COGNITIVE MODELING

CONTENT
Introduction
1. Subject of cognitive analysis
1.1. External environment
1.2. instability external environment
1.3. Weakly structured external environment
2. General concept cognitive analysis
3. Stages of cognitive analysis
4. Goals, stages and basic concepts of cognitive modeling
4. 1. The purpose of building a cognitive model
4.2. Stages of cognitive modeling
4.3. Directed graph (cognitive map)
4.4. Functional graph (completion of the cognitive model building)
5. Types of factors

6.1. Identification of factors (elements of the system)
6.2. Two approaches to identifying relationships between factors
6.3.Examples of highlighting factors and relationships between them
6.4. The problem of determining the strength of the influence of factors
7. Checking the adequacy of the model
8. Using a cognitive model
8.1. Application of cognitive models in decision support systems
8.2. An example of working with a cognitive model
9. Computer systems for supporting management decisions
9.1. general characteristics decision support systems
9.2. "Situation - 2"
9.3. "Compass-2"
9.4. "Canvas"
Conclusion
Bibliography
Application

Introduction
At present, obtaining reliable information and its rapid analysis have become the most important prerequisites for successful management. This is especially true if the control object and its external environment are a complex of complex processes and factors that significantly affect each other.
One of the most productive solutions to problems that arise in the field of management and organization is the use of cognitive analysis, which is the subject of study in the course work.
The methodology of cognitive modeling, designed for analysis and decision making in ill-defined situations, was proposed by the American researcher R. Axelrod 1 .
Initially, cognitive analysis was formed within the framework of social psychology, namely, cognitivism, which studies the processes of perception and cognition.
The application of the developments of social psychology in management theory led to the formation of a special branch of knowledge - cognitive science, concentrating on the study of management and decision-making problems.
Now the methodology of cognitive modeling is developing in the direction of improving the apparatus for analyzing and modeling situations.
Theoretical achievements of cognitive analysis have become the basis for the creation of computer systems focused on solving applied problems in the field of management.
Work on the development of the cognitive approach and its application to the analysis and control of so-called semi-structured systems is currently being carried out at the Institute of Control Problems of the Russian Academy of Sciences 2 .
By order of the Administration of the President of the Russian Federation, the Government of the Russian Federation, the Government of the city of Moscow, a number of socio-economic studies using cognitive technology were carried out at the IPU RAS. The developed recommendations are successfully applied by the relevant ministries and departments 3 .
Since 2001, under the auspices of the IPU RAS, international conferences “Cognitive Analysis and Situation Development Management (CASC)” have been regularly held.
When writing a term paper, the works of domestic researchers were involved - A.A. Kulinich, D.I. Makarenko, S.V. Kachaeva, V.I. Maksimova, E.K. Kornoushenko, E. Grebenyuk, G.S. Osipova, A. Raikov. Most of these researchers are specialists from the Institute of Computer Science, Russian Academy of Sciences.
Thus, cognitive analysis is being actively developed not only by foreign, but also by domestic specialists. Nevertheless, within the framework of cognitive science there are a number of problems, the solution of which could significantly improve the results of applying applied developments based on cognitive analysis.
aim term paper is the analysis of the theoretical base of cognitive technologies, the problems of the methodology of cognitive analysis, as well as computer systems based on cognitive modeling of decision support.
The set goals correspond to the structure of the work, which sequentially reveals the basic concepts and stages of cognitive analysis in general, cognitive modeling (as a key moment of cognitive analysis), general principles for applying the cognitive approach in practice in the field of management, as well as computer technologies that apply methods of cognitive analysis.

1. The subject of cognitive analysis
1.1. External environment
For effective management, forecasting and planning, it is necessary to analyze the external environment in which the objects of management operate.
The external environment is usually defined by researchers as a set of economic, social and political factors and subjects that have a direct or indirect impact on the possibility and ability of the subject (be it a bank, an enterprise, any other organization, an entire region, etc.) to achieve the set development goals.
For orientation in the external environment and for its analysis, it is necessary to clearly represent its properties. Specialists of the Institute of Control Problems of the Russian Academy of Sciences identify the following main characteristics of the external environment:
1. Complexity - this refers to the number and variety of factors to which the subject must respond.
2. The relationship of factors, that is, the force with which a change in one factor affects the change in other factors.
3. Mobility - the speed with which changes occur in the external environment 4 .
The selection of such characteristics to describe the environment indicates that researchers apply a systematic approach and consider the external environment as a system or a set of systems. It is within the framework of this approach that it is customary to represent any objects in the form of a structured system, to single out the elements of the system, the relationships between them and the dynamics of the development of elements, relationships and the entire system as a whole. Therefore, cognitive analysis used to study the external environment and develop ways and methods of functioning in it is sometimes considered as a component of system analysis 5 .
The specificity of the external environment of control objects lies in the fact that this environment is subject to the influence of the human factor. In other words, it includes subjects endowed with an autonomous will, interests and subjective ideas. This means that this environment does not always obey linear laws that unambiguously describe the relationship of causes and effects.
From this follow two basic parameters of the external environment in which the human factor operates - instability and weakly structured. Let's take a closer look at these parameters.

1.2. Instability of the external environment

The instability of the external environment is often identified by researchers with unpredictability. “The degree of instability of the external economic and political environment for ... [the object of control] is characterized by the familiarity of expected events, the expected pace of change, and the ability to predict the future” 6 . This unpredictability is generated by multifactorial nature, variability of factors, pace and direction of development of the environment.
“The cumulative effect of all environmental factors, summarize V. Maksimov, S. Kachaev and E. Kornoushenko, “shapes the level of its instability and determines the expediency and direction of surgical intervention in ongoing processes” 7 .
The higher the instability of the external environment, the more difficult it is to develop adequate strategic decisions. Therefore, there is an objective need to assess the degree of instability of the environment, as well as to develop approaches to its analysis.
According to I. Ansoff, the choice of strategy for managing and analyzing situations depends on the level of instability of the external environment. For moderate instability, conventional control is applied based on extrapolation of knowledge about the environment's past. With an average level of instability, management is carried out on the basis of a forecast of changes in the environment (for example, "technical" analysis financial markets). At a high level of instability, management is used based on flexible expert decisions (for example, "fundamental" 8 analysis of financial markets) 9 .

