time), territorial coverage, and other features. There are search, normative and integral forecasting. the main objective search(genetic, resource) forecasting is to find out the ways of development of an object or process while maintaining existing trends. At the same time, it is assumed that the observed trends cannot be changed by a volitional decision. Normative forecasting is based on determining the optimal option for the development of an object in the future within the framework of scientifically substantiated needs and norms. Its task is to determine the ways and timing of achieving the desired state of the object in the future, in accordance with the goal. integral forecasting arose at the intersection of these two types of forecasting and is used to develop targeted integrated programs for the development of districts and cities.

In terms of scope, geographic forecasts can be global, regional and local.

According to the content, private and integral geographic forecasts are distinguished. Particular forecasts are necessary to solve such problems as substantiating the involvement of natural resources in the economic turnover, forecasting the development of intersectoral complexes and territorial socio-economic systems of various hierarchical ranks, improving the system of population resettlement, internal and external economic ties, the development of plans for the social development of cities and regions, the rationale for recreational activities, etc. The totality of all particular geographic forecasts is an integral forecast.

The development of geographic forecasts is a sequence of several logically interrelated stages: 1. Setting the goal and objectives of the study. 2. Determination of the chronological and territorial scope of the study. 3. Collection and systematization of all information about the functioning and development of territorial systems and their functional subsystems. 4. Building a "tree of goals", choosing forecasting methods, identifying limitations and inertial aspects of the development of a predicted object or process. 5. Development of private geographic forecasts: natural resources, territorial organization productive forces, intersectoral complexes, population and settlement systems, etc.

The system of main stages of geographic forecasting includes theoretical and Information Support forecast, analytical work and choice of method, as well as ensuring the reliability of the forecast (verification of the forecast).

The theoretical support of the forecast is based on the latest achievements in geography. It is based on the doctrine of geosystems that are formed under the influence of natural and anthropogenic factors. These factors determine the dynamism, stability and nature of interrelations in territorial systems. When they are violated, irreversible changes occur in geosystems, the study of which has great importance for forecasting.

The information support of the forecast is based on the collection of information on theoretical issues of forecasting in relation to a specific object and obtaining specific information about it. Information materials can be obtained both as a result of special studies (expeditionary, stationary, semi-stationary), and in statistical bodies, in scientific reports, literature, etc.

The reliability and accuracy of the forecast depend on the level of development of theoretical knowledge about the predicted object, the degree of completeness of the information used, and the correct formulation of the problem of choosing a research method. To verify the forecast, the following approaches are used:

1. Deeper knowledge of the structure, functions and relationships of the object of forecasting, mechanisms for the formation and development of natural and socio-economic processes and phenomena.

2. Verification of forecasting methods and techniques on similar objects.

3. The use of several methods and techniques for making a forecast to establish the degree of coincidence of the forecast results.

4. Splitting the actual series of observations of the predicted process into two parts in order to use one part to predict the other.

5. Using the method of expert assessments.

6. Synthesis of particular geographic forecasts.

7. Development of the main forecast options.

8. Building a preliminary forecast.

9. Examination and preparation of the final forecast.

10. Forecast correction.

11. Using forecasting results to solve theoretical and practical tasks geography.

An important task of geographical forecasting is search for stable links (structural, functional, spatial, temporal, etc.) between the components of geosystems. This is due to the multidimensionality of the forecasting object - the territorial system of a certain region. To overcome the barrier of multidimensionality, it is necessary to use the following approaches of general scientific forecasting: 1) decomposition techniques, i.e., breaking down the whole into its component parts, which are more simple and accessible for research; 2) the use of simple indicators that reflect the most important predictive factors or their sum; 3) aggregation, i.e. combining several indicators into one. Therefore, in Geographical forecast simultaneously applies the synthesis and analysis of natural and socio-economic processes and phenomena.

Methods of geoforecasting

The purpose and object of the forecast determine the choice of its methods. Under geographical forecasting methods understands the methods of theoretical and practical development of the forecast. There are a large number of economic and geographical forecasting methods, and their number is constantly growing. The choice of one or another forecasting method depends on the purpose of the study, the information base, and the nature of the processing of the initial information.

Therefore, certain methods correspond to each specific study and the stage of forecasting. These methods can be divided into three groups: general scientific(analysis and synthesis, induction and deduction, extrapolation and interpolation, analogy, experiment, etc.), interscientific(simulation, operations research, statistical, expert assessments, etc.) and private scientific(assessment of the prospects of the geographical location, functional zoning of the territory, cartographic, etc.). Consider the most common methods of geographic forecasting.

logical methods. These methods are based on the application of a certain sequence of mental operations. Their wide distribution in the study of territorial systems is due to their great complexity, the variety of relationships between natural and economic systems, a long time for the formation of forecast objects.

General scientific logical methods include methods of induction and deduction. by induction method causal relationships between objects and phenomena are established. The study is carried out from the particular to the general by determining the similarities and differences in the development of the object. In forecasting, this method is used to obtain probabilistic judgments with an insufficient information base, i.e., in the absence of a long series of statistical data.

deduction method represents a transition in the process of cognition from the general to the particular and the individual, the derivation of the particular and the individual from the general. This method is used to determine the strategy of predictive phenomena.

Widespread in geographic forecasting intersystem analysis method proposed by A.L. Chizhevsky back in the 20s for two periodically connected systems - solar activity and the rhythms of natural processes. The 11-year period of solar activity is noted as the main period influencing many natural processes of the Earth - river runoff and floods, avalanches and mudflows, landslides and dust storms, and others. This period is used to predict many natural processes. Deviations from 11-year cycles are explained both by the properties of the natural processes themselves and by the perception of solar rhythms by a specific natural and economic background underlying the Earth's surface. This makes it necessary to predict natural processes, taking into account local landscapes and economic features of the region.

