The method of cognitive modeling in the study of management problems. Medium-term forecasting of the Russian economy using a cognitive model

Individual work

Cognitive modeling

Introduction

1. Concepts and essence of "cognitive modeling" and "cognitive card"

2. Problems of cognitive approach

Conclusion

List of used literature

Introduction

In the middle of the 17th century, the famous philosopher and mathematician René Descartes expressed aphorism, which became classic: "Cogito Ergo Sum" (I think, therefore, existed). Latin root Cognito has an interesting etymology. It consists of parts "CO-" ("together") + "GNOSCERE" ("I know"). In English, there is a whole family of terms with this root: "Cognition", "Cognize" and others.

In the tradition, which is designated by the term "cognitive", only one "face" of thought is, its analytical essence (the ability to decompose integer on the part), decompose and reduce reality. This side of thinking is associated with the identification of causal relationships (causality), which is characteristic of the mind. Apparently, Decartes absolutized the reason in his algebraic system. Another "face" of thought is its synthesizing essence (the ability to design a whole of unbiased integer), perceive the reality of intuitive forms, synthesize solutions and anticipate events. This side of thinking, revealed in Plato's philosophy and his school, is inherent in human mind. It is not by chance in the Latin roots we find two grounds: Ratio (rational relationship) and REASON (reasonable penetration into the essence of things). The reasonable face of thought originates from the Latin Reri ("think"), ascending to the Eneldo root ARS (art), then turned into a modern concept of Art. Thus, Reason (reasonable) is a thought, akin to the work of the artist. Cognitiveness as "Mind" means "the ability to think, explain, externalized actions, ideas and hypotheses".

For a "strong" cognitiveness, a special, constructive status of the category "Hypothesis" is essential. It is the hypothesis that is an intuitive starting point for grading a solution. When considering the situation, the LPR discovers some negative links and structures ("gaps" of the situation) to be replaced by new objects, processes and relationships that eliminate the negative effects and creating a clearly pronounced positive effect. This is the essence of the Innovation Management. In parallel with the detection of "breaks" of the situation, often qualified as "challenges" or even "threats", the subject of management intuitively imagines some "positive answers" as holistic images of the state of the future (harmonized) situation.

Cognitive analysis and modeling are fundamentally new elements in the structure of decision support systems.

The technology of cognitive modeling allows you to investigate problems with fuzzy factors and relationships; - Changes in the external environment; - use objectively established trends in the development of the situation in their own interests.

Such technologies conquer increasingly more and more confidence in structures engaged in strategic and operational planning at all levels and in all spheres of management. The use of cognitive technologies in the economic sphere allows for a short time to develop and substantiate the strategy of economic development of the enterprise, the Bank, region or a whole state, taking into account the impact of changes in the external environment. In the field of finance and stock market, cognitive technologies allow you to take into account the expectations of market participants. In the military and information security area, the use of cognitive analysis and modeling makes it possible to resist strategic information weapons, recognize conflict structures, without bringing the conflict to the stage of armed collision.

1. Concepts and essence of "cognitive modeling" and "cognitive card"

The methodology for cognitive modeling, intended for analysis and decision-making in poorly defined situations, was proposed by Axelrod. It is based on the modeling of subjective presentations of experts on the situation and includes: a methodology for structuring the situation: a model of representing an expert knowledge in the form of a sign orgraf (cognitive card) (F, W), where F is a set of factors of the situation, W is a set of causal relationships between factors situations; Methods for analyzing the situation. Currently, the methodology for cognitive modeling is developing in the direction of improving the analysis and simulation of the situation. Here are proposed models for the development of the situation; methods of solving inverse problems

Cognitive card (from Lat. Cognitio-knowledge, knowledge) - the image of a familiar spatial environment.

Cognitive cards are created and modified as a result of the active interaction of the subject with the surrounding world. At the same time, cognitive cards of varying degrees of community, "scale" and organization (for example, a map-review or map-path depending on the completeness of the representation of the spatial relationship and the presence of a pronounced point of reference) can be formed. This is a subjective picture, which, above all, spatial coordinates in which separate perceived objects are localized. Allocate a map-path as a sequential representation of links between objects on a specific route, and a receipt card as a simultaneous representation of the spatial location of objects.

The leading scientific organization of Russia engaged in the development and application of the technology of cognitive analysis is the Institute for the Problem of Management of the Russian Academy of Sciences, Division: Sector-51, scientists Maksimov V.I., Korotoshenko E.K., Kachaev S.V., Grigoryan A.K. other. On their scientific works in the field of cognitive analysis and this lecture is based.

The basis of cognitive analysis and modeling technology (Figure 1) is cognitive (cognitive-target) structuring knowledge of the object and the external environment for it.

Figure 1. Technology of cognitive analysis and modeling

Cognitive structuring of the subject area is the identification of future target and undesirable states of the object of management and the most significant (basic) management factors and external environment affecting the transition of an object into these states, as well as the establishment at a qualitative level of causal relationships between them, taking into account mutual influence factors on each other.

The results of cognitive structuring are displayed using a cognitive card (model).

2. Cognitive (educational-target) structuring knowledge about the test object and external environment for it based on Pest analysis and SWOT analysis

The selection of basic factors is carried out by applying Pest analysis, allocating four main groups of factors (aspects), which determine the behavior of the object under study (Figure 2):

P. olicy - politics;

E. Conomy - economy;

S. Ociety - Society (socio-cultural aspect);

T. Echnology - Technology

Figure 2. Pest Analysis Factors

For each specific complex object, there is a special set of the most significant factors that determine its behavior and development.

Pest analysis can be considered as an option of system analysis, because the factors belonging to the listed four aspects generally closely interrelated and characterize various hierarchical levels of society as systems.

In this system, there are deterministic bonds directed from the lower levels of the system hierarchy to the upper (science and technology affect the economy, the economy affects politics), as well as inverse and inter-level connections. The change in any of the factors through this relationship system can affect all other.

These changes may pose a threat to the development of the facility, or, on the contrary, provide new opportunities for its successful development.

The next step is a situational analysis of problems, SWOT analysis (Figure 3):

S. TrengTHS - Strengths;

W. Eakneses - Disadvantages, weaknesses;

O. PportUnities - opportunities;

T. Hreats - Threats.

Figure 3. SWOT-Analysis Factors

It includes an analysis of the strengths and weaknesses of the development of the object under study in their interaction with threats and capabilities and allows you to identify actual problem areas, bottlenecks, chances and dangers, taking into account the factors of the external environment.

Opportunities are defined as circumstances that contribute to the favorable development of the object.

Threats are situations in which damage can be damaged, for example, its functioning may be violated or it can lose their existing advantages.

Based on the analysis of various possible combinations of strengths and weaknesses with threats and capabilities, the problem field of the object under study is formed.

The problem field is a set of problems that exist in the simulated object and the environment in their relationship with each other.

The presence of such information is the basis for determining the goals (directions) of the development and ways to achieve them, develop a development strategy.

Cognitive modeling based on a situational analysis allows you to prepare alternative solutions to reduce the degree of risk in the dedicated problem areas, predict possible events that may be harder to reflect on the position of the simulated object.

Stages of cognitive technology and their results are presented in Table 1:

Table 1

Stages of cognitive technology and its results

Name stage	Form of representation of the result
1. Cognitive (educational-target) structuring knowledge of the test object and external environment for it based on Pest analysis and SWOT analysis: Analysis of the initial situation around the object under study with the allocation of basic factors characterizing economic, political, etc. Processes occurring in the object and in its macroclamination and affecting the development of the object. 1.1 Detection of factors characterizing the strengths and weaknesses of the object under study 1.2 Detection of factors characterizing the possibilities and threats from the outside environment of the object 1.3 Construction of the problem field of the object under study	Report on the system conceptual study of the object and its problem area
2. Building a cognitive model for the development of the object - formalization of knowledge obtained at the stage of cognitive structure 2.1 Isolation and justification of factors 2.2 Establishment and substantiation of relationships between factors 2.3 Building a graph model	Computer cognitive model of an object in the form of an oriented graph (and matrix of relationships of factors)
3. Scenic study of the trends in the development of the situation around the object under study (with the support of the program complex "Situation", "Compass", "KIT") 3.1 Determination of the purpose of the study 3.2 Quest Scripting research and their simulation 3.3 Detection of trends in the development of an object in its macoclaning 3.4 Interpretation of the results of a scenario study	Report on a scenario study of the situation, with interpretation and conclusions
4. Development of strategies for managing the situation around the object under study 4.1 Definition and justification of the goal of management 4.2 Quitory solution 4.3 Selection of management strategies and ordering them according to criteria: opportunities to achieve the goal; risk of loss of control of the situation; Risk of emergency situations	Development Strategy Development Report with Strategies for Different Management Quality Criteria
5. Search and justify the goal achievement strategies in stable or changing situations for stable situations: a) the choice and justification of the goal of management; b) the choice of events (offices) to achieve the goal; c) analysis of the principal possibility of achieving the goal from the current state of the situation using the selected activities; d) analysis of real restrictions on the implementation of selected activities; e) analysis and substantiation of the real possibility of achieving the goal; e) development and comparison of strategies for achieving the goal of: proximity to the results of the management of the intended purpose; costs (financial, physical, etc.); by nature of the consequences (reversible, irreversible) from the implementation of these strategies in the real situation; At risk of emergency situations for changing situations: a) the choice and justification of the current goal of management; b) in relation to the current goal of the previous paragraphs p. bd; c) Analysis of changes occurring in the situation and their mapping in the graph model of the situation. Transition to p. A.	Report on the development of strategies to achieve a goal in stable or changing situations
6. Development of a program for implementing the development strategy of the studied object based on dynamic simulation (with the support of ITHINK software package) 6.1. Dragging resources in directions and in time 6.2 Coordination 6.3 Performance Control	Program for implementing an object development strategy. Computer Simulation Model Development of the Object

2. Problems of cognitive approach

Today, many advanced countries are "spinning" an economy based on knowledge and effective management. The most valuable goods of the state becomes intellectual property. The essence of modern and future war becomes the confusion of intellectuals. In such conditions, indirect and unconventional actions are the most appropriate ways of achieving geopolitical goals and, therefore, the information weapon becomes enormous. There are two concepts of development of strategic arms with different roles in their strategic information weapons (SIO). The first generation SIO is an integral part of the strategic weapons along with other types of strategic weapons and conventional weapons.

The second generation SIO is an independent, radically new type of strategic weapon, which emerged as a result of the information revolution and used in new strategic directions (for example, an economic, political, ideological, etc.). The exposure time in such a weapon can be a much longer period - month, year and more. The second generation SIO will be able to withstand many other types of strategic weapons and will be the kernel of strategic arms. As a result of the use of SIO-2 situations, the situation is a threat to the security of Russia and are characterized by uncertainty, an unclear and fuzzy structure, the influence of a large number of heterogeneous factors and the presence of many alternative development options. This leads to the need to apply non-traditional methods to study geopolitical, information and other processes occurring in Russia and the world, together and interacting both among themselves and with an external unstable environment. Anti-foreign modeling is intended for structuring, analysis and making management decisions In complex and indeterminate situations (geopolitical, internal political, military, etc.), in the absence of quantitative or statistical information on the processes occurring in such situations.

Cognitive modeling allows for express mode

in a short time at a high quality level:

- assess the situation and analyze the mutual influence of existing factors that determine possible scenarios for the development of the situation;

- to identify the trends in the development of situations and the real intentions of their participants;

- develop a strategy for the use of trends in the development of the political situation in the national interests of Russia;

- to determine possible mechanisms for the interaction of the situation participants to achieve its focused development in the interests of Russia;

- to develop and substantiate the directions of management of the situation in the interests of Russia;

- determine possible options for the development of the situation, taking into account the consequences of the adoption of the most important solutions and compare them.

The use of cognitive modeling technology makes it possible to act on ahead and do not bring potentially dangerous situations to threatening and conflicting, and in the event of their occurrence - to make rational decisions in the interests of Russia's constituent entities.

For the tasks associated with organizational systems, the problem of uncertainty in the description and modeling of participants' functions is not a methodological, but the internally inherent subject of research. Different setting of the problem of managing the situation, depending on the completeness of the participants of information about the situation and the rest of the participants, in particular, to search for resonant and synergistic effects, when improving the situation while exposure to several participants more "association" of positive effects from each of the participants separately.

From the point of view of management science today, it is especially important to use a soft resonant management of complex socio-economic systems, the art of which consists in the methods of self-government and self-control systems. Weak, so-called resonant phenomena are extremely effective for "promotion" or self-government, as they correspond to the internal trends in the development of complex systems. The main problem is how small resonant influence to push the system to one of its own and favorable development system, how to ensure self-government and self-sustainable development (self-sustain).

Conclusion

The use of cognitive modeling opens up new possibilities of forecasting and management in various fields:

in the economic sphere, this allows in a short time to develop and substantiate the strategy of economic development of the enterprise, the Bank, region or even a whole state, taking into account the impact of changes in the external environment;

in the field of finance and stock market - to take into account the expectations of market participants;

in the military field and the field of information security, to resist the strategic information weapons, recognizing conflict structures in advance and developing adequate response measures to threats.

Cognitive modeling automates part of the functions of cognition processes, so they successfully can be used in all areas in which self-knowledge claims. Here are just some of these areas:

1. Models and methods of intelligent information technologies and systems for creating geopolitical, national and regional strategies of socio-economic development.

2. Models of survival of "soft" systems in changing media with a deficiency of resources.

3. Situational analysis and development of events in crisis environments and situations.

4. Information monitoring of socio-political, socio-economic and military-political situations.

5. Development of principles and methodology for conducting computer analysis of problem situations.

6. Development of analytical scenarios for the development of problem situations and management of them.

8. Monitoring problems in the socio-economic development of the Corporation, region, city, states.

9. Technology of cognitive modeling of targeted development of the region of the Russian Federation.

10. Analysis of the development of the region and monitoring problem situations in the targeted development of the region.

11. Models for the formation of state regulation and self-regulation of the consumer market.

12. Analysis and management of the development of the situation in the consumer market.

Cognitive modeling technology can be widely used for unique projects for the development of regions, banks, corporations (and other objects) in crisis conditions after appropriate learning.

List of used literature

1. http://www.ipu.ru.

2. http://www.admhmao.ru.

3. Maksimov V.I., Kornowoshenko E.K. Knowledge is the basis of analysis. Banking technologies, No. 4, 1997.

4. Maksimov V.I., Kornowoshenko E.K. Analytical bases for applying a cognitive approach when solving low-resistated tasks. Proceedings IPU, ISP.2, 1998.

5. Maksimov V.I., Kachaev S.V., Kornowoshenko E.K. Conceptual modeling and monitoring of problem and conflict situations in the targeted development of the region. In Sat "Modern management technologies for cities and regions administrations." Foundation "Management Problems", M. 1998.

Figure 3. Cognitive map for analyzing the problem of electricity consumption in the region

Arc () has a "+" sign, as the enhancement of the environment leads to an increase in the number of residents, and the deterioration of the environment of the environment causes outflow of the population. Arc () has a sign "-", since an increase in energy consumption worsens the state of the environment, and the reduction in energy consumption has a beneficial effect on its condition. Arc () has a "+" sign due to the fact that the increase in the number of inhabitants cause an increase in energy consumption and, on the contrary, a decrease in population leads to a drop in energy consumption.

Consider the interaction of factors in the circuit. Suppose that the population has increased. This will lead to an increase in energy consumption and, therefore, will worsen the state of the environment, which in turn will lead to a decrease in the number of residents. Thus, the effect of the pulse in the top will be compensated by the action of the contour, and the behavior of the system is stabilized. Three factors form a contour opposing deviation.

In the circuit, all arcs with the "+" sign, and it is easy to see that an increase (decrease) of any variable in this circuit will be enhanced. As mentioned above, on the mathematical language, the cognitive map is called a sign-based oriented graph. Under the contour, the column means a closed oriented path, all the vertices of which are different.

The contours in a cognitive map correspond to feedback contours. The contour, enhancing the deviation, is a circuit of a positive feedback, and the contour opposed by the deviation is a negative feedback circuit. Japanese scientist M.Marayama called these contours respectively morphogenetic and homeostatic. In the same work, Maruyam proved that the contour enhances the deviation if and only if it contains an even number of negative arcs or does not contain them at all, otherwise it is a contour opposing deviation. Indeed, in the case of an even number of negative arcs, the opposition will be to meet the opposition itself. If the number of negative arcs is odd, then the latter opposition does not meet the opposition.

This analysis scheme mainly corresponds to intuitive ideas about causality. It is clear that the interaction of two factors and can obey more complex patterns, but in this case, the languages \u200b\u200bof functional relationships should be used to describe the process under study.

The experience of using cognitive maps shows that the researcher often excessively simplifies the situation due to limited cognitive capabilities, the difficulties of simultaneous accounting for a large number of factors, their dynamic interaction. M. Vertheimer wrote that the researcher often lacks the breadth of vision in difficult situations, including several submissions, an understanding of the whole is lost, a narrow look at the problem is imposed.

In the monograph D. Hake, dedicated to the caused analysis, it is emphasized that only a few interesting phenomena in public sciences depend on only one reason. Public phenomena usually include many different events, trends defined by several factors, each in turn affects some number of other factors. The networks of causal relationship are formed, i.e. causality is systemic. The causality causes the model of social phenomena, and the study of models ensures the deepening of the understanding of the causal relationships that they spent them.

Analyzing its and other cognitive cards, the researcher can quickly deepen understanding the problem, improve the quality and validity of the decisions made. In addition, the cognitive map is a convenient tool for changing well-established stereotypes, contributes to the generation of new points of view. Thus, in the work of M. Maruyama, an example of an erroneous belief (cognitive cliché) is given, that the trade of the two countries is a game with a zero amount. If one partner wins, then the other than the same loses. This belief is a psychological background of the war restrictions on the importation of goods (imports).

For a country that has a deficit in trade with another country, at first glance there are two equivalent ways to improve trade balance: reduce imports and increase exports. However, war restrictions leads to a negative total effect: due to the reduction of capital turnover between the two states, the increase in unemployment lose both parties. On the contrary, mutual export expansion increases capital circulation rate and gives a positive effect for both states.

The cognitive card is especially useful for the analysis of the action difficult to formalizable factors, the dimension of which is often a very difficult problem.

English scientist K.idan offered to use cognitive cards for collective development and decision-making. K.iden emphasizes the importance of the fact that the effectiveness of interaction in the group of decision-making persons significantly depends on how each participant understands ways to interpret situations by other members of the Group. An important role in obtaining consensus is played by members of the Unity Group in the method of constructing future events, the processes of "strengthening understanding", "change of symbols", identifying new points of view. We need a tool for fixing and analyzing opinions, which are often based on experience and intuition of experts. It is important to be able to record contradictory points of view of experts without losing the wealth of argumentation. The cognitive map makes it possible to trace the relationship between the future, the present and the past under the process.

It is clear that the use of cognitive cards for planning in an organization may require fixation of several thousand interconnected statements. Therefore, to write, storing, searching and analyzing information, it is necessary to use computer and special software. Currently, a number of commercial packages for analyzing cognitive cards (Nipper, Cope, Gismo) have been developed.

EUM can be used for the following purposes:

§ search for concepts containing a certain set of keywords;

§ search clusters in the map, i.e. groups of interrelated concepts that are close to each other;

§ finding card outputs (statements without consequences);

§ search for statements that are central to a large number of arguments;

§ definitions of allegations with the highest argumentation;

analysis of links of expressed opinions with the structure of the organization.

The cognitive card is a "synthetic wisdom" of the organization's team and accumulates the views of people, many of whom have never met. Each participant of the process must be confident that his opinion is taken into account and may affect the organization's strategy. Therefore, it is desirable that employees of the Organization are included in this process on a regular basis, and they should know that the remaining employees are also included in the process of forming a strategy. With the help of various working groups and committees, there is a study of individual parts of the strategic plan and, most importantly, the effects of feedback are monitored.

This approach allows you to get rid of a number of circumstances that impede the adoption of effective solutions: a narrowing of the view on reality under the influence of the usual experience, boredom and the ritual nature of planning, the institution of organizational structures, the influence of stereotypes, ambitions, etc.

The technique of cognitive modeling, as a rule, implies a certain sequence of actions. It involves the separation of the medium, relevant to the problem under study, on the outer and internal. The external environment is something that practically and clearly does not depend on the person (manager, subject, leader, leader, organization, etc.) interested in resolving the problem arising, but affects the solution of this problem, and internal is that this person can change.

Then the factors (concepts, concepts) are allocated, characterizing the situation and estimated the mutual influence between them. Sometimes factors immediately distribute to positive (positive) and negative (negative) factors. Such factors can first be overly much to help solve the problem (about 100-120), therefore, they use special procedures for compression, allowing them to be reduced to 5-5.

At the next stage, it is necessary to attract expert assessments to fill the resulting scheme with specific meanings, which makes it possible to answer the question of what factors can affect directly, as well as factors whose values \u200b\u200bwould be changed, but it is impossible to do it directly.

Based on expert assessments and their appropriate analysis, possible scenarios for the development of the situation and options for the activities undertaken, on the basis of which the simulation of the situation in its dynamics is being implemented. The result of cognitive modeling should be the formulation of the most disturbing strategy of actions that takes into account not only the external benefits and restrictions, but also the requirements of the internal environment.

Cognitive modeling is, first of all, the rapid receipt of answers to the questions like "What will happen if ...?" And "What should I do to ...?" Through the identification of factors and their mutual influences in a folding weak-substitable and unstable situation, where people have a great influence on the dynamics of the problem of solving problems, and the task is most often reverse and incorrect.

The development trends of cognitive modeling methods are formed in the context of improving the methods of situational analysis, as well as other theoretical and applied analytical studies, namely:

· From informing participants - to the extraction of knowledge and understanding;

· From reference work - to analytical;

· From one participant to groups;

· From the analysis of the inner medium - to the external;

· From extrapolating trends - to find extraordinary purposes and paths;

· From registration of data - to knowledge management;

· From information security - to sustainable management;

· From accuracy - to intellectuality;

Cognitive modeling is a unique and practical way to support strategic and tactical management, ensuring the growth of confidence in the leader; Enhance confidence in the correctness of actions; achieve the satisfaction of management from the quality of meetings; prompt search for good measures and solutions; warnings of conflicts and crises; deep understanding of problems; Convenient and visual resource management.

The technology of cognitive analysis and modeling (Fig. 4) is cognitive (cognitive-target) structuring knowledge about the object and the external environment for it.

Medium-term forecasting of the Russian economy using a cognitive model

The article substantiates the feasibility of applying a cognitive approach for research and prediction of the resource-dependent economy. The results of modeling the medium-term forecast of the Russian economy using a fuzzy cognitive card will be presented.

Resource dependence, uncertainty and forecasting. Specific features of the economy of modern Russia are resource dependence, transitional type of development and crisis state of the economy. Resource dependence gives rise to various kinds of adverse trends, the extension of which is very undesirable, as it significantly limits the possibilities of forecast extrapolation. The transitional state of the economy is associated with the "mental imperfection inherited from the past years, the lack of sustainable trends and mature economic structures, which makes the" achieved level "not too reliable base for forecasting. The same can be said about the crisis in the economy, especially considering it to a large extent "man-made" nature associated with state economic policies and aggressive external influences. In general, the deterioration of the country's economic situation, which occurs since 2013, "Deeply natural and caused by the internal reasons for a fundamental nature".

One of the factors of braking economic growth is the dependence on world oil prices, the decrease in which minimizes the positive effect of increasing hydrocarbon production. The problem of uncertainty is highly inherent in a resource-dependent economy, since along with the factors of development traditional for all economies, factors associated with the development of natural resources are gaining significant impact. In the Russian economy, fundamental uncertainty 2 it is due to the resource-raw material nature of development over the past decades. Moreover, as the scale and the degree of maturity of the resource-commodity sector increases, the uncertainty inherent inherent in the sector, but also the economy as a whole. Thus, it can be said that the "beam" of complex and far from obvious economic and political ties is affected by the resource-dependent economy, and from this point of view, the Russian economy is no exception.

Applied forecast model of the Russian economy. The methodology for cognitive modeling, intended for analysis and decision-making in weakly defined situations, is proposed by the American researcher R. Axelrod. It is based on modeling subjective presentations of experts on the situation, its main tool is a cognitive map of the situation (Fuzzy Cognitive Map), composed in the form of a focused functional graph. The vertices (concepts) of the graph correspond to the factors under consideration (events), and directional arcs characterized by signs and parameters of intensity reflect the mutual influence between factors (events). The cognitive card is used to identify the structure of the causes of ties between the elements of the system and assess the effects of impact on them or changes in the nature of the relationship.

1 The article was prepared as part of the research with the financial support of the Russian Scientific Fund(Project No. 14-18-02345).

2 Fundamental uncertainty eliminates the possibility of correct transformation in a risk situation. The use of the term "risk" is associated with cases where the degree of uncertainty or the likelihood of a certain event can be measured. The practical difference between the risk and uncertainty categories is that in the first case the distribution of the results of events is known (which is achieved by a priori computing or studying the statistics of previous experience), and in the second - no.

The implementation of modeling procedures is usually divided into three stages. The first stage is modeling (imitation) of self-development situations (system) in the absence of control influences "from the side of the researcher. The second stage implies a managed situation of the situation: a researcher as a result of impact on any factors determines the control factors and varies them, observing the changes in the system. The third stage is a solution to the opposite problem, which is to determine the values \u200b\u200bof the control pulses required to solve the problem. Thus, in the process of numerical implementation of the cognitive model, various scenarios of the forecast of the situation of the situation (system) can be built: without management and management to attenuate negative or enhancing positive trends.

The use of the cognitive modeling method justifies itself in theoretical, and in applied research. The use of cognitive models in the study of patterns and mechanisms of resource dependence on the analysis of interactions of endogenous and exogenous factors and their impact on economic growth is considered in one of our works. As examples of applied research, work on cognitive modeling of socio-economic ratings in the Republic of Komi and the development of the tourist and recreational system of the South of Russia is possible. Our task is made wider: to evaluate the influence of key factors on the dynamics of the socio-economic development of Russia, which involves the construction of an aggregated construction covering the entire socio-economic system of the country. According to its formulation, this task is close to well-known foreign studies, in one of which theoretical cognitive model of the economy is presented, and in the other - a model built to assess the socio-economic consequences of exploration of oil and gas resources in Cyprus. From domestic studies, we will especially note the work, where the cognitive model is presented, with the help of which the main factors affecting the process of creating an innovative economy in Russia, and shows the priority impact of industrial policy on economic growth.

Our conceptual approach and technique of working with applied cognitive models are characterized in the work, where the results of modeling the medium-term forecasting of the socio-economic development of the Tomsk region are presented and meaningfully interpreted. This region is interesting in that it is at the same time resource and innovative, in its economy the oil and gas sector, manufacturing industry and the scientific and educational complex play a major role. The Tomsk region can be described as a kind of "large-scale model" of Russia - with a close structure of the economy, similar achievements and problems in socio-economic development. It should be noted that the comparability of oil and gas production indicators (as one of the main sources of income) per capita: in the Tomsk region - approximately 15 tons. e. / person, in Russia - about 8 tons. e. / person . 3

The results of research on the problems of the socio-economic development of the Tomsk region allowed to come to the conclusions, which can largely be correlated to the whole country. Therefore, proceeding to work on the forecast model of the Russian economy, we focused on the results of previous studies and on the practical experience of building cognitive models obtained in these studies.

3 For comparison: the average per capita rates of hydrocarbon production in Yamalo-Nenets AO account for about 1 thousand tons, in Nenets and AO - more than 440, in the Khanty-Mansiysk AO - 190, in the Sakhalin region - 70 tons (calculated according to Rosstat).

The developed model of the Russian economy has a forecasting horizon until 2020. The model cognitive map contains 16 factors broken into 6 classes (Table 1), interconnected by the 121st arc that simulates mutual influence.

Table 1. Factors of the applied forecast model of the Russian economy

Class factors	Factor characteristics	Designation
Basic resource	Oil and gas resources (in production indicators, million tons. E.) Human capital (accumulated costs of formation, billion rubles)	0-1 Oil 0-2 human capital
Correct financial flows	Investments in fixed assets (billion rubles) Revenues and budget expenditures (billion rubles) Admission of foreign direct investment (FDI, million dollars) production costs (billion rubles) Innovation costs (R & D expenses, billion rubles)	1-1 Investments 1-2 budget 1-4 Costs 1-5 Innovations
The main shopping complexes	Oil and gas sector (gross value added, billion rubles) Industry (manufacturing, gross value added, billion rubles.) Scientific and educational complex (NOK, gross value added, billion rubles)	2-1 NGS 2-2 Industry
Providing factors	Infrastructure (production of infrastructure branches and providing activities, billion rubles) Technology level (high-quality variable *) Level of the development of the social sphere (high-quality variable)	3-1 Infrastructure 3-2 Technology 3-3 Social Sphere
Externalia	External situation (oil prices, dollars / barrel.) External risks - financial, political, regulatory, etc. (qualitative variable)	4-1 prices
Target factor	Economic Development Level (per capita GDP, thousand rubles)	5-1 GDP
* Qualitative (non-measured) variables reflect different states, each of which corresponds to a certain numerical equivalent. The presence of one model of quantitative and high-quality variables is possible, since the solution of the solution is aimed at obtaining not absolute values, but dynamic (increase) characteristics in terms of deterioration or improved situation.

The preliminary values \u200b\u200bof the intensity of mutual influence between the measurable factors of the cognitive model were established by correlation analysis. Pairing correlations between the time series of data were considered (for the period 2000-2013) by factors given in Table. 1. Further, the coefficients were specified by the expert, according to the logic of the system transition from one stationary state to another as a result of external impulse effects.

It should be noted that this is one of the most complex and non-obvious to the perception of the nuances of cognitive modeling, because any cognitive model is subjective representation of the expertabout processes in a complex dynamic situation (system), formally represented in the form of a oriented iconic graph. The question arises: can such subjectivity be justified? Will it lead to the receipt of distorted concepts about the laws of the development of the system under study?

The problem of subjectivity can be largely solved with reverse verification, i.e., by checking models under certain conditions, their "dives" in the past. We tested the model for a retrospective period of 2000-2013. Based on disposable statistical data on measurable model factors. At the same time, the initial trends in the following factors are given in the vector: 0-1 oil (+ 31%); 1-3 FDI (+ 28%); 4-1 Prices (+ 182%) - based on existing statistical data - and 4-2 risks (-70%) are assessed, based on realistic hypothesis about a significant overall reduction in risks for the Russian economy in the 2000s compared with 1990 " Factor "Oil" We are considering along with external influences (world oil prices, FDI, risks), since the dynamics of oil and gas production in Russia is more closely related to the market situation and export opportunities than with the needs of the national economy.

The general correctness of the model at this stage was confirmed by the proximity of the factors calculated on the rates of growth rates to the actual growth rates in 2013 compared to 2000. The estimated growth rate of GDP amounted to 78% compared with the factual indicator at the level of 79% (Table 2 ). As a result, the matrix of the coefficients of the mutual influences of the verified model, which was used to build a prediction for the period up to 2020

Table 2. Estimated and actual growth rates of model indicators: 2013/2000,%

The results of modeling the medium-term forecast. At the first stage of numerical modeling, the self-development of the situation was imitated, and the growths of the "oil" and "prices" were the sources of impulse exposure to the system. It was assumed that the production of hydrocarbons in the Russian Federation will increase by about 10% by 2020 compared with 2013 (up to 1250 million tons in n. E. - in the landmarks of the energy strategy of Russia for the period up to 2030), and the price of Oil will decrease by about 40% (according to the extrapolation of scenario conditions for the forecast of the socio-economic development of the Russian Federation for the period up to 2018, the Ministry of Economic Development of Russia). Hypotheses regarding the change in the size of FDI and external risks were not considered.

Calculations have shown that for given impulse effects, the forecast change in the GDP factor in 2020 is: -12%, budget revenues will decrease by 22%, investments in fixed assets - by 28%; Gross added value of the manufacturing industry will decrease by 9%, the scientific and educational complex - by 7% compared to the level of 2013. Thus, when self-regulation (self-development), crisis trends in the Russian economy are predicted. In view of the undesirability of this outcome, targeted impacts on the economic system are needed to form more favorable results.

At the stage of imitation of managed development of the system as factors subject to control influences, the following were selected (see Table 1): Investments, FDI, Industry, Nok, Infrastructure, Risks. This implies the state stimulation of relevant economic processes, sectors of the economy and activities by conducting purposefully regulated policies. In addition, measures are considered to reduce risks and stimulating economic growth (at the macro level). The consistently asked "weak" increments of the values \u200b\u200bof all the factors listed above at 10% (risks - reduction by 10%) made it possible to estimate the sensitivity of the economy to the control influences according to these regulation directions.

In the process of experiments on the model, indicators of GDP factor growth were obtained in the range from -12 to + 2% by 2020. Regarding 2013. If we consider individual factors, then the most effective risk reduction measures are most effective. The conditional combination of the weak impact of all considered factors leads to an increase in GDP by about 2% (Table 3).

Table 3. GDP increase in per capita in 2020 in relation to the level of 2013 according to model calculations,%

The modeling result corresponds to an unfavorable scenario of economic development. The obtained indicators are lower than the forecast reference points of the Ministry of Economic Development of Russia 2020: According to the long-term development developed by the Ministry, the conservative scenario of long-term development, the GDP increases should be 29% by 2013 compared with 2013. The extrapolation of scenario trends according to the forecast for 2018 gives growth rates by 2020 (in comparison with 2013) by 10 and 16%.

The required intensity of the impact on the control factors at a given increment of the target factor can be calculated in the third stage of modeling - solutions of the inverse problem. As a target, we will accept the growth rate of GDP per capita by 2020 regarding 2013 equal to 16%. When modeling in this case, it was found that the greatest influence intensity is required to stimulate FDI and the development of NOC, and the smallest industry, infrastructure and risks (Fig. 1).

Fig. 1. The calculated values \u200b\u200bof the intensity of the control influences necessary to achieve the GDP target increase by 2020 by 16% compared with 2013.

In other words, to ensure economic growth requires relatively small - due to a fairly powerful basis - efforts aimed at stimulating industry and infrastructure, and maximum regulatory efforts are necessary to attract investments and develop the innovative sector.

The results of the forecast estimate show that the necessary increase in investment should be almost two and a half times higher than the increase in the target indicator (Fig. 2), as it was, for example, in the period 2001-2007. The forecast growth of the NOC is relatively slow, despite the high intensity of the calculated control impact. Probably, the reason is in the most considerable nature of the development of the innovation sphere, when the work of the NOC is assessed to a greater extent on the cost of innovation (the share of R & D spending in GDP), and not according to the real effect of the economy.

Fig. 2. Forecast growth indicators of the model factors by solving the inverse problem (2013 \u003d 100)

In general, the results of the solution of the opposite problem, in our opinion, are quite natural. It should be primarily to form a favorable investment climate that contributes to the accumulation of the internal and influx of external investment, as well as the innovative nature of the development of the economy: the relationship of these factors in the system will contribute to strengthening the positive effects of other factors on the target indicator from the part.

The results obtained, in our opinion, very meaningful, the results should be recognized in many ways prior. A further study of the possibilities of cognitive modeling is required to substantiate economic forecasts and regulatory policies, primarily when choosing its priority areas. Based on its experience, we can note that the cognitive approach is most effective in analyzing and predicting the development of complex economic systems. The peculiarity of this approach is to apply the methods of quantitative analysis in combination with the construction of model structures based on the subjective vision of the situation. Each stage of work relies on the solution of the researcher, the total of which determines the adequacy of the model. It should be especially noted that cognitive models cannot replace models of other types and classes, they only have to occupy their "niche" as part of a mathematical instrumental in economic studies, including the solution of a projected nature. We believe that the further development of a cognitive approach to the study of the Russian economy will allow efficient tools and to build forecasts, and to substantiate decisions on the management of emerging problem situations.

Literature

Aganbegian A.G. Socio-economic development of Russia: analysis and forecast // Problems of forecasting. 2014. No. 4. P. 3-16.
Knight F. Risk and uncertainty // Thesesis. 1994. № 5. P. 12-17.
OlsevichYu.Y.Fundamental uncertaintymarketand the concept of a modern crisis. M.: Institute of Eco-Nomiki RAS, 2011. 51 p. URL: http://www.inecon.org/images/stories/nauchnaya-jizn/konverensii/olsevich_8-08-2011.pdf.(Reference date 02/13/2016).
Shmat V.V. Resources in the "Western" globalization // Eco. 2015. No. 7. P. 163-178.
Axelrod R. The Structure of Decision: Cognitive Maps of Political Elites. PrinceTon // NJ: Princeton University Press, 1976. 404 p.
Morozova M.E., Shmat V.V. How to know the resource dependence mechanisms? Applying the method of cognitive modeling in the study of the resource-dependent economy // Eco. 2015. No. 6. P. 146-159.
Lavresh I.I., Mironov V.V., Smirnov A.V. Cognitive modeling of regions socio-economic rates // Bulletin ITark. 2011. No. 1. P. 22-30.
Solokhin S.S. On cognitive modeling of sustainable development of socio-economic systems (on the example of the tourist and recreational system of the south of Russia) // Artificial Intellect. 2009. № 4. P. 150-160.
CARVALHO J.P., Tome Jose A.B. Rule Based Fuzzy Cognitive Maps in Socio-Economic Systems // IFSA-EUSFLAT 2009 Proceedings. Lisbon. 2009. PP. 1821-1826. URL: http://www.eusflat.org/proceedings/ifsa-eusflat_2009/pdf/tema_1821.pdf. (Reference date 02.2016).
Neocleous. , Schizas. C. Papaioannou M. Fuzzy cognitive. maps. iN. estimating their rEPERCUSSONSIONS. of. oil / Gas. exploration oN. politico-Economic issues. iN. Cyprus. // 2011 IEEE. INTERNATIONAL. Conference. ON. Fuzzy Systems. Taipei, Taiwan: IEEE, 2011. 1119-1126. URL: http://ieeeexplore.ieeee.org/xpl/mostrecentissue.jsp?punumber\u003d5976945 (date of the application13.02.2016).

Kuleshov V., Alekseev A., Jagolnizer M. Road Map Reindustrial Policy: Cognitive Toolkit // Economist. 2015. № 10. P. 51-63.
Bellan A.K., Shmat V.V. Analysis of the impact of resource and imminent factors on the growth of the economy of the Tomsk region with the use of a cognitive approach // Bulletin of NSU. Series: Socio-economic sciences. 2015. T. 15. Vol. 1. P. 78-93.
Unified interdepartmental information and statistical system (EMISS). URL: https://www.fedstat.ru/indicators/data.do (Reference date 02/13/2016).
Energy Strategy of Russia for the period up to 2030. Approved by the order of the Government of the Russian Federation of November 13, 2009 №1715-p. M.: Institute of Energy Strategy, 2009. URL:http://www.energystrategy.ru/projects/es-2030.htm. (Reference date 02.2016).
Scenario conditions, the main parameters of the forecast of the socio-economic development of the Russian Federation and the limiting levels of prices (tariffs) for the services of the infrastructure sector companies for 2016 and on the planning period of 2017 and 2018. M.: Ministry of Economic Development of the Russian Federation, May 28, 2015. URL:http://economy.gov.ru/minec/activity/sections/macro/prognoz/201505272. (Reference date 02.2016).
Forecast long-term socio-economic development Russian Federation on the period before 2030 of the year. M.: Mi-non-amradeRF8 Nov. 2013. URL:http://economy.gov.ru/minec/activity/sections/macro/prognoz/doc20131108_5 (date of the application 02.2016).

The theory of creating organizational knowledge I. Nonaki and H. Takechi.

Individual and organizational training.

Cognitive analysis and modeling in strategic management

The essence of the concept of cognitiveness. Cognitive organization.

Theme 5. Cognitive as a prerequisite for the strategic development of the enterprise.

5.1. The essence of the concept of "cognitiveness". Cognitive organization.

Cognitology- Interdisciplinary (philosophy, neuropsychology, psychology, linguistics, informatics, mathematics, physics, etc.) Scientific direction, learning methods and models of knowledge of knowledge, knowledge, universal structural schemes.

Cognitiveness (from Lat. Cognitio - knowledge, study, awareness) As part of science management means the ability of managers to mental perception and processing of external information. At the heart of the study of this concept there are mental processes of personality and the so-called "mental states" (confidence, desire, conviction, intention) in terms of information processing. This term is also used in the context of studying the so-called "contextual knowledge" (abstraction and concretization), as well as in areas where concepts such as knowledge, skills or training are considered.

The term "cognitiveness" is also used in a broader sense, means the "act" of knowledge or identity itself. In this context, it can be interpreted as the appearance and "becoming" of knowledge and concepts associated with this knowledge reflected both in thoughts and actions.

Cognitiveness of the organization It characterizes the set of cognitive abilities of individual people in the company and those effects that arise when combining individual cognitive abilities. The application of this concept with respect to the company (organization, firm, enterprise) means the intention to consider it in the plane, which is characterized by a specific analysis apparatus and a special angle of view on the interaction of the enterprise or its components with an external environment.

Term "Cognitiveness of the organization" Allows you to assess the company's ability to assimilate information and turn it into knowledge.

One of the most productive solutions to the problems arising in the field of management and organization is to apply cognitive analysis.

The methodology for cognitive modeling, intended for analysis and decision-making in poorly defined situations, was proposed by the American researcher R. Axelrod.

Cognitive analysis is sometimes referred to as researchers with "cognitive structuring". Cognitive analysis is considered as one of the most powerful tools for studying an unstable and low-rested environment. It contributes to a better understanding of the problems existing in the medium, detecting contradictions and qualitative analysis of the flowing processes.

The essence of cognitive (cognitive) modeling - key point of cognitive analysis - It is that the most complicated problems and trends in the development of the system to reflect in a simplified form in the model, to explore possible scenarios of the occurrence of crisis situations, find ways and conditions for their permission in the model situation. The use of cognitive models will qualitatively increases the reasonableness of making management decisions in a complex and rapidly changing environment, eliminates the expert from "intuitive wandering", saves the time to understand and interpret the events occurring in the system. The use of cognitive technologies in the economic sphere allows for a short time to develop and justify the strategy of economic development of the enterprise, taking into account the impact of changes in the external environment.

Cognitive modeling - This is a way to analyze the determination of the strength and direction of the influence of factors for the transfer of the control object into the target state, taking into account the similarities and differences in the effect of various factors on the control object.

The cognitive analysis consists of several stages, each of which is implemented a certain task. The consistent solution of these tasks leads to the achievement of the main goal of cognitive analysis.

The following steps characteristic of the cognitive analysis of any situation can be distinguished:

1. Formulation of the purpose and objectives of the study.

2. Studying a difficult situation from the position of the goal: Collecting, systematization, analysis of existing statistical and qualitative information regarding the object of management and its external environment, determining the requirements of the investigated situations, conditions and restrictions.

3. Allocation of the main factors affecting the development of the situation.

4. Determination of the relationship between factors by considering causal chains (building a cognitive card as an oriented graph).

5. Study of the strength of mutual influence of various factors. For this purpose, both mathematical models describing some of the exactly identified quantitative relationships between factors and subjective reports of an expert relative to informalized qualitative relationships of factors.

As a result of the passage of the steps 3 - 5, it is ultimately a cognitive model of the situation (system), which is displayed as a functional graph. Therefore, we can say that steps 3 - 5 are cognitive modeling.

6. Check the adequacy of the cognitive model of the real situation (verification of the cognitive model).

7. Determination using a cognitive model of possible options for the development of the situation (system), detection of paths, mechanisms of impact on the situation in order to achieve the desired results, prevent undesirable consequences, that is, the development of management strategy. The task of the target, desired directions and the forces of changing the trends of processes in the situation. The choice of a set of measures (aggregate of managing factors), determining their possible and desired force and the direction of impact on the situation (the specific practical application of the cognitive model).

As part of a cognitive approach, the terms "cognitive card" and "oriented graph" are used as equivalent; Although, strictly speaking, the concept oriented graph is wider, and the term "cognitive card" indicates only one of the applications of the oriented graph.

Classic cognitive map- This is an oriented graph in which a privileged vertex is some future (as a rule, the target) state of the control object, the remaining vertices correspond to factors, arcs that connect factors from the vertex of the state have a thickness and a sign corresponding to the strength and direction of the influence of this factor on the control of the control object To this state, and arcs connecting factors show similarities and differences in the effect of these factors on the control object.

The cognitive card consists of factors (system elements) and connections between them.

In order to understand and analyze the behavior of a complex system, build a structural scheme of causal relationships of elements of the system (situation factors). The two elements of the system A and B are depicted in the diagram in the form of individual points (vertices), connected by an oriented arc, if the element A is associated with the element in the causal relationship: A À B, where: A - the reason, in the consequence.

Factors can influence each other, with such an effect, as already mentioned, can be positive when an increase (decrease) of one factor leads to an increase in (decreasing) of another factor, and negative when an increase (decrease) of one factor leads to a decrease (increase ) another factor. Moreover, the effect may also have a variable sign depending on the possible additional conditions.

Such diagrams of presentation of causal relations are widely used to analyze complex systems in economics and sociology.

Example. The cognitive structural scheme for analyzing the energy consumption problem may have the following form (Fig. 5.1):

Fig. 5.1. Cognitive Structural Scheme for Analysis Energy Problems

The cognitive map displays only the fact of the presence of influences of factors on each other. It does not reflect the detailed nature of these influences, neither the dynamics of changes in influences depending on the change in the situation nor the temporary changes in the factors themselves. Accounting for all these circumstances requires the transition to the next level of information structuring, that is, to the cognitive model.

At this level, each connection between the cognitive card factors is disclosed by the appropriate dependencies, each of which may contain both quantitative (measured) variables and high-quality (non-measured) variables. In this case, quantitative variables are naturally presented in the form of their numerical values. The same high-quality variable is put in accordance with the set of linguistic variables displaying various states of this high-quality variable (for example, buying demand may be "weak", "moderate", "hype", etc.), and each linguistic variable corresponds to a certain numerical equivalent. in scale. As knowledge of the processes occurred in the study situation is accumulated, it becomes possible to disclose the nature of the relationship between factors in more detail.

Formally, the cognitive model of the situation can, as well as a cognitive card, be represented by the graph, but each arc in this graph represents some kind of functional relationship between the corresponding factors; those. The cognitive model of the situation seems to be a functional graph.

An example of a functional graph reflecting the situation in the conditional region is presented in Fig. 5.2.

Fig.5. 2. Function graph.

Note that this model is a demonstration, so many factors of the external environment are not taken into account.

Such technologies conquer more and more confidence in structures that are engaged in strategic and operational planning at all levels and in all areas of management. The use of cognitive technologies in the economic sphere allows for a short time to develop and justify the strategy of economic development of the enterprise, taking into account the impact of changes in the external environment.

The use of cognitive modeling technology makes it possible to act on ahead and cannot be given potentially dangerous situations to the level of threatening and conflicting, and in the event of their occurrence - to take rational solutions in the interests of the enterprise.

Cognitive modeling

Introduction

1. Concepts and essence of "cognitive modeling" and "cognitive card"

2. Problems of cognitive approach

Conclusion

List of used literature

Introduction

Cognitive analysis and modeling are fundamentally new elements in the structure of decision support systems.

1. Concepts and essence of "cognitive modeling" and "cognitive card"

Cognitive card (from Lat. Cognitio-knowledge, knowledge) - the image of a familiar spatial environment.

The basis of cognitive analysis and modeling technology (Figure 1) is cognitive (cognitive-target) structuring knowledge of the object and the external environment for it.

Figure 1. Technology of cognitive analysis and modeling

The results of cognitive structuring are displayed using a cognitive card (model).

2. Cognitive (educational-target) structuring knowledge about the test object and external environment for it based on Pest analysis and SWOT analysis

The selection of basic factors is carried out by applying Pest analysis, allocating four main groups of factors (aspects), which determine the behavior of the object under study (Figure 2):

P.olicy - politics;

E.conomy - economy;

S.ociety - Society (socio-cultural aspect);

T.echnology - Technology

Figure 2. Pest Analysis Factors

For each specific complex object, there is a special set of the most significant factors that determine its behavior and development.

These changes may pose a threat to the development of the facility, or, on the contrary, provide new opportunities for its successful development.

The next step is a situational analysis of problems, SWOT analysis (Figure 3):

S.trengTHS - Strengths;

W.eakneses - Disadvantages, weaknesses;

O.pportUnities - opportunities;

T.hreats - Threats.

Figure 3. SWOT-Analysis Factors

Opportunities are defined as circumstances that contribute to the favorable development of the object.

Threats are situations in which damage can be damaged, for example, its functioning may be violated or it can lose their existing advantages.

Based on the analysis of various possible combinations of strengths and weaknesses with threats and capabilities, the problem field of the object under study is formed.

The problem field is a set of problems that exist in the simulated object and the environment in their relationship with each other.

The presence of such information is the basis for determining the goals (directions) of the development and ways to achieve them, develop a development strategy.