1.3. Weakly structured external environment

The environment in which the subjects of management are forced to work is characterized not only as unstable, but also as weakly structured. These two characteristics are closely related, but distinct. However, these terms are sometimes used interchangeably.
Thus, specialists from the IPU RAS, defining semi-structured systems, point to some of their properties inherent in unstable systems: “Difficulties in analyzing processes and making managerial decisions in such areas as economics, sociology, ecology, etc. due to a number of features inherent in these areas, namely: the multidimensionality of the processes occurring in them (economic, social, etc.) and their interconnectedness; because of this, it is impossible to isolate and study individual phenomena in detail - all the phenomena occurring in them must be considered as a whole; the lack of sufficient quantitative information about the dynamics of processes, which forces us to switch to qualitative analysis such processes; variability of the nature of processes over time, etc. Due to these features, economic, social, etc. systems are called semi-structured systems” 10 .
However, it should be noted that the term "instability" implies the impossibility or difficulty of predicting the development of the system, and weakly structured - the impossibility of formalizing it. Ultimately, the characteristics "instability" and "weakly structured", in my opinion, reflect different aspects of the same phenomenon, since we traditionally perceive a system that we cannot formalize and thus absolutely accurately predict its development (that is, a weakly structured system) as unstable, prone to chaos. Therefore, hereinafter, following the authors of the articles studied, I will use these terms as equivalent. Sometimes researchers, along with the above concepts, use the term "difficult situations".
So, unlike technical systems, economic, socio-political and other similar systems are characterized by the absence of a detailed quantitative description of the processes occurring in them - the information here is of a qualitative nature. Therefore, for semi-structured systems, it is impossible to create formal traditional quantitative models. Systems of this type are characterized by uncertainty, description at a qualitative level, and ambiguity in assessing the consequences of certain decisions 11 .
Thus, the analysis of an unstable external environment (weakly structured systems) is fraught with many difficulties. When solving them, an expert's intuition, his experience, associativity of thinking, guesses are needed.
Computer means of cognitive (cognitive) modeling of situations make it possible to cope with such an analysis. These funds have been used in economically developed countries for decades, helping enterprises to survive and develop their business, and the authorities to prepare effective regulatory documents 12 . Cognitive modeling is designed to help the expert reflect on a deeper level and streamline his knowledge, as well as formalize his ideas about the situation to the extent possible.

2. General concept of cognitive analysis

Cognitive analysis is sometimes referred to by researchers as "cognitive structuring" 13 .
Cognitive analysis is considered as one of the most powerful tools for studying an unstable and semi-structured environment. It contributes to a better understanding of the problems existing in the environment, the identification of contradictions and a qualitative analysis of ongoing processes. The essence of cognitive (cognitive) modeling - the key point of cognitive analysis - is to the toughest problems and trends in the development of the system should be reflected in a simplified form in the model, explore possible scenarios for the emergence of crisis situations, find ways and conditions for their resolution in a model situation. The use of cognitive models qualitatively increases the validity of managerial decision-making in a complex and rapidly changing environment, saves the expert from "intuitive wandering", saves time for comprehending and interpreting events occurring in the system 14 .
IN AND. Maksimov and S.V. Kachaev, to explain the principles of using information cognitive (cognitive) technologies to improve management, use the metaphor of a ship in a raging ocean - the so-called "frigate-ocean" model. Most commercial and non-commercial activities in a volatile and semi-structured environment “are inevitably associated with risk, both from the uncertainty of future operating conditions and the potential for mismanagement decisions…. It is very important for management to be able to anticipate such difficulties and develop strategies in advance to overcome them, i.e. to have predetermined attitudes of possible behavior. These developments are proposed to be carried out on models in which the information model of the control object (“frigate”) interacts with the model of the external environment - economic, social, political, etc. ("ocean"). “The purpose of such a simulation is to give recommendations to the “frigate” on how to cross the “ocean” with the least “effort” ... Of interest ... are the ways to achieve the goal, taking into account the favorable “winds” and “currents” ... So, we set the goal: to determine the “wind rose” ... [ external environment], and then we will see which “winds” will be favorable, which will be opposite, how to use them and how to discover the properties of the external situation that are important for ... [the object]” 15 .
Thus, the essence of the cognitive approach lies, as already mentioned, in helping the expert to reflect on the situation and develop the most effective management strategy, based not so much on his intuition as on ordered and verified (as far as possible) knowledge about a complex system. Examples of the application of cognitive analysis to solve specific problems will be discussed below in paragraph “8. Using a cognitive model”.

3. Stages of cognitive analysis

Cognitive analysis consists of several stages, each of which implements a specific task. Consistent solution of these problems leads to the achievement of the main goal of cognitive analysis. Researchers give a different nomenclature of stages depending on the specifics of the studied object (objects) 16 . If we summarize and generalize all these approaches, we can distinguish the following stages, which are characteristic of the cognitive analysis of any situation.

(As a result of passing stages 3 - 5, a cognitive model of the situation (system) is built, which is displayed in the form of a functional graph. Therefore, we can say that stages 3 - 5 are cognitive modeling. In more detail, all these stages and basic concepts cognitive modeling will be discussed in paragraphs 4 - 7).

options

Let us consider in detail each of the above stages (with the exception of the first and second, which are essentially preparatory), the mechanisms for implementing the particular tasks of each of the stages, as well as the problems that arise at different stages of cognitive analysis.

4. Goals, stages and basic concepts of cognitive modeling

A key element of cognitive analysis is the construction of a cognitive model.

4. 1. The purpose of building a cognitive model

Cognitive modeling contributes to a better understanding of the problem situation, the identification of contradictions and a qualitative analysis of the system. The purpose of modeling is to form and refine a hypothesis about the functioning of the object under study, considered as a complex system, which consists of separate, but still interconnected elements and subsystems. In order to understand and analyze the behavior of a complex system, a block diagram of the cause-and-effect relationships of the elements of the system is built. An analysis of these relationships is necessary for the implementation of various process controls in the system 18 .

4.2. Stages of cognitive modeling

AT in general terms the stages of cognitive modeling are discussed above. The works of IPU RAS specialists contain a concretized presentation of these stages. Let's highlight the main ones.

Thus, the cognitive model includes a cognitive map (directed graph) and graph arc weights (assessment of mutual influence or influence of factors). When determining the weights of the arcs, the directed graph turns into a functional one.
The problems of identifying factors, assessing the mutual influence of factors and the typology of factors will be discussed in paragraphs 5 and 6; here we will consider such basic concepts of cognitive modeling as a cognitive map and a functional graph.

4.3. Directed graph (cognitive map)

Within the framework of the cognitive approach, the terms "cognitive map" and "directed graph" are often used interchangeably; although, strictly speaking, the concept of a directed graph is broader, and the term "cognitive map" indicates only one of the applications of a directed graph.
A cognitive map consists of factors (elements of the system) and links between them.
In order to understand and analyze the behavior of a complex system, a block diagram of the cause-and-effect relationships of the elements of the system (situation factors) is built. Two elements of the system A and B are depicted on the diagram as separate points (vertices) connected by an oriented arc, if element A is connected to element B by a cause-and-effect relationship: A a B, where: A is the cause, B is the effect.
Factors can influence each other, and such an influence, as already mentioned, can be positive, when an increase (decrease) in one factor leads to an increase (decrease) in another factor, and negative, when an increase (decrease) in one factor leads to a decrease (increase) ) another factor 20 . Moreover, the influence can also have a variable sign, depending on possible additional conditions.
Such schemes for representing cause-and-effect relationships are widely used to analyze complex systems in economics and sociology.
An example of a cognitive map of some economic situation is shown in Figure 1.

Figure 1. Directed graph 21 .