Methods of expert assessments. These methods are used in conditions where there is no sufficient theoretical basis (substantiation) for the development of an object. Their use is also justified in cases where there is no representative and reliable statistics of the characteristics of the object, there is a large uncertainty in the environment for the functioning of the object, when forecasting socio-economic objects that are strongly influenced by scientific and technological progress, as well as when forecasting under time pressure.

Program forecasting method involves the development of a classification of the type of events that need to be analyzed, and an initial list of experts on the problem under study. For each type of problem, the authority of each expert is determined on a 100-point scale using objective methods. At the first stage, the task is formulated by listing events, the time and probability of which are called final. The scenario of these events is given to the experts having the highest "weight" on the given problem. Experts determine the conditions under which these events can be assessed. Then, the probability of the occurrence of the event and the probable amount of time between the time the condition is met and the time the event occurs are estimated. The final forecast of the occurrence of this event is made on the basis of averaging the estimates of individual experts, taking into account their "weight".

Heuristic forecasting method named in connection with the homogeneity of the forms of mental activity of the expert. This method is used to get an idea of ​​the prospects for the development of a narrow field of science and technology based on a systematic processing of predictive assessments of expert groups.

The method of collective generation of ideas, or the method of "brainstorming". When using this method, there is an avalanche-like promotion of new ideas and activation of the creative potential of a group of specialists. This is achieved in the following way:

Each participant gets the opportunity to see the problem through the eyes of colleagues;

The skills of collective creative thinking are developed.

Summing up is carried out collectively. The following tasks are solved:

Get definitive answers to the questions raised;

A plan for solving the relevant tasks is formed;

Ideas are selected that can be used to solve a particular problem;

New aspects of the problem under study are established.

Another method of expert assessments is the PATTERN method. On the initial stage the development trends of the predicted object are studied and their expert assessment is given to obtain judgments about possible ways of changing the object. Then the optimal options and means of achieving the main objectives are determined. To do this, a scenario for the development of the predicted object is compiled. Scenario - it is a way of determining a logical sequence of probabilistic events to establish development alternatives. Event - is an action that may or may not occur under certain set of conditions. This method is widely used in solving forecasting problems. scientific and technological progress and development of industries.

Target tree method. A goal tree is a systematized record of the steps involved in solving a given problem. The ultimate goal is divided into intermediate stages, each of which is necessary to solve the previous task. Each of the nodes of the tree of goals is divided into several branches with elements that are evaluated by the degree of importance in terms of achieving the nearest goal.

Widespread in geographic forecasting is one of the oldest ways of knowing - analogy method. A forecast by analogy is a conclusion made about the properties of the predicted object based on its similarity with other objects both in terms of structural and genetic features, i.e., this spatio-temporal situation is compared with some past historical situation. Using this method, the predicted parameters, the timing of the onset and the significance of the expected events are specified. The main stages of the analogy method are the search and selection of an analog, the construction of a model and its study, the extrapolation of data from the analog to the object under study, the verification of extrapolation conclusions by analogy.

Popular in forecasting genetic method, based on the analysis of spatio-temporal evolutionary stages in the development of phenomena and processes that explain the observed facts and suggest still unknown ones. In physical-geographical forecasting, this method is interpreted as landscape-genetic series method. Knowing the sequence of spatial change of natural complexes within the genetic series, it is possible to predict the order of their change in the process of development. Using these and other forecasting methods, it is possible to outline trends in future changes in the natural environment under the influence of natural and anthropogenic impact factors with a probability of about 60-65%.

Statistical forecasting methods are aimed at identifying the characteristics of the predicted object that are stable in time, searching for the patterns of its development and studying the state to determine the main directions of the object's change in time and space.

The most developed of the formalized forecasting methods was the method extrapolation of development trends. The extrapolation method is a classic popular forecasting method based on finding the probabilistic value of the predicted object at a given time using known characteristics. To do this, the trends in the development of the forecast object are determined, i.e., the trends in the development of the natural environment in the past and future, taking into account not only its stable development or the preservation of the absolute increments of the predicted values, but also their possible acceleration or even the emergence of new factors that limit or stimulate development.

The solution of the extrapolation problem involves finding, from the known qualitative and quantitative values, the probabilistic value of the predicted indicator at a certain point in time, taking into account the duration of the forecasting period. The predicted process consists of regular and random components . The first value is the trend component. The second is considered to be an uncorrelated random process and is necessary to correct the characteristics of the forecast. The main attention is paid to the process of the best description of the trend, on the basis of which predictive extrapolations are built. The choice of the trend that most adequately describes the predicted process is associated with the definition of the appropriate type of functions. To build prognostic functions, information is needed about stable relationships, the pace and direction of processes over a long time, the properties of processes at a certain moment, and the initial and restrictive conditions of the development process.

It is also important to correctly determine the extrapolation lag (extrapolation distance). The depth of predictive extrapolation should not exceed half of the period taken as the base, i.e., for example, a 10-year forecast requires a time series of 25-30 years. The reliability of the forecast obtained is determined by the probability of the predicted event occurring.

Others formalized methods geographic forecast are correlation, regression, factor analysis, the method of envelope curves, etc.

Correlation analysis- this is the definition of the relationship between two quantities, expressed in the fact that when one quantity changes in a certain direction, the other also changes. Regression analysis is to identify the functional dependence of the average value of one quantity on one or more variables. Factor analysis allows you to "compress" a large number of initial indicators into a smaller number of generalized characteristics (factors) with the loss of an insignificant amount of initial information. Envelope curve method is based on identifying trends in the parameters of the predicted object when different conditions, defining the limits of growth. The main development trends are plotted on a graph, and then an envelope curve is drawn along the inflection points of the curve, which is a generalized trend in the change of the object over time. This method is especially effective for obtaining short-term forecasts of changes in technical and economic indicators. technological processes and changes in the level of pollution of the natural environment from sources of different capacities.

