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.

A cognitive approach to the study of complex systems, such as socio-economic, political, etc., a number of concepts associated with this, as well as the methodology and technology of cognitive modeling complex systems are considered.

Mathematical presentation of cognitive models

The beginning of research related to the use of a cognitive approach to studying, modeling, making decisions in the field of complex systems, refers to the middle of the XX century, when the ideas of cognitive psychology began to be applied in various branches of knowledge and the system of disciplinary studies, named "Cognitive Science" began to develop ( English cognitive Science). Its main directions are philosophy, psychology, neurophysiology, linguistics, artificial intelligence. Currently, there is an expansion of the subject areas in which a cognitive approach is used. The active application of a cognitive approach in the studies of complex systems in our country was launched in the 1990s., IPU RAS became the center of research. This paragraph presents a number of the results of cognitive research of complex systems held in the Southern Federal University, the origin of which can be considered the work of R. Axelrod, F. Roberts, J. Caste, R. Etkina, as well as employees IPU RAS (V. I. Maximova, V. V. Klub, N. A. Abramov, etc.).

To understand the meaning of cognitive research, their directions, models and methods, the knowledge of a number of special terms, such as: cognitive science and cognitivist, cognitology (knowledge engineering), cognitive approach (cognitive), cognitive (cognitive-target) modeling technology, visualization, cognitive Modeling, cognitive structure or conceptualization, cognitive modeling methodology, cognitive model, cognitive map. The definitions of these concepts (and a number of others related to cognitive sciences can be found in the works. Cognitive cards have not only visual, but also a mathematical justification. These are clear and fuzzy graphs (fuzzy cognitive cards).

The Count turns out to be a suitable model for presenting relations between economic objects (enterprises, organizations, means and factors of production, elements of the social sphere, characterized as an object in which economic activity focuses or to which economic relations are directed and representing a certain side of economic relations), between social processes (for example, people, groups of people), between the subsystems of socio-economic systems, between other concepts, entities, etc. We use the definition of F. Roberts: "An iconic graph (a sign orgraf) is a graph in which" ... vertices correspond to members of the group; From the vertex V-, An arc is carried out in the top if there is a distinct relation to V; to V, and the arc vD \u003d (V, V]) has a plus sign (+) if V, "sympathizes" U ^ I. The sign minus (-) otherwise. "

The concept of "sign orgraf" can have a variety of applications, so arcs and signs are interpreted differently depending on the studied complex system. In addition, theoretical studies of complex systems are developing within a more complex model, rather than a sign orgraf - as part of a suspended orgraf, in which each arc eC. attributed a valid number (weight) hyu.

An example of a cognitive card is shown in Fig. 6.12 (Figure is made using the PSC software system ^). Solid lines of arcs match Sht \u003d +1, barcupotyre - = -one. The sign can be interpreted as "positive (negative) changes at the top of the r\u003e lead to the preferred (negative) changes in the top of the GU", i.e. These are unidirectional changes; The sign "-" - as "positive (negative) changes in the top lead to negative (positive) changes in the vertex VJ "- Metrated changes. Counter arrows display the interference of the vertices, the graph cycle; Such a relation is symmetrically. Most concepts of orgraves also apply to suspended orgraves. These are concepts: path, simple way, half empty, contour, cycle, half-door; Strong, weak, one-sided connectivity, "sign of the path, closed path, contour."

Sign of path, chains, closed path, closed chain, cycle contour, etc. Determined as the work of the signs of the arcs in them.

Obviously, the path, cycle, etc. have a sign if the number of negative arcs contained in them is odd, otherwise they have a "+" sign. So, for the graph "Romeo and Juliet" Path V, - "V, -" W. -> V, is negative, and cycle Wow -\u003e -\u003e V, - positive.

Fig. 6.12. Doug gOU \u003d +1 I. Shz \u003d -1

With mathematical modeling of complex systems, the researcher arises the problem of finding a compromise between the accuracy of modeling results and the ability to obtain accurate and detailed information to build a model. In such a situation, iconic and weighted orgraves are suitable for the development of "simple" mathematical models and when analyzing the results obtained with minimal information.

We give two more examples from [Noesh, from. 161, 162] - Fig. 6.13 and 6.14, interesting from a historical point of view as one of the first cognitive cards, but not lost relevance and now.

In fig. 6.14 contour Wow -\u003e U - \u003e $ -\u003e U6 - " Wow counteracts the deviation in the top of V ,. If you increase / reduce any variable in this circuit, these changes are driven through other vertices to a decrease / increase in this variable (interpretation: the more population, the more waste, the greater bacteria, the greater the incidence - the more incidence, the less people, etc.). This is the contour of the negative feedback. Contour V, -\u003e U -\u003e UA -\u003e V is a circuit that enhances the deviation, i.e. Positive feedback circuit.