4.4. Functional graph (completion of the cognitive model building)
The cognitive map reflects only the fact of the presence of influences of factors on each other. It does not reflect either the detailed nature of these influences, nor the dynamics of changes in influences depending on changes in the situation, nor temporary changes in the factors themselves. Taking into account all these circumstances requires a transition to the next level of information structuring, that is, to a cognitive model.
At this level, each relationship between the factors of the cognitive map is revealed by the corresponding dependencies, each of which can contain both quantitative (measured) variables and qualitative (not measured) variables. In this case, quantitative variables are presented in a natural way in the form of their numerical values. Each qualitative variable is associated with a set of linguistic variables that reflect the various states of this qualitative variable (for example, consumer demand can be “weak”, “moderate”, “rush”, etc.), and each linguistic variable corresponds to a certain numerical equivalent in the scale. With the accumulation of knowledge about the processes occurring in the situation under study, it becomes possible to reveal in more detail the nature of the relationships between factors.
Formally, a cognitive model of a situation can, like a cognitive map, be represented by a graph, but each arc in this graph already represents a certain functional relationship between the corresponding factors; those. the cognitive model of the situation is represented by a functional graph 22 .
An example of a functional graph reflecting the situation in a conditional region is shown in fig. 2.

Figure 2. Functional graph 23 .
Note that this model is a demonstration model, so many environmental factors are not taken into account in it.

5. Types of factors
To structure the situation (system), researchers subdivide factors (elements) into different groups, each of which has a certain specificity, namely, a functional role in modeling. Moreover, depending on the specifics of the analyzed situation (system), the typology of factors (elements) can be different. Here I will highlight some types of factors used in cognitive modeling of most systems (situations, environments).
First, among all the factors discovered, there are basic (affecting the situation in a significant way, describing the essence of the problem) and "excessive" (insignificant) factors, "weakly connected" with the "core" of basic factors 24 .
When analyzing a particular situation, the expert usually knows or assumes what changes in the basic factors are desirable for him. The factors of greatest interest to the expert are called target factors. IN AND. Maksimov, E.K. Kornoushenko, S.V. Kachaev describes the target factors as follows: “These are the “output” factors of the cognitive model. The task of developing decisions on managing processes in a situation is to ensure the desired changes in target factors, this is the goal of management. The goal is considered correctly set if the desired changes in some target factors do not lead to undesirable changes in other target factors” 25 .
In the initial set of basic factors, a set of so-called control factors is distinguished - “”input” factors of the cognitive model, through which control actions are fed into the model. The control action is considered consistent with the goal if it does not cause undesirable changes in any of the target factors” 26 . To identify control factors, factors influencing the target ones are determined. The controlling factors in the model will be potentially possible levers of influence on the situation 27 .
The influence of control factors is summed up in the concept of "control vector" - a set of factors, each of which is supplied with a control impulse of a given value 28 .
Situation factors (or elements of the system) can also be divided into internal (belonging to the management object itself and under more or less complete control of management) and external (reflecting the impact on the situation or system). external forces which may not be controlled or only indirectly controlled by the subject of management).
External factors are usually divided into predictable ones, the occurrence and behavior of which can be predicted on the basis of an analysis of the available information, and unpredictable ones, the behavior of which the expert learns about only after their occurrence 29 .
Sometimes researchers identify so-called indicator factors that reflect and explain the development of processes in a problem situation (system, environment) 30 . For such purposes, the concept of integral indicators (factors) is also used, by changing which one can judge the general trends in this area 31 .
Factors are also characterized by a trend in their values. Distinguish the following trends: growth, decline. If there is no change in the factor, one speaks of the absence of a trend or a zero trend 32 .
Finally, it should be noted that it is possible to identify causal factors and factors-consequences, short-term and long-term factors.

6. Main problems of building a cognitive model
There are two main problems in constructing a cognitive model.
First, it is difficult to identify factors (elements of the system) and to rank factors (selection of basic and secondary ones) (at the stage of constructing a directed graph).
Secondly, identifying the degree of mutual influence of factors (determining the weights of graph arcs) (at the stage of constructing a functional graph).

6.1. Identification of factors (elements of the system)

It can be stated that the researchers have not developed a clear algorithm for identifying the elements of the systems under study. It is assumed that the studied factors of the situation are already known to the expert conducting the cognitive analysis.
Usually, when considering large (for example, macroeconomic) systems, the so-called PEST-analysis is used (Policy - policy, Economy - economy, Society - society, Technology - technology), which involves the allocation of 4 main groups of factors through which political, economic, socio - cultural and technological aspects of the environment 33 . This approach is well known in all socio-economic sciences.
PEST analysis is a tool for the historically established four-element strategic analysis of the external environment. At the same time, for each specific complex object, there is a special set of key factors that directly and most significantly affect the object. The analysis of each of the identified aspects is carried out systematically, since in life all these aspects are closely interconnected 34 .
In addition, it is assumed that the expert can judge the range of factors, in accordance with their subjective ideas. Thus, the "Fundamental" analysis of financial situations, close in some parameters to cognitive analysis, is based on a set of basic factors (financial and economic indicators) - both macroeconomic and lower order, both long-term and short-term. These factors, in accordance with the "fundamental" approach, are determined on the basis of common sense 35 .
Thus, the only conclusion that can be drawn regarding the process of identifying factors is that the analyst, in pursuing this goal, should be guided by ready-made knowledge of various socio-economic sciences dealing with the specific study of various systems, as well as his experience and intuition.

6.2. Two approaches to identifying relationships between factors

To display the nature of the interaction of factors, positive and normative approaches are used.
The positive approach is based on taking into account the objective nature of the interaction of factors and allows you to draw arcs, assign signs (+ / -) and exact weights to them, that is, reflect the nature of this interaction. This approach is applicable if the relationship of factors can be formalized and expressed by mathematical formulas that establish precise quantitative relationships.
However, not all real systems and their subsystems are described by certain mathematical formulas. We can say that only some special cases of interaction of factors are formalized. Moreover, the more complex the system, the less likely it is to be fully described by traditional mathematical models. This is primarily due to the fundamental properties of unstable, semi-structured systems, described in paragraph 1. Therefore, a positive approach is complemented by a normative one.
The normative approach is based on a subjective, evaluative perception of the interaction of factors, and its use also allows you to assign weights to the arcs, i.e., reflect the strength (intensity) of the interaction of factors. The clarification of the influence of factors on each other and the assessment of these influences are based on the "estimations" of the expert and are expressed in quantitative form using the scale [-1,1] or linguistic variables such as "strong", "weak", "moderately" 36 . In other words, with the normative approach, the expert is faced with the task of intuitively determining the strength of the mutual influence of factors, based on their knowledge of the qualitative relationship.
In addition, as already mentioned, the expert needs to determine the negative or positive nature of the influence of factors, and not just the strength of influence. In carrying out this task, obviously, it is possible to use the two approaches indicated above.

6.3.Examples of highlighting factors and relationships between them
Here are some examples used by researchers to illustrate the selection of factors and the establishment of relationships between them.
Thus, V. Maksimov, S. Kachaev and E. Kornoushenko identify the following basic factors to build a cognitive model of processes occurring in a crisis economy: 1. Gross domestic product (GDP); 2. Aggregate demand; 3. inflation; 4. Savings; 5. Consumption; 6. Investments; 7. Public procurement; 8. Unemployment; 9. Offer of money; 10. State transfer payments; 11. Government spending; 12. Government revenues; 13. State budget deficit; 14. Taxes; 15. Non-payment of taxes; 16. interest rate; 17. Demand for money 37 .
V. Maksimov, E. Grebenyuk, E. Kornoushenko in the article “Fundamental and technical analysis: integration of two approaches” give another example of identifying factors and reveal the nature of the links between them: “The most important economic indicators that affect the stock market in the US and Europe, are: gross national product (GNP), manufacturing output index (PPI), consumer price index (CPI), producer price index (CPI), unemployment rate, oil price, dollar exchange rate ... If the market is growing and economic indicators confirm the stable development of the economy , then further price growth can be expected ... Stocks rise in price if the company's profits grow and there is a prospect of their further growth ... If the real growth rates of economic indicators diverge from the expected ones, then this leads to a panic in the stock market and to its sharp changes. The change in the gross national product is normally 3-5% per year. If the annual GNP growth exceeds 5%, then this is called an economic boom, which can eventually lead to a market collapse. The change in GNP can be predicted by changes in the index of the manufacturing industry. A sharp increase in the IPI indicates a possible increase in inflation, which leads to a fall in the market. The growth of the CPI and CPI and oil prices also leads to a fall in the market. High unemployment in the US and Europe (over 6%) is forcing the federal agencies to lower the bank interest rate, which leads to a revival of the economy and a rise in stock prices. If unemployment decreases gradually, then the market does not react to these changes. If the level of unemployment drops sharply and becomes less than the expected value, then the market begins to fall, because a sharp decrease in unemployment can increase the rate of inflation beyond the expected one” 38 .

6.4. The problem of determining the strength of the influence of factors

So, the most important problem of cognitive modeling is to identify the weights of graph arcs, that is, to quantify the mutual influence or influence of factors. The fact is that the cognitive approach is used in the study of an unstable, semi-structured environment. Recall that its characteristics: variability, difficult to formalize, multifactorial, etc. This is the specificity of all systems in which people are included. Therefore, the inoperability of traditional mathematical models in many cases is not a methodological defect of cognitive analysis, but a fundamental property of the subject of study 39 .

Thus, the most important feature of most situations studied in control theory is the presence of thinking participants in them, each of which represents the situation in its own way and makes certain decisions based on “their own” representation. As J. Soros noted in his book The Alchemy of Finance, “when thinking participants act in a situation, the sequence of events does not lead directly from one set of factors to another; instead, it crisscrosses ... connects factors with their perceptions, and perceptions with factors. This leads to the fact that "the processes in the situation do not lead to equilibrium, but to a never-ending process of change" 40 . Hence it follows that a reliable prediction of the behavior of processes in a situation is impossible without taking into account the assessment of this situation by its participants and their own assumptions about possible actions. This feature of some systems J. Soros called reflexivity.
Formalized quantitative dependencies of factors are described different formulas(regularities) depending on the subject of research, that is, on the factors themselves. However, as already mentioned, the construction of a traditional mathematical model is not always possible.

The problem of universal formalization of mutual influence of factors has not yet been solved and is unlikely to ever be solved.

Therefore, it is necessary to come to terms with the fact that it is far from always possible to describe the relationships of factors by mathematical formulas, i.e. it is by no means always possible to accurately quantify the dependences 41 .
Therefore, in cognitive modeling, when estimating the weights of arcs, as mentioned, the subjective opinion of an expert is often taken into account 42 . The main task in this case is to compensate for the subjectivity and distortion of estimates through various verification procedures.

In this case, one check of the expert's assessments for consistency is usually not enough. The main goal of the expert's subjective opinion processing procedure is to help him reflect, more clearly understand and systematize his knowledge, evaluate their consistency and adequacy of reality.

In the process of extracting expert knowledge, the expert - the source of knowledge - interacts with a cognitologist (knowledge engineer) or with a computer program, which makes it possible to follow the course of reasoning of specialists when making decisions and reveal the structure of their ideas about the subject of research 43 .
In more detail, the procedures for checking and formalizing the expert's knowledge are disclosed in the article by A.A. Kulinich “The system of cognitive modeling “Canva”” 44 .

7. Checking the adequacy of the model
Researchers have proposed several formal procedures for checking the adequacy of the constructed model 45 . However, since the model is based not only on formalized relationships of factors, mathematical methods for checking its correctness do not always give an accurate picture. Therefore, the researchers proposed a kind of "historical method" for testing the adequacy of the model. In other words, the developed model of any situation is applied to similar situations that existed in the past and whose dynamics are well known 46 . In the event that the model turns out to be workable (that is, it produces forecasts that coincide with the real course of events), it is recognized as correct. Of course, not one of the methods for verifying the model separately is exhaustive, so it is advisable to use a set of validation procedures.

8. Using a cognitive model

8.1. Application of cognitive models in decision support systems
The main purpose of the cognitive model is to help the expert in the process of learning and, accordingly, making the right decision. Therefore, the cognitive approach is used in decision support systems.
The cognitive model visualizes and organizes information about the environment, intent, goals and actions. At the same time, visualization performs an important cognitive function, illustrating not only the results of the actions of the subject of control, but also prompting him to analyze and generate solutions 47 .
However, the cognitive model serves not only to systematize and "clarify" the expert's knowledge, but also to identify the most beneficial "points of application" of the control actions of the subject of management 48 . In other words, the cognitive model explains which factor or the relationship of factors must be acted upon, with what force and in what direction, in order to obtain the desired change in target factors, that is, in order to achieve the goal of control at the lowest cost.
Control actions can be short-term (impulse) or long-term (continuous), acting until the goal is achieved. It is also possible to use pulsed and continuous control actions 49 .
Upon reaching given goal the task immediately arises of keeping the situation in the achieved favorable state until a new goal appears. In principle, the task of keeping the situation in the desired state does not differ from the task of achieving the goal 50 .
A complex of interrelated control actions and their logical time sequence constitute an integral control strategy (control model).
The use of different management models can lead to different results. Here it is important to be able to predict what consequences this or that management strategy will ultimately lead to.
To develop such forecasts, a scenario approach (scenario modeling) is used within the framework of cognitive analysis. Scenario modeling is sometimes referred to as "dynamic simulation".
The scenario approach is a kind of “acting out” different scenarios depending on the chosen management model and the behavior of unpredictable factors. For each scenario, a triad "initial prerequisites - our impact on the situation - the result obtained" is built 51 . The cognitive model in this case makes it possible to take into account the whole complex of effects of control actions for different factors, the dynamics of factors and their relationships under different conditions.
Thus, all possible options for the development of the system are identified and proposals are developed regarding the optimal control strategy for the implementation of the desired scenario out of possible 52 .
Researchers quite often include scenario modeling as part of their cognitive analysis or consider scenario modeling as an adjunct to cognitive analysis.
If we summarize and generalize the opinions of researchers regarding the stages of scenario modeling, then in the most general form the stages of scenario analysis can be represented as follows.
1. Development of the goal of management (the desired change in target factors).
2. Development of scenarios for the development of the situation when applying different management strategies.
3. Determination of the achievability of the set goal (feasibility of scenarios leading to it); checking the optimality of the already planned control strategy (if any); selection of the optimal strategy corresponding to the best, in terms of the goal, scenario.
4. Concretization of the optimal management model - development of specific practical recommendations for managers. This concretization includes the identification of control factors (through which one can influence the development of events), determining the strength and direction of control actions on control factors, predicting probable crisis situations due to the influence of unpredictable external factors, etc.
It should be noted that the stages of scenario modeling may vary depending on the object of study and management.
At the initial stage of modeling, there may be enough high-quality information that does not have an exact numerical value and reflects the essence of the situation. In the transition to modeling specific scenarios, the use of quantitative information, which is numerical estimates of the values of any indicators, becomes more and more significant. In what follows, quantitative information is mainly used to carry out the necessary calculations 53 .
The very first scenario that does not require any actions of the researcher to form it is the self-development of the situation (in this case, the vector of control actions is “empty”). The self-development of the situation is the starting point for the further formation of scenarios. If the researcher is satisfied with the results obtained during self-development (in other words, if the set goals are achieved in the course of self-development), then further scenario research is reduced to studying the impact of certain changes in the external environment on the situation 54 .
There are two main classes of scenarios: scenarios simulating external influences and scenarios simulating purposeful (controlled) development of the situation 55 .

8.2. An example of working with a cognitive model

Consider an example of working with a cognitive model given in the article by S.V. Kachaeva and D.I. Makarenko "Integrated information and analytical complex for situational analysis of the socio-economic development of the region."
“The use of an integrated information-analytical complex of situational analysis can be considered on the example of developing a strategy and program for the socio-economic development of the region.
At the first stage, a cognitive model of the socio-economic situation in the region is built... Next, scenarios of the potential and real possibility of changing the situation in the region and achieving the goals set are modeled.
The following were chosen as the goals of socio-economic policy:

To achieve the goals set, the following “levers” (control factors - Yu.M.) were selected, with the help of which the decision maker can or wants to influence the situation:

As a result of modeling, the potential and real possibility of achieving the set goals with the help of the selected levers and the resulting control actions is clarified (see Fig. 3).

Figure 3. Cognitive and dynamic simulation (scenario) modeling.

At the next stage, they move from developing a strategy for achieving goals to developing a program of specific actions. The tool for implementing the strategy is the regional budgetary and tax policy.
The levers chosen at the previous stage and certain impacts correspond to the following directions of budgetary and tax policy.

Levers of Achievement strategic goals	Directions of the budget and tax policy
Population income	Social policy spending
Investment climate	Public Administration Expenditure Law Enforcement Expenses Expenses for industry, power industry, construction and agriculture
production costs	Regulation of tariffs for electricity, fuel, heat, rent, etc.
Development of production infrastructure	Market Infrastructure Development
Tax collection	Regulation of the level of non-payment of taxes
tax incentives	Regulation of the level of tax incentives
Political and economic preferences for the region.	Free transfers from other levels of government

Thus, the integrated information and analytical complex of situational analysis is a powerful tool for developing a strategy for the development of the region and implementing this strategy into reality” 56 .
It should be noted that in studies examples of the use of cognitive and scenario modeling are usually given in a very general form, since, firstly, this kind of information is exclusive and has a certain commercial value, and, secondly, each specific situation (system, environment, control object) requires an individual approach.
The existing theoretical base of cognitive analysis, although it requires clarification and development, allows different management subjects to develop their own cognitive models, since, as mentioned, it is assumed that specific models are compiled for each area, for each problem.

9. Computer systems for supporting management decisions

Conducting a cognitive analysis of unstable, semi-structured situations and environments is an extremely difficult task, for which information systems are involved. In essence, these systems are designed to improve the efficiency of the decision-making mechanism, since the main applied task of cognitive analysis is the optimization of control.

9.1. General characteristics of decision support systems
Decision support systems, as a rule, are interactive. They are designed to process data and implement models that help solve individual, mostly weakly or unstructured tasks (for example, making investment decisions, making forecasts, etc.). These systems can provide workers with the information they need to make individual and group decisions. Such systems provide direct access to information reflecting current situations and all the factors and relationships necessary for decision-making 57
etc.................

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

The article substantiates the expediency of applying the cognitive approach to research and forecasting a resource-dependent economy. The results of modeling the medium-term forecast of the Russian economy using a fuzzy cognitive map are presented.1

Resource dependence, uncertainty and forecasting. Specific features of the economy modern Russia are resource dependence, transitional type of development and the crisis state of the economy. Resource dependence gives rise to various kinds of unfavorable trends, the extension of which is highly undesirable, since it significantly limits the possibilities of predictive extrapolation. The transitional state of the economy is associated with the “mental imperfection” inherited from past years, the lack of stable trends and mature economic structures, which makes the “achieved level” not a very reliable basis for forecasting. The same can be said about the crisis in the economy, especially if we take into account its largely "man-made" nature, associated with the economic policy of the state and aggressive external influences. In general, the deterioration of the economic situation of the country, which has been taking place since 2013, "deeply natural and caused by internal causes of a fundamental nature".

One of the factors slowing down economic growth is dependence on world oil prices, the decline of which minimizes the positive effect of an increase in hydrocarbon production. The problem of uncertainty is highly inherent in a resource-dependent economy, since along with the development factors traditional for all economies, factors associated with the development of natural resources. Fundamental uncertainty in the Russian economy 2 due to the well-established resource and raw material nature of development over the past decades. Moreover, as the scale and degree of maturity of the resource and raw materials sector increases, so does the uncertainty inherent not only in the sector, but also in the economy as a whole. Thus, it can be said that a “bundle” of complex and far from obvious economic and political ties affects the resource-dependent economy, and from this point of view, the Russian economy is no exception.

Applied predictive model of the Russian economy. The methodology of cognitive modeling, designed for analysis and decision making in poorly defined situations, was proposed by the American researcher R. Axelrod. It is based on modeling the subjective ideas of experts about the situation, its main tool is a cognitive map of the situation (Fuzzy Cognitive Map), compiled in the form of a directed functional graph. The vertices (concepts) of the graph correspond to the factors (events) under consideration, and the directed arcs, characterized by signs and intensity parameters, reflect the mutual influences between the factors (events). A cognitive map serves to identify the structure of causal relationships between the elements of the system and assess the consequences of influencing them or changing the nature of the relationships.

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

2 Fundamental uncertainty excludes the possibility of a correct transformation in a risk situation. The use of the term "risk" is associated with cases where the degree of uncertainty or the probability of the occurrence of some event can be measured. The practical difference between the categories of risk and uncertainty is that in the first case, the distribution of the outcomes of events is known (which is achieved by a priori calculations or by studying the statistics of previous experience), while in the second case it is not.

The implementation of modeling procedures is usually divided into three stages. The first stage is modeling (imitation) of the situation (system) self-development in the absence of control actions “from the outside” of the researcher. The second stage assumes a controlled development of the situation: the researcher, as a result of influencing any of the factors, determines the control factors and varies them, observing the changes taking place in the system. The third stage is the solution of the inverse problem, which consists in determining the values of the control pulses required to solve the problem. Thus, in the process of numerical implementation of the cognitive model, various scenarios for predicting the development of the situation (system) can be built: without control and with control to reduce negative or strengthen positive trends.

The use of the method of cognitive modeling justifies itself in both theoretical and applied research. The use of cognitive models in the study of patterns and mechanisms of resource dependence to analyze the interactions of endogenous and exogenous factors and their impact on economic growth is considered in one of our papers. As examples of applied research, one can name works on cognitive modeling of socio-economic ratings in the Komi Republic and the development of the tourist and recreational system of the South of Russia. Our task is set more broadly: to assess the influence of key factors on the dynamics of social economic development Russia, which involves the construction of an aggregated structure, covering the entire socio-economic system of the country. In its formulation, this task is close to well-known foreign studies, one of which presents a theoretical cognitive model of the economy, and the other - a model built to assess the socio-economic consequences of oil and gas exploration in Cyprus. From domestic research we especially note the work , where a cognitive model is presented, with the help of which the main factors influencing the process of creating an innovative economy in Russia are identified, and the priority influence is shown industrial policy to economic growth.

Our conceptual approach and technique of working with applied cognitive models are described in the work, where the results of modeling the medium-term forecast of the socio-economic development of the Tomsk region are presented and meaningfully interpreted. This region is interesting because it is both resourceful and innovative; the oil and gas sector, the manufacturing industry and the scientific and educational complex play an important role in its economy. The Tomsk region can be described as a kind of " scale model» Russia - with a similar economic structure, similar achievements and problems in socio-economic development. Of particular note is the comparability of oil and gas production (as one of the main sources of income) per capita: in the Tomsk region - about 15 toe. e./person, in Russia - about 8 tons of oil equivalent. e./person . 3

The results of research on the socio-economic development of the Tomsk region made it possible to come to conclusions that can be largely correlated to the whole country. Therefore, starting to work on a predictive model of the Russian economy, we focused on the results of previous studies and on the practical experience in building cognitive models obtained in these studies.

3 For comparison: the average per capita production of hydrocarbons in the Yamalo-Nenets Autonomous Okrug is about 1 thousand tons, in the Nenets Autonomous Okrug - more than 440, in the Khanty-Mansiysk Autonomous Okrug - 190, in the Sakhalin Region - 70 tons (calculated according to Rosstat).

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

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

Class factors	Factor characteristic	Designation
Basic resource	Oil and gas resources (in terms of production, million toe) Human capital (cumulative costs of formation, billion rubles)	0-1 Oil 0-2 Human capital
Mediating financial flows	Investments in fixed assets (billion rubles) Budget revenues and expenditures (billion rubles) Foreign direct investment inflow (FDI, million dollars) Production costs (billion rubles) Innovation spending (R&D spending, billion rubles)	1-1 Investments 1-2 Budget 1-4 Costs 1-5 Innovation
Main economic complexes	Oil and gas sector (gross value added, billion rubles) Industry (manufacturing, gross value added, billion rubles) Scientific and educational complex (NOC, gross value added, billion rubles)	2-1 NHS 2-2 Industry
Providing factors	Infrastructure (output of infrastructure sectors and supporting activities, billion rubles) Level of technology (qualitative variable*) Level of development of the social sphere (qualitative variable)	3-1 Infrastructure 3-2 Technologies 3-3 Social sphere
Externalities	External situation (oil prices, USD/bbl) External risks - financial, political, regulatory, etc. (qualitative variable)	4-1 Prices
target factor	Level of economic development (GDP per capita, thousand rubles)	5-1 GDP
* Qualitative (not measurable) variables reflect different states, each of which corresponds to a certain numerical equivalent. The presence of quantitative and qualitative variables in the composition of one model is possible, since the search for a solution is aimed at obtaining not absolute values, but dynamic (incremental) characteristics in terms of worsening or improving the situation.

Preliminary values of the intensity of mutual influence between the measurable factors of the cognitive model were established by correlation analysis. We considered pairwise correlations between time series of data (for the period 2000-2013) according to the factors given in Table. 1. Next, the coefficients were refined by an expert in accordance with the logic of the transition of the system from one stationary state to another as a result of external impulsive influences.

It should be noted that this is one of the most complex and non-obvious nuances of cognitive modeling for perception, because any cognitive model is subjective view of an expert about processes in a complex dynamic situation (system), formally represented as a directed signed graph. The question arises: can such subjectivity be justified? Won't it lead to obtaining distorted concepts about the patterns of development of the system under study?

The problem of subjectivity can be largely solved with the help of reverse verification, i.e., by checking models under known conditions, their "immersion" in the past. We tested the model for the retrospective period 2000-2013. based on the available statistical data on the measurable factors of the model. At the same time, the increments of the following factors were set in the vector of initial trends: 0-1 oil (+31%); 1-3 FDI (+28%); 4-1 prices (+182%) - based on available statistics - and 4-2 risks (-70%) are estimated based on a realistic hypothesis of a significant overall reduction in risks for the Russian economy in the 2000s compared to 1990 mi. We consider the “oil” factor on a par with external influences (global 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 to the needs of the development of the national economy.

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

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

Results of medium-term forecast modeling. At the first stage of numerical simulation, the self-development of the situation was simulated, and the increments of the “oil” and “price” factors served as sources of impulsive action on the system. It was assumed that hydrocarbon production in the Russian Federation by 2020 would increase by about 10% compared to 2013 (up to 1250 million tons of oil equivalent - according to the guidelines 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 changes in FDI and external risks were not considered.

Calculations have shown that for given impulsive effects, the predicted change in the GDP factor in 2020 is: -12%, budget revenues will decrease by 22%, investments in fixed assets - by 28%; the gross value added of the manufacturing industry will decrease by 9%, the scientific and educational complex - by 7% compared to the level of 2013. Thus, with self-regulation (self-development) of the situation, crisis tendencies are predicted in the Russian economy. 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 simulating the controlled development of the system, the following factors were chosen as factors subject to control influences (see Table 1): investments, FDI, industry, NPLs, infrastructure, risks. This implies state stimulation of the relevant economic processes, sectors of the economy and activities through the implementation of a purposefully regulated policy. In addition, measures are being considered to reduce risks and stimulate economic growth (at the macro level). Sequentially set “weak” increments of the values of all the factors listed above at the level of 10% (risks – reduction by 10%) made it possible to assess the sensitivity of the economy to control actions in these areas of regulation.

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

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

The simulation result corresponds to an unfavorable scenario of economic development. The figures obtained are below the forecast targets of the Ministry of Economic Development of Russia for 2020: according to the conservative long-term development scenario developed by the Ministry, GDP growth should be 29% by 2020 compared to 2013. Extrapolation of scenario trends according to the forecast for 2018 gives indicators of growth by 2020 (compared to 2013) by 10% and 16%.

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

Rice. 1. Estimated values of the intensity of control actions required to achieve the target GDP growth by 16% by 2020 compared to 2013

In other words, to ensure economic growth, relatively small efforts are required to stimulate industry and infrastructure due to a fairly strong base, while maximum regulatory efforts are needed to attract investment and develop the innovation sector.

The results of the forecast estimate show that the required increase in investment should be almost two and a half times higher than the increase in the target indicator (Fig. 2), as was the case, for example, in the period 2001-2007. The predicted growth of NPL turns out to be relatively slow, despite the high intensity of the calculated control action. Probably, the reason lies in the current costly nature of the development of the innovation sphere, when the activities of the NJC are assessed to a greater extent by the costs of innovation (the share of R&D expenditures in GDP), and not by the real effect of the economy.

Rice. 2. Forecast indicators of the growth of the factors of the model by solving the inverse problem (2013 = 100)

In general, the results of solving the inverse problem, in our opinion, are quite natural. First of all, it is necessary to create a favorable investment climate that contributes to the accumulation of domestic and foreign investment, as well as the innovative nature of economic development: the relationship of these factors in the system will enhance the positive impact of other factors on the target indicator from the outside.

The results obtained, in our opinion, are very informative, should be recognized as preliminary in many respects. Further study of the possibilities of cognitive modeling is required to substantiate economic forecasts and regulatory policy, primarily when choosing its priority areas. Based on our experience, we can note that the cognitive approach is most effective in analyzing and predicting the development of complex economic systems. A feature of this approach is the use of quantitative analysis methods in combination with the construction of model structures based on the subjective vision of the situation. Each stage of the work is based on the decisions of the researcher, the result 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” in the composition of mathematical tools used in economic research, including solving problems of a predictive nature. We believe that the further development of the cognitive approach to the study of the Russian economy will make it possible to obtain effective tools both for making forecasts and for substantiating decisions on managing emerging problem situations.

Literature

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In order to understand and analyze the behavior of a complex system, a structural diagram of cause-and-effect relationships is built. Such schemes that interpret the opinion and views of the decision maker are called a cognitive map.

The term "cognitive map" was coined by the psychologist Tolman in 1948. A cognitive map is a type of mathematical model that allows you to formalize the description of a complex object, problem or system functioning and identify the structures of cause-and-effect relationships between the elements of the system, the complex object that make up the problem and assess the consequences as a result of impact on these elements or changes in the nature of relationships. The English scientist K.Idei suggested using cognitive maps for collective development and decision-making.

Cognitive map of the situation is a directed graph, the nodes of which are some objects (concepts), and the arcs are the connections between them, characterizing the cause-and-effect relationships.

The development of the model begins with the construction of a cognitive map that reflects the situation "as is". On the basis of the formed cognitive map, the self-development of the situation is modeled in order to identify positive development trends. Self-development allows you to compare subjective expectations with model ones.

The main concept in this approach is the concept of "situation". The situation is characterized by a set of so-called basic factors, with the help of which the processes of changing states in a situation are described. Factors can influence each other, and such an influence can be positive, when an increase (decrease) in one factor leads to an increase (decrease) in another factor, and negative, when an increase (decrease) in one factor leads to a decrease (increase) in another factor.

The matrix of mutual influences presents the weights of only direct influences between factors. Rows and columns of the matrix are mapped to the factors of the cognitive map, and the signed value at the intersection of the i-th row and the j-ro column indicates the weight and direction of the influence of the i-ro factor on the j-th factor. To display the degree (weight) of influence, a set of linguistic variables such as "strong", "moderate", "weak", etc. is used; such a set of linguistic variables are compared with numerical values from the interval: 0.1 - "very weak"; 0.3 - "moderate"; 0.5 - "significant"; 0.7 - "strong"; 1.0 - "very strong". The direction of influence is given by a sign: positive, when an increase (decrease) in one factor leads to an increase (decrease) in another factor, and negative, when an increase (decrease) in one factor leads to a decrease (increase) in another factor.

Identification of Initial Trends

Initial tendencies are given by linguistic variables of the type

"strongly", "moderately", "weakly", etc.; such a set of linguistic variables are compared with numerical values from the interval . If a trend is not set for some factor, this means that either there are no noticeable changes in the factor under consideration, or there is not enough information to evaluate the existing trend on it. When modeling, it is considered that the value of this factor is 0 (i.e., it does not change).

Selection of target factors

Among all the selected factors, it is necessary to determine the target and control factors. Target factors are factors whose dynamics must be brought closer to the required values. Ensuring the required dynamics of target factors is the solution that is pursued when building a cognitive model.

Cognitive maps can be used to qualitatively assess the influence of individual concepts on each other and on the stability of the system as a whole, to model and evaluate the use of various strategies in decision-making and forecast decisions.

It should be noted that the cognitive map reflects only the fact that the factors influence each other. It does not reflect either the detailed nature of these influences, nor the dynamics of changes in influences depending on changes in the situation, nor temporary changes in the factors themselves. Taking into account all these circumstances requires a transition to the next level of structuring information displayed in a cognitive map, that is, a cognitive model. At this level, each relationship between the factors of the cognitive map is revealed to the corresponding equation, which can contain both quantitative (measured) variables and qualitative (not measured) variables. At the same time, quantitative variables enter in a natural way in the form of their numerical values, since each qualitative variable is associated with a set of linguistic variables, and each linguistic variable corresponds to a certain numerical equivalent in the scale [-1,1]. With the accumulation of knowledge about the processes occurring in the situation under study, it becomes possible to reveal in more detail the nature of the relationships between factors.

There are mathematical interpretations of cognitive maps, such as soft mathematical models (the famous Lotka-Volterra model of the struggle for existence). Mathematical methods it is possible to predict the development of the situation and analyze the stability of the solution obtained. There are two approaches to the construction of cognitive maps - procedural and process. A procedure is an action that is discrete in time and has a measurable result. Mathematics made significant use of discreteness, even if we measured by linguistic variables. The process approach speaks more about maintaining processes, it is characterized by the concepts of “improve”, “activate”, without reference to measurable results. The cognitive map of this approach has an almost trivial structure - there is a target process and surrounding processes that have a positive or negative impact on it.

There are two types of cognitive maps: traditional and fuzzy. Traditional maps are set in the form of a directed graph and represent the modeled system as a set of concepts that display its objects or attributes, interconnected by cause-and-effect relationships. They are used to qualitatively assess the impact of individual concepts on the stability of the system.

In order to expand the possibilities of cognitive modeling, fuzzy cognitive maps are used in a number of works. In a fuzzy cognitive map, each arc determines not only the direction and nature, but also the degree of influence of the associated concepts.

Cognitive modeling

Introduction

1. Concepts and essence of "Cognitive modeling" and "Cognitive map"

2. Problems of the cognitive approach

Conclusion

List of used literature

INTRODUCTION

In the middle of the 17th century, the famous philosopher and mathematician René Descartes uttered an aphorism that has become a classic: "Cogito Ergo Sum" (I think, therefore I am). The Latin root cognito has an interesting etymology. It consists of the parts “co-“ (“together”) + “gnoscere” (“I know”). AT English language there is a whole family of terms with this root: "cognition", "cognize", etc.

In the tradition that we have designated by the term "cognitive", only one "face" of thought is visible - its analytical essence (the ability to decompose the whole into parts), decompose and reduce reality. This side of thinking is associated with the identification of cause-and-effect relationships (causality), which is characteristic of reason. Apparently, Descartes absolutized reason in his algebraic system. Another "face" of thought is its synthesizing essence (the ability to construct a whole from an unprejudiced whole), perceive the reality of intuitive forms, synthesize solutions and anticipate events. This side of thinking, revealed in the philosophy of Plato and his school, is inherent in the human mind. It is no coincidence that we find two bases in Latin roots: ratio (rational relations) and reason (reasonable insight into the essence of things). The rational face of thought originates from the Latin reri ("to think"), going back to the Old Latin root ars (art), then turned into the modern concept of art. Thus, reason (reasonable) is a thought akin to the work of an artist. Cognitive as "reason" means "the ability to think, explain, justify actions, ideas and hypotheses."

For "strong" cognition, a special, constructive status of the category "hypothesis" is essential. It is the hypothesis that is the intuitive starting point for deducing the image of the solution. When considering the situation, the decision maker discovers in the situation some negative links and structures (“breaks” in the situation) that are to be replaced by new objects, processes and relationships that eliminate the negative impact and create a clearly expressed positive effect. This is the essence of innovation management. In parallel with the discovery of the "breaks" of the situation, often qualified as "challenges" or even "threats", the subject of management intuitively imagines some "positive answers" as integral 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 explore problems with fuzzy factors and relationships; - take into account changes in the external environment; - use objectively established trends in the development of the situation in your own interests.

Such technologies are gaining more and more confidence from structures involved in strategic and operational planning at all levels and in all areas of management. Application of cognitive technologies in economic sphere allows you to quickly develop and justify a strategy for the economic development of an enterprise, bank, region or the whole state, taking into account the impact of changes in the external environment. In the field of finance and the stock market, cognitive technologies make it possible to take into account the expectations of market participants. In the military field and the field of information security, the use of cognitive analysis and modeling makes it possible to counter strategic information weapons, to recognize conflict structures without bringing the conflict to the stage of an armed clash.

1. Concepts and essence of "Cognitive modeling" and "Cognitive map"

A cognitive modeling methodology designed for analysis and decision making in ill-defined situations was proposed by Axelrod. It is based on modeling the subjective ideas of experts about the situation and includes: a methodology for structuring the situation: a model for representing expert knowledge in the form of a signed digraph (cognitive map) (F, W), where F is a set of situation factors, W is a set of cause-and-effect relationships between factors situations; methods of situation analysis. At present, the methodology of cognitive modeling is developing in the direction of improving the apparatus for analyzing and modeling the situation. Here, models for forecasting the development of the situation are proposed; methods for solving inverse problems

Cognitive map (from Latin cognitio - knowledge, cognition) - an image of a familiar spatial environment.

Cognitive maps are created and modified as a result of the active interaction of the subject with the outside world. In this case, cognitive maps of varying degrees of generality, “scale” and organization can be formed (for example, an overview map or a path map, depending on the completeness of the representation of spatial relations and the presence of a pronounced reference point). This is a subjective picture, having, first of all, spatial coordinates, in which individual perceived objects are localized. A path map is singled out as a sequential representation of links between objects along a certain route, and an overview map as a simultaneous representation of the spatial arrangement of objects.

leading scientific organization The Institute of Control Problems of the Russian Academy of Sciences, subdivision: Sector-51, scientists Maksimov V.I., Kornoushenko E.K., Kachaev S.V., Grigoryan A.K., is engaged in the development and application of cognitive analysis technology. and others. On them scientific papers in the field of cognitive analysis and this lecture is based.

The technology of cognitive analysis and modeling (Figure 1) is based on cognitive (cognitive-targeted) structuring of knowledge about an object and its external environment.

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 control object and the most significant (basic) factors of control and the environment that affect the transition of the object to these states, as well as the establishment of cause-and-effect relationships between them at a qualitative level, taking into account mutual influence factors to each other.

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

2. Cognitive (cognitive-targeted) structuring of knowledge about the object under study and its external environment based on PEST-analysis and SWOT-analysis

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

P olicy - policy;

E economy - economy;

S ociety - society ( sociocultural 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 system analysis, because the factors related to the listed four aspects are generally closely interconnected and characterize the various hierarchical levels of society as a system.

In this system, there are determining links directed from the lower levels of the system hierarchy to the upper ones (science and technology affect the economy, the economy affects politics), as well as reverse and interlevel links. A change in any of the factors through this system of connections can affect all the others.

These changes may pose a threat to the development of the object, or, conversely, provide new opportunities for its successful development.

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

S trends - strengths;

W eaknesses - shortcomings, 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 opportunities and allows you to determine the actual problem areas, bottlenecks, chances and dangers, taking into account environmental factors.

Opportunities are defined as circumstances conducive to favorable development object.

Threats are situations in which damage to an object can be caused, for example, its functioning can be disrupted or it can lose its existing advantages.

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

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

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

Cognitive modeling on the basis of the situational analysis carried out makes it possible to prepare alternative solutions to reduce the degree of risk in the identified problem areas, to predict possible events that may most severely affect the position of the object being modeled.

The theory of organizational knowledge creation by I. Nonaki and H. Takeuchi.

Individual and organizational learning.

Cognitive analysis and modeling in strategic management

The essence of the concept of cognition. organization cognition.

TOPIC 5. COGNITIVITY AS A PREREQUISITE FOR THE STRATEGIC DEVELOPMENT OF THE ENTERPRISE.

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

cognitive science- interdisciplinary (philosophy, neuropsychology, psychology, linguistics, computer science, mathematics, physics, etc.) scientific direction, which studies the methods and models of the formation of knowledge, cognition, universal structural schemes of thinking.

Cognitiveness (from Latin cognitio - knowledge, study, awareness) within the framework of management science means the ability of managers to mentally perceive and process external information. The study of this concept is based on the mental processes of the individual and the so-called "mental states" (confidence, desire, belief, intentions) in terms of information processing. This term is also used in the context of the study of the so-called "contextual knowledge" (abstractization and concretization), as well as in areas where concepts such as knowledge, skills or learning are considered.

The term "cognitivity" is also used in a broader sense, meaning the "act" of cognition or self-knowledge itself. In this context, it can be interpreted as the emergence and "becoming" of knowledge and the concepts associated with this knowledge, reflected both in thoughts and in actions.

Organization Cognitiveness characterizes the totality cognitive abilities individual people in the company and the effects that arise from the combination of individual cognitive abilities. The application of this concept in relation to a company (organization, firm, enterprise) means the intention to consider it in a plane that is characterized by a specific apparatus of analysis and a special angle of view on the interaction of the enterprise or its components with the external environment.

Term organization cognition allows you to assess the company's ability to assimilate information and turn it into knowledge.

One of the most productive solutions to problems that arise in the field of management and organization is the application of cognitive analysis.

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

Cognitive analysis is sometimes referred to by researchers as "cognitive structuring". Cognitive analysis is considered as one of the most powerful tools for studying an unstable and semi-structured environment. It contributes to a better understanding of the problems existing in the environment, the identification of contradictions and a qualitative analysis of ongoing processes.

The essence of cognitive (cognitive) modeling - the key moment of cognitive analysis - is to reflect the most complex problems and trends in the development of the system in a simplified form in the model, to explore possible scenarios for the emergence of crisis situations, to find ways and conditions for their resolution in a model situation. The use of cognitive models qualitatively increases the validity of managerial decision-making in a complex and rapidly changing environment, saves the expert from "intuitive wandering", saves time for understanding and interpreting events occurring in the system. The use of cognitive technologies in the economic sphere makes it possible to develop and justify the strategy for the economic development of an enterprise in a short time, taking into account the impact of changes in the external environment.

Cognitive modeling- this is a method of analysis that determines the strength and direction of the