For the development of economic and geographical forecasts, modeling is increasingly changing, in particular mathematical. It is necessary to create adequate predictive models of the studied objects, phenomena and processes. Modeling makes it possible to reveal the causality of the system parameters and to give a functional, point and interval assessment of them.

Among the existing models for forecasting purposes are used following models:

1. functional, describing the functions that are performed by individual components of the system and the system as a whole.

2. Physical process models, defining mathematical dependencies between the variables of this process. They can be continuous and discrete in time, deterministic and stochastic.

3. economic, determining the relationship between various parameters of the process and phenomenon under study, as well as criteria that allow optimizing economic processes.

4. procedural, describing the operational characteristics of systems necessary for making management decisions.

predictive models can be conceptual(expressed by verbal description or flowcharts), graphic(presented in the form of curves, drawings, maps), matrix (as a link between verbal and formalized representation), mathematical(represented as formulas and mathematical operations), computer(expressed by a description suitable for computer input).

A special place is occupied simulation predictive models. Simulation modeling is a formalization of empirical knowledge about the object under consideration using modern computers. Under simulation model is understood as a model that reproduces the process of functioning of systems in space at a fixed point in time by displaying elementary phenomena and processes while maintaining their logical structure and sequence. This allows, using the initial data on the structure and main properties of territorial systems, to obtain information about the relationships between their main components and to identify the mechanism for the formation of their sustainable development.

The process of developing geoecological forecasts based on mathematical modeling includes the following steps:

1. Formulation of the purpose and objectives of the study. Qualitative analysis of the predicted object in accordance with the purpose of the study.

2. Definition of the subject and level of modeling, depending on the tasks of forecasting.

3. Selection of the main features and parameters of the model. The model should include only parameters that are essential for solving a specific goal, since an increase in the number of variables increases the uncertainty of the results and complicates the calculations using the model.

4. Formalization of the main parameters of the model, i.e. the mathematical formulation of the purpose and objectives of the study.

5. Formalized representation of the relationship between the parameters and characteristics of the predicted object or process.

6. Checking the adequacy of the model, i.e., the accuracy of the reflection of the features of the original by the mathematical model.

7. Determining the informative capabilities of the model by establishing quantitative relationships of regularities.

Lecture No. 10

The concept of a field in geography

The main questions discussed at the lecture:

1. The concept of the field in geography.

2. Maps of fields and their varieties.

3. General rules creating field maps.

4. Maps of fields of continuous and discrete phenomena.

5. Cartographic-statistical method and field maps.

6. Field maps and modeling method.

7. Mathematical-statistical and isolinear models as a tool for analysis and synthesis of the studied indicators.

1. The concept of the field in geography there is a system of ideas about real and abstract fields and surfaces, about the methods of their cartographic representation. It is designed to create and use cartographic field models for scientific and practical purposes (Chervyakov, 1992).

At present, the field concept has seriously interested representatives of various sciences - geophysicists, meteorologists, hydrologists, geographers, demographers, sociologists, geologists, linguists, etc. This can be explained, on the one hand, by the noticeable benefits of using physical analogies, and on the other, by the possibility of widely mathematical apparatus and a map as a means of obtaining, storing, transforming and visualizing various quantitative information about natural and socio-economic phenomena.

Physicists usually consider a field to be space in which forces of one kind or another act. Hence, physical fields are often called force fields. It is no coincidence that the most geophysical field of the Earth is considered to be the space in which the forces associated with the terrestrial matter, its movement and the processes occurring in it act.

Another, abstract mathematical concept of a field implies the presence of a space, at each point of which the numerical value of a certain quantity is determined. In this case, the field is considered as a function of the position of the point in space and time. In this form, the scope of the concept of "field" is greatly expanded. It covers not only natural, but also socio-economic phenomena. The first includes the spatial distribution of atmospheric pressure, temperatures, precipitation, the second - the distribution of the population, natural resources, production, institutions serving the population.

Finally, the field is often understood as the area of ​​distribution of any phenomena, expressed not only quantitatively, but also qualitatively, not only in analytical, but also in synthetic indicators. To define such a field is not an easy task. In terms of content, but, perhaps, comes closer to such universal philosophical categories as “space”, “object”, “phenomenon”.

Based on the foregoing, we will assume that there are three main ideas about the field: 1) physical (field as an area of ​​distribution of forces, energies, interactions); 2) abstract mathematical (the area of ​​​​distribution of quantities characterizing denia from a variety of sides); 3) abstract-logical (the area of ​​distribution of any phenomena and their indicators, both in qualitative and quantitative terms).

Geographers who adhere to the physical (force) concept of the field note the importance of using the physical concept in geographical research (gravitational field), which arises around some source of "power" (for example, industrial enterprise or locality). These conditionally force fields are often considered as the result of the interaction of many homogeneous objects ("tel" - settlements, factories, mines), which differ from each other "mass" - quantitative characteristics (population, volumes of natural resources, manufactured products, etc.). In population geography, such "bodies" are often taken as population points, and for "mass" - population. "Gravity fields" or fields of potentials of this kind are involved in economic geography to study not only the population, but also production, transport links, service elements, fixed assets and other phenomena. Geographical fields are considered as a source of connections in geosystems, they try to find analogues of electrostatic and gravitational fields in their structure and functioning, they propose to identify the conditions for the emergence of flows of matter, energy and information, to find their sources.

The abstract-mathematical (quantitative) representation of the field penetrated into geography and became widespread in it thanks to the close ties of geography with other sciences about the Earth and, above all, with geophysics, which studies the processes occurring in the solid, liquid and gaseous shells of the Earth with the help of fields. "Field" is an integral part of the vocabulary of a meteorologist and hydrologist, used by them in the study of the spatial distribution of air and soil temperatures, atmospheric pressure, precipitation and other meteorological elements. It can be considered an undoubted merit of geophysicists and hydrometeorologists that, on the one hand, they accepted the abstract mathematical concept of the field, extended it to a wider range of natural phenomena and developed a fundamental methodological basis mathematical analysis of fields; On the other hand, they created the conditions for effective use field theory in other geosciences, including a cycle of branch geographical disciplines covering both nature and society.

The abstract-logical (non-quantitative) concept of the field is quite popular among geographers, which is explained by the exceptional complexity of geographical objects, which makes it difficult to parameterize phenomena. There is also an underestimation of the importance of actively introducing quantitative and other mathematical approaches into geography.

Without denying the possibility of considering the concept of a field in geography from the three noted sides (physical, abstract-mathematical, and abstract-logical), when solving problems of interaction between nature and society, preference should be given to the second side. Indeed, the physical interpretation is distinguished by its narrowness, its inability to cover the entire diversity of natural and especially socio-economic phenomena. The abstract-logical interpretation is too broad, indefinite and not always amenable to mathematical description. Experience shows that fundamental concepts are successfully introduced into science and practice after the problem of measuring and calculating the signs they study is solved. It is no coincidence that, therefore, in the exact sciences, the abstract-mathematical (quantitative) description of fields predominates.

The continuity of the distribution of the studied quantitative characteristics is an attribute of any field. Hence, it is legitimate to call a field a region of continuous distribution of quantitative features. "Topographic" and "industrial" reliefs, "statistical" and trend (smoothed)" surfaces - the essence of the geometric image of their fields, outwardly resembling the relief of the earth's surface. Of all the possible ways of cartographic representation of fields, and therefore surfaces, the main one is the isoline method, which has increased visibility, special metricity of information content (the ability to remove information at any point, relief imagery (the ability to perceive various indicators of continuous and discrete phenomena in the form of a relief of the earth's surface) , low sign loading of maps. field map it is legitimate to call a special group of maps designed to isolinearly display a continuous, smooth, smooth territorial distribution of quantitative features that characterize both natural and socio-economic phenomena.

2. Maps of fields and their varieties. It is known that physicists divide fields into two large groups scalar and vector. A scalar field is a region of space, each point of which is described by its own value of a quantitative attribute. To describe points in space vector fields two vector characteristics are required - numerical value (modulus) and direction of movement. The concept of this field arose in physics, mainly in the study of the velocities of the particles of a liquid, the strength of the lines of force (magnetic and electric), the shifts of the points of an elastic body, etc.

According to these two groups of fields, we select scalar maps and vector field maps. Maps of scalar fields are directly related to the concept of "statistical surface" and to isolines as the most effective means of cartographic representation of these fields. Ways to display vector fields on maps are less developed. However, perhaps the most suitable arrows here are those that can combine two characteristics - module and direction.

According to the method of obtaining quantitative information, field maps can be divided into field maps of field observations and field calculation maps.

Field observation maps are compiled according to the data of direct instrumental measurements of field parameters (scalar and vector). These include measurements of the relief of the earth's surface, geological and soil structure, meteorological and hydrological indicators.

Maps of calculated fields are compiled as a result of preliminary mathematical (more often mathematical-statistical) processing in office conditions of various quantitative information collected in the field or taken from maps and photographs, obtained from statistical reporting materials.

Mathematical-statistical processing can be subjected to both temporary and territorial series. In the first case, continuous distributions of such indicators as the average monthly air temperature, standard deviation of precipitation by years, annual increase in grain yields are calculated and mapped, and in the second case, data localized at points, on lines and areas, which are summarized statistically over the entire study area. territories or in separate territorial cells. In this case, not average monthly or average annual indicators are obtained, but indicators averaged over territorial cells, for example, average temperatures, precipitation by region.

Given the orientation of modern sciences to the study of objects as systems consisting of separate dynamic and interconnected elements, it is advisable to subdivide the entire variety of maps of fields of natural and socio-economic phenomena into maps of fields of statics, dynamics and interconnection of phenomena. If the second group of field maps shows in what direction and with what intensity the development of phenomena occurs, then the third group - maps of interconnection fields - gives an answer to the question of what factors and to what extent determine the existing spatial structure of the objects and phenomena under study.

3. General rules for creating field maps. Despite the wide variety of field maps, when compiling them, one should be guided by the following general rules, which are based on the property of a continuous continuous distribution of scalar and vector characteristics of the mapped fields, as well as the fundamental impossibility of making measurements at all points of the terrain.

Rule one - obligatory preliminary measurement (for maps of calculated fields) of scalar and vector characteristics at selected points of the terrain.

Rule two - the potential ability to determine the characteristics of fields at any point in the terrain (maps).

Rule three - representative (representative) selectivity of measurements and calculations in points. Indeed, it is not possible to determine cartographically and reproduce scalar and vector characteristics in an infinite set of terrain points. We have to limit ourselves to selective measurements on regular or irregular grids of points, which are often called control points. When these points are intended for drawing isolines, they are more correctly called reference points.

Rule four - reproduction in point measurements / calculations of continuous properties of fields, which manifests itself in determining the gradual change in quantitative characteristics between neighboring control (reference) points, in the absence of sharp jumps and infinitely large values.

Rule five - distribution of data obtained at one point to the entire mapped area. This is most often done using conventional cartographic interpolation.

4. Maps of fields of continuous and discrete phenomena. With the help of isolines, the relief of the earth's surface, the territorial distributions of atmospheric pressure, temperature, precipitation, magnetic declination, and other truly continuous phenomena have been successfully mapped for a long time. However, these maps of continuous phenomena, constructed, as a rule, according to field measurements, display only a part of the natural indicators usually obtained on the ground. Isolinear mapping of such discrete discontinuous, territorially separated phenomena , as natural resources, population, agricultural and industrial production, does not differ in sufficient accuracy and reliability. This can be explained by the fact that the isolines here were built not according to traditional point observations, but according to areal indicators, only conditionally related to the centers of the corresponding territorial cells. At the same time, it turned out that the quantitative indicators at the points-centers do not meet the uniqueness rule numerical values. The latter largely depend on the size, shape, and orientation of the territorial cells of the source data localization. Hence, cartographers were faced with the task of developing a more advanced methodological apparatus for creating isolinear maps from discrete data, which makes it possible to determine the mapped values ​​at any point in the terrain. It is only such maps that can be legitimately called maps of the fields of discrete phenomena.

The solution of this problem made it possible to significantly expand the range of isolinear maps of fields and create more favorable conditions for the integrated study of complex geographical objects, conjugation of isolinear maps of natural and socio-economic, continuous and discrete phenomena. From here, cartographers faced the second task of developing a system of methodological methods for compiling maps of fields of different content, different spatial and temporal belonging. The ability to take data at any point and in any volume created favorable conditions for comparing the maps under consideration not only visually, but also at the level of mathematical processing of cartographic information.

Each of the two tasks considered has its own theoretical basis stimulating the development of new types of maps, mapping techniques. Thus, on the basis of the dialectical unity of discreteness and discontinuity, the legitimacy and expediency of extending the field concept to many natural and socio-economic phenomena, the absolute spatiotemporal discreteness of which was not previously in doubt, was proved (Chervyakov, 1978). For this, a new type of maps of fields of discrete phenomena is proposed, the core of which is maps of density fields, but

The main purpose of the analysis of the forecasting object, as indicated, is the development of its forecasting model. In the literature, the concept of a model is interpreted very broadly. This term refers to such concepts as a mathematical description of a process or an object, an algorithmic description of an object, a formula that determines the law of the object's functioning, a graphical representation of an object (process) in the form of a graph or flowchart.

In a strict sense, a model is defined as “a phenomenon, object, installation, symbolic formation or conditional image (description, diagram, etc.) that is in some correspondence with the object under study and is able to replace it in the process of research, giving information about the object.” In forecasting, this concept is specific and narrower. Predictive model - a model of the forecasting object, the study of which allows obtaining information about the possible states of the object in the future and ways to achieve these states. Thus, the purpose of the predictive model is to obtain information not about the object in general, but about its future states.

This determines the features of building and testing the adequacy of predictive models. When constructing and evaluating them, it is impossible to directly check the correspondence between the model and the original in relation, since the model must refer to the future states of the object. In this case, either the object itself does not currently exist (the projected object), or it exists, but it is not known what changes may happen to it in the future.

The classification of management models, the most typical in the above areas, looks like this: functional models; models of physical processes; economic models; procedural models.

functional models describe the functions performed by the main constituent parts system or controlled process. These models are usually built at the start of a system study or simulation experiment. It would be more correct to call such models structural-functional and. A structural-functional model is built in the form of a diagram. Functions are most often described additionally in verbal form.

Physical process model determines the mathematical relationships between the variables of the physical process of production. These can be technological parameters of the process: temperature, pressure, fuel consumption, rolling speed, pressing force, percentage of substance in the mixture, etc. In accordance with the nature of the process under study, such models can be continuous and discrete in time, deterministic and statistical, and according to the method of obtaining information - analytical and experimental.

Economic Models determine the relationship between various economic indicators of a process or system and various kinds of restrictions imposed on economic indicators, criteria that allow optimizing the process in economic terms. They can, like models of physical processes, take the form of formulas, equations, and also algorithmic notation, if the analytical representation of the process is difficult. This class of models can, in turn, be subdivided into planning models and production models.

Planned models serve the purposes of optimizing the developed plans for the development of the system. These include forecasting models that are aimed at formulating probable alternatives for the development of the system in order to select the optimal planned decision. Planning economic models are designed to provide a quantitative assessment of various plan options in accordance with the optimality criterion embedded in the model.

Production models determine the relationship of economic indicators with process parameters in the course of its development. They are intended for operational management of the system operation. In this case, as a rule, a mathematical or algorithmic description of the objective function is formulated, methods for its operational calculation and optimization in various external conditions are determined.

Economic models, depending on the scale of the process being modeled, are divided into macro- and microeconomic ones. Macro-, roeconomic models relate to processes at the level of the national economy, to the tasks of planning and managing industries, and to solving intersectoral problems. The most common form of macroeconomic models are balance-sheet planning models. Microeconomic models deal with the problems of planning and management at the level of enterprises or the stages of the process of creating large technical systems.

Procedural Models describe the operational characteristics of systems, i.e., the order and content of management actions. The most important models in this class, which are of particular interest for the system of process optimization and control automation, are information models. In addition to them, models of modes and ensuring the safety of work can be attributed to this class. Information models determine the structure of information flows in the system, the content, format, speed of information processing, the points of origin and consumption of information, the main stages of its passage and control over it. Procedural models of operating modes and ensuring safety of operation describe an action that changes the state of the system (starting, stopping, changing the load, etc.), as well as a set of rules and restrictions imposed on the functioning of systems under safety conditions. A characteristic of the latter type of models is the inclusion of a model of a human operator in the scheme. It performs the functions of monitoring operating modes and making decisions that prevent a breakdown or an emergency.

The classification of models depends not only on the essence of the process being modeled, but also on the methodological apparatus that underlies the model. Obviously, in this aspect, the classification of predictive models will coincide with the classification of forecasting methods. In this regard, we can note a specific type of predictive models - expert models. They involve a formal description of the functioning procedures, representation of the modeling object in the form of a process, special formulas and algorithms for processing expert assessments. However, the very procedure for generating these estimates is creative and informal.

The main principles of social forecasting are the following:

systemic forecasting, requiring interconnection and subordination of the forecasts of the forecasting object and the forecast background and their elements, taking into account feedback;

consistency - harmonization of normative and search forecasts of various nature;

variability - development of forecast options based on the characteristics of the working hypothesis, the purpose of the forecast and options for the forecast background;

continuity - correction of forecasts as new data on the object of forecasting becomes available;

verifiability - determination of the reliability, accuracy, validity of forecasts;

profitability - increase economic effect from using a forecast over the cost of developing it.

There are the following types of forecasts: search, the content of which is to determine the possible states of the forecasting object in the future;

normative, the content of which is to determine the ways and timing of achieving the possible states of the object of forecasting in the future;

complex, containing elements of search and normative forecasts;

interval, the result of which is presented as a confidence interval of the characteristics of the forecasting object for a given probability of making the forecast;

point, the result of which is presented as a single value of the characteristic of the predicted object without specifying a confidence interval;

operational, with a lead time for the forecasting object up to one month;

short-term, with a lead period for the forecasting object from one month to one year;

medium-term, with a lead period for the forecasting object from one to five years;

long-term, with a lead time for forecasting objects from five to fifteen years;

long-term, with a lead time for the object of forecasting over fifteen years;

multidimensional, containing several qualitative or quantitative characteristics of the forecasting object;

one-dimensional, relating to the Earth and humanity as a whole; national, pertaining to the state as a whole.

Forecast options include:

period of the basis of the forecast - the period of time on the basis of which the retrospection is built;

forecast accuracy - an estimate of the confidence interval of the forecast for a given probability of its implementation;

reliability of the forecast - an assessment of the probability of the implementation of the forecast for a given confidence interval;

the validity of the forecast - the degree of compliance of methods and initial information with the object, goals and objectives of forecasting;

forecast error - a posteriori deviation from the forecast, from the actual state of the object or the ways and timing of the forecast.

The stages of forecasting are:

pre-forecast orientation - a set of works that precede the development of a task for a forecast and include the definition of the object, goals and objectives of forecasting, as well as the period of foundation and the period of lead of the forecast;

task for the forecast - a document that defines the goals and objectives of the forecast and regulates the procedure for its development;

predictive retrospection - a study of the history of the development of the object of forecasting and the forecast background in order to obtain their systematic description;

predictive diagnosis - a study of a systematic description of the object of forecasting and the forecast background in order to identify trends in their development and select models and methods of forecasting;

prognostic prospection - development of a prognosis based on the results of a prognostic diagnosis;

verification of the forecast - assessment of the reliability and accuracy or verification of the validity of the forecast;

correction of the forecast - specification of the forecast on the basis of its verification and additional data;

forecast synthesis - development of a systemic forecast.

Science-based forecasting is an important tool of modern management. It is used both for strategic planning of the development of individual enterprises and for the development of long-term socio-economic programs at the state level. The structure and steps of this process are closely related to the methodology and the adopted model.

Forecasting is a system of theoretically based ideas about the possible future states of an object and about the directions of its development. This concept is similar to the term hypothesis, but, unlike the latter, it is based on quantitative indicators and has greater reliability. A common feature of these two concepts is that they explore an object or process that does not yet exist.

Applied forecasting techniques were actively developed in the 70s. XX century, and the boom of their use abroad continues to this day. This is mainly due to a new direction in research - global issues, the main task of which is to solve the world's resource, demographic and environmental problems.

Forecasting is a science that has a close relationship with statistics and its analytical methods. In carrying out the analysis, the achievements of mathematics, natural and other sciences are widely used.

Forecasting and planning complement each other in various variations. In most cases, a forecast is developed before a plan is created. He can also follow the plan - to determine possible consequences. In large-scale studies (at the state or regional level), the forecast can act as the plan itself.

Goals

The main task of forecasting is to identify effective ways management of socio-economic processes in society or the economic and technical development of an enterprise.

The methodological bases for achieving these goals are as follows:

• analysis of trends in the development of the economy and technology;
• anticipation of various options;
• comparison of existing trends and goals;
• assessment of the possible consequences of the adopted economic decisions.

Forecast methods

Forecasting is carried out according to a certain methodology, which is understood as a system of indicators and approaches to the object under study, the logic of research. Other parameters also depend on which method is chosen - how many forecasting stages will be carried out and what their content will be.

Among huge amount forecasting methods can be divided into the following main groups:

1. Individual expert assessments:

• Interview - information is obtained during the conversation (formalized and non-formalized, preparatory and independent, directed and non-directed).
• Questionnaire survey (individual, group, mass, full-time and correspondence questioning).
• Development of a predictive scenario (used in the areas of management activities).
• Analytical method - building a tree of goals (for assessing hierarchical or structural processes).

2. Collective expert assessments based on obtaining an agreed opinion in a group of experts:

• meetings;
• "round tables";
• "Delphi";
• "brainstorm";
• court method.

3. Formalized methods based on the use of mathematical evaluation methods:

• extrapolation;
• math modeling;
• morphological method and others.

4. Complex techniques that combine several of the above:

• "double tree" (used for fundamental research and R&D);
• predictive graph;
• "Pattern" and others.

A correctly chosen forecasting method significantly affects its errors. For example, when strategic planning no extrapolation method is used (foresight beyond experimental data or distribution of properties from one subject area to another).

Stages

The sequence of forecasting stages in the general case is the work carried out according to the following scheme:

1. Training.
2. Analysis of internal and external conditions in retrospect.
3. Development of options for the development of events along an alternative path.
4. Expertise.
5. Selection of a suitable model.
6. Her assessment.
7. Analysis of the quality of the expertise (a priori and a posteriori).
8. Implementation of predictive developments, their control and adjustment (if necessary).

Below is a description of the main stages of forecasting and their characteristics.

Preparatory stage

At the first stage, the following questions are solved:

1. Pre-forecast orientation (formulation of the object of study, statement of the problem, determination of goals and objectives, primary modeling, formulation of working hypotheses).
2. Information and organizational preparation.
3. Formulation of the assignment for the forecast.
4. Preparation of computer support.

At the staging stage of forecasting, the performers who must carry out the forecast are also determined. This group may consist of competent workers responsible for organizational work and information support, and also includes an expert commission.

The following points are documented:

• prediction decision;
• the composition of the working commissions;
• work schedule;
• analytical review on the problem under study;
• contracts or other agreements with specialists involved in forecasting.

Analysis

At the second, analytical stage of forecasting, the following types of work are carried out:

• research of information about the object in retrospect;
• separation of qualitative and quantitative indicators;
• analysis of internal conditions (in relation to an enterprise, this can be: its organizational structure, technologies, personnel, production culture and other qualitative parameters);
• study and assessment of external conditions (interaction with business partners, suppliers, competitors and consumers, the general state of the economy and society).

In the process of analysis, the current state of the object is diagnosed and the trends of its further development are determined, the main problems and contradictions are identified.

Alternatives

The stage of identifying other, most probable options for the development of an object is one of the key stages of forecasting. The accuracy of the forecast and, accordingly, the effectiveness of decisions made on its basis depend on the correctness of their determination.

At this stage, the following works are performed:

• development of a list of alternative development options;
• exclusion of those processes that in a given period have a probability of implementation below the threshold value;
• detailed study of each additional option.

Expertise

Based on the available information and previous analysis, an expert study of an object, process or situation is carried out. The result of this forecasting stage is a reasonable conclusion and determination of scenarios according to which development will be most likely.

Examination can be carried out in various ways:

• interviewing;
• questioning;
• one-time or multi-round survey of experts;
• anonymous or open exchange of information and other means.

Model selection

A forecasting model is a simplified description of an object or process under study, which allows you to obtain the necessary information about its future state, directions for achieving such a state, and about the interconnections of individual elements of the system. It is chosen based on the research method.

AT economics There are several types of such models:

• functional, describing the operation of the main components;
• models characterized by methods of economic physics (determination of mathematical relationships between various variables of the production process);
• expert (special formulas for processing expert assessments);
• economic, based on determining the dependencies between the economic indicators of the predicted system;
• procedural (describing managerial interactions and their order).

There are also other classifications of models:

1. According to the aspects reflected in them - industrial and social.
2. Models designed to describe income, consumption, demographic processes.
3. Economic models of various levels (long-term for forecasting economic development, intersectoral, sectoral, production).

In predictive models, the following forms of describing phenomena are distinguished:

• text;
• graphical (extrapolation methods);
• network (graphs);
• construction of block diagrams;
• matrix (tables);
• analytical (formulas).

The model is formed using the following methods:

• phenomenological (direct study and observation of occurring phenomena);
• deductive (selection of details from the general model);
• inductive (generalization from particular phenomena).

After selecting the model, a forecast is made for certain periods. The results obtained are compared with currently known information.

Quality control

The stage of verification of the forecast, or verification of its reliability, is carried out on the basis of previous experience (a posteriori) or independently of it (a priori). Quality assessment is done using the following criteria: accuracy (scatter of predictive trajectories), reliability (probability of the selected option), reliability (measure of process uncertainty). To assess the deviation of forecast criteria from their actual values, such a concept as forecast errors is used.

In the process of controlling, the results are also compared with other models, and recommendations are developed for managing an object or process, if such an impact can have an impact on the development of events.

There are 2 methods for quality assessment:

1. Differential, in which clear criteria are used (defining the clarity of setting a forecast task, the timeliness of stage-by-stage work, the professional level of performers, the reliability of information sources).
2. Integral (generalized estimate).

Main Factors

The following main factors influence the accuracy of the forecast:

• competence of the expert group;
• the quality of the prepared information;
• accuracy of measurement of economic data;
• the level of methods and procedures used in forecasting;
• correct choice of model;
• consistency of methodological approaches between different specialists.

Often large errors also arise due to the fact that the features of the conditions under which this model is used are not taken into account.

Implementation

The last stage of forecasting is the implementation of the forecast and monitoring the progress of its implementation. When critical deviations are identified that can significantly affect further development events, the forecast is corrected.

The level of adoption of corrective decisions may be different. If they are insignificant, then the adjustment is carried out by the analytical group, which is responsible for developing the forecast. In some cases, experts are involved in this work.

Forecasting stages: sequence and characterization - all about traveling to the site

The practice of political forecasting varied and different:

1. For goals and directions.

2. Over time.

3.Behind the grounds.

4. For tools. According to the main goals political activity forecasts

may be aimed at:

Identification of major trends international relations;

Knowledge of the mechanism of emergence and evolution of possible international conflicts;

Forecast of the result of the election campaign;

Establishing the dynamics of the influence of the main political forces in a particular country;

Determining the degree of popularity of political leaders and their influence on the change in the political situation in a particular country;

Analysis of the possible consequences of one or another political decision.

The basis of political forecasting strong different.

Different types statistical information;

Sociological research data;

Interrogation of public thought;

Mass media materials;

intelligence data;

Historical, psychological, economic, ethnographic research;

Knowledge of the factors that influence the course of political processes.

By timing, forecasts are:

1. Short-term - up to 5 years.

2. Medium-term - from 5 to 15 years.

3. Long-term - up to 30 years.

Naturally, with an increase in the period, the degree of reliability of the occurrence of events or processes that are assumed decreases. Toolkit of political forecasting includes:

Different types of surveys;

Qualitative and quantitative methods for evaluating public thought survey data;

For processing large data arrays, the following is used:

Modern computer technology;

A special mathematical apparatus, the effectiveness of which is becoming more and more obvious.

Drafting political forecast

It is a system of phased actions, among which are:

Structural analysis of the relevant political system, identification of its components, determination of the nature of the links, the relationship between them;

Selection of the main factors, quantitative expression, comparison of their significance;

Identification of the main trends aimed at the development of processes operating in the system;

Extrapolation (imaginary continuation) of these processes, synthesis of these trajectories in their interaction;

Compilation of a comprehensive forecast for the development of the political system.

Forecasting methods in politics

Traditionally widely used in political forecasting:

1. extrapolation method(an imaginary continuation into the future of certain existing political processes).

The use of this method is based on the fact that most political phenomena are actually processes, that is, phenomena that last in time and have their own trajectory of movement, which can be established by knowing the chain of past and present events.

2. analogy method. It is actively used in political forecasting based on similarity of conditions which caused this or that event in the past, allows us to draw a conclusion about the possibility of an event in the future.

The analogy method can be used in forecasting just right for predicting events or individual phenomena.

3. Scenario method provides a description of possible future events in a region or around the world. It is used primarily to describe the pattern of development conflict situations, while preparing political decisions more or less long term.

Drawing up scenarios is always associated with an assessment of events and trends in their deployment, and the assessment expresses a subjective attitude towards the phenomenon on the part of the one who does it. Therefore, there may be several scenarios that relate to the prediction of the same phenomenon. The choice of one or another scenario requires the inclusion of an expert assessment in the forecasting activity, which is obtained by polling scientists.

4.Modeling method turns out to be extremely useful in political analysis and forecasting.

Political forecasting- the process of developing a scientifically based judgment about a possible scenario for the development of political events in the future, alternative ways and terms for its implementation, as well as identifying specific recommendations for the use of practical measures in real-life conditions.

Areas of use:

1.Economic and political.

2. Socio-political.

3.State-legal.

4. Political and ideological.

5. Military-political.

6. Foreign policy.

7. Internal political.

Basic principles:

1. Consistency.

2. Consistency.

3. Continuity.

4. Credibility.

5.Optimality

6. Alternative.

7. Profitability.

8. Analogy.

Forecasts are divided into:

2.Regulatory.

Beyond the warning period:

Operative - up to 1 month.

Short term - from 1 month to 1 year.

Medium-term - from 1 to 5 years.

Long-term - from 5 to 15 years.

Extra-long-term - for a period of more than 15 years.

For spheres:

1. Internal political.

2. Foreign policy.

The main stages of political forecasting:

1. Forward-looking orientation.

2. Building a basic model.

3. Collection of forecast background data.

5. Evaluation of the reliability and accuracy of the forecast.

6. Building a search model.

The main methods of political forecasting:

1. Behind the sign of the information base:

factual;

expert;

combined.

2. According to the principle of information processing:

statistical;

analogies;

direct expert assessments;

expert opinions from feedback;

leading

3. Behind the sign of the implementation apparatus:

Extrapolation;

Interpolation;

Factor analysis;

Correlation analysis;

Mathematical analogies;

Historical analogies;

Expert questioning;

Expert analysis.

Political situation

The totality and result of factors and conditions that express the correlation and alignment of socio-political forces, as well as the state of political relations, directly related to the achievement of political goals, the satisfaction of the needs of political subjects.

Structure:

The subjects of the political situation, their alignment and balance of power;

Real life circumstances, specific political processes, phenomena and development trends;

Political interests and whole.

Characteristics:

Complexity;

scale;

Dynamism;

Variety of trends;

A large number of manifestation forms.

Main types:

cooperative;

confrontational;

Cooperative-confrontational (mixed).

Methodology for analyzing the internal political situation:

Definition of subjects of political relations;

analysis of the qualitative and quantitative composition of policy subjects

Analysis of the goals and interests of policy subjects;

Analysis of real processes and phenomena in various fields public life, identifying trends in their development;

Analysis of the state of the economy;

Analysis of the state of social-class and national relations;

Analysis of public consciousness, cultural life;

Analysis of the criminal situation in the country;

Analysis of the internal military-political situation;

Analysis of the legitimacy of political power;

Assessment of the political situation in the country;

Forecast of the development of the political situation.

Methodology for analyzing the international (regional) political situation:

Determining the poles of power in the world (region);

Determination of centers of power at the poles;

Analysis and evaluation of military potentials of centers of power;

Analysis and assessment of the internal political situation in the centers of power;

Assessment of the international (regional) political situation;

Development of a forecast for the development of the international (regional) political situation.

The basic processes that ensured the globalization of the 21st century are:

1.Commercialization - the consistent formation of global markets for goods, services, work, capital.

2. Bureaucratization - the evolution of bureaucracies: from agrarian empires to absolute monarchies, from absolute monarchies to constitutional monarchies; republican structure and forms of democratic control over the bureaucracy.

3. Collectivization - different forms of social mobilization that use the mechanisms of social regulation and self-regulation.

Democratization - different forms of social mobilization that use the mechanisms of social regulation and self-regulation

5. Rationalization - the emergence of experimental science of the New Age, its paradigmization (Newtonian mechanics), the emergence of scientific technologies, social design and social sciences, applied scientific ^-scientific-scientific-natural-science, technical and social research, social engineering, scientific programming, system management, informatization. Scheme of political globalization:

State sovereignty - the absence of sovereign states; multiple centers of power at the global, local and intermediate

Problem solving – solving local problems in the context of a global community;

International organizations- powerful and dominant in relation to national organizations;

Political culture is a planetary overcoming of the dominance of state-centric values.