Fig. 6.13.

We will use the following next instirmation Maruyama: "The contour enhances the deviation if and only if it contains an even number of negative arcs (otherwise it is the contour opposing the deviation)."

The scheme (Fig. 6.14) contains a small number of vertices and connections for the convenience of preliminary analysis. A more thorough analysis of the problem of electricity consumption will require, according to Roberts, a significantly larger number of variables and more subtle methods for their choice. At the same time, the problem arises to combine the opinions of experts.

To solve problems marked in the examples. 6.13 and 6.14, not enough to build a graph of one or another complexity and analyze the chains of its connections (paths) and cycles, a deeper analysis of its structure, stability properties (instability), an analysis of the impact of changes in the peak parameters to other vertices, sensitivity analysis is necessary.

Fig. 6.14.(Roberts. , from. 162)

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

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.

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Problems of study of complex technical and economic, social, political, etc. Defended by a number of features inherent in the specified areas:

· Interrelated processes on them (technical and economic, social, political etchers) and their multi-term; By virtue of this, it is impossible to decay and a detailed study of individual phenomena (for example, only economic or only social) - all occurring inside the economic (political, etc.) of the system of phenomena should be considered and investigated in aggregate;

· The lack of sufficient quantitative information about the dynamics of processes occurring in a simulated system, which forces to use along with quantitative and qualitative information when describing such processes;

· Nonstomarity of the processes themselves, and the nature of changes in certain characteristics of processes is often unknown, which makes it difficult to build their quantitative models.

Such systems are called low-resistant (weakly informalized). They are not possible a traditional mathematical (economic, sociometric, etc.) approach to the analysis of processes to develop complex (i.e., affecting various aspects of the system under study) solutions. To simulate complex badly informable systems (for example, social, technical and economic, regional, etc.), a cognitive approach is used, which is based on cognitive aspects. These aspects include processes of perception, thinking, knowledge, explanation and understanding. A schematic, simplified description of the picture of the world relating to the problem situation is depicted as a cognitive card.

From the standpoint of a cognitive approach, the modeling process can be represented as a schema - Fig.8.2.

Fig. 8.2 Simulation process

Cognitive analysis provides for the consistent causal structuring of information about the processes occurring in the under study. The following stages of the description of the system are distinguished:

a. Any event that occurred in the system is caused by certain reasons (prerequisites), the appearance of which is associated with the movement of material flows (goods, money, resources, etc.) and intangible streams (information interactions). The movement of each stream can be described in the most general form by the corresponding chains of causal relationships that make up the knowledge of the analyst or its assumptions about the laws acting in this system.

b. Each of the selected flows is described by the corresponding set of factors. The union of all these aggregates is many factors, in terms of which processes in the system are described;

c. The relationships between factors are determined by considering causal chains describing the movement of each flow. It is believed that the factors included in the first part "if ..." chains "if that., ..", affect the factors of its second part "that ...", and this effect can be either reinforcing (positive) or Thoring (negative), or a variable sign, depending on the possible additional conditions.

The impact force of factors is described by each other using linguistic variables of the type "significant", "moderate", "weak", etc. You can compare the combination of such linguistic variables some numeric scale so that each variable will correspond to a certain number in this scale. You can choose an interval as such a scale.

· The mutual influence of factors is displayed using a cognitive card, which is the model of the system under study in the form of a suspended orgraf Each vertex of the graph corresponds to one factor or the element of the picture of the world. Arcs that bind the vertices correspond to the causal relationship between the vertices, communication can be positive and negative.

· The cognitive modeling method refers to soft modeling methods (Soft Simulation). The nearest analogues of this method are simulation modeling, system speaker method. The advantage of this method is that the method can operate not only by accurate quantitative values \u200b\u200band formulas, but high-quality values \u200b\u200band estimates. But also the moment is also a disadvantage, because Results are obtained quality.

Cognitive modeling is a "zero level" modeling. Cognitive modeling helps to quickly get primary results, figure out in more detail in the simulated system, identify patterns and then go to more accurate models (if it seems possible and necessary). Therefore, the use of cognitive modeling at the top level of decision-making in the analysis of complex socio-economic, political, technical, techno-economic systems will be the most reasonable.

In Russia, this method will be applied to the MPS (2002 in IPU RAS, a model of railway transport was built), as well as in the administrations of some areas. Abroad, this method will be applied in a number of consulting organizations.

Developed tools of DC "Situation" and "Kanva" (IPA RAS). "Situation" - a closed system, no information on it is practically no. Kanva is a simple system that implements only basic methods.

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 areas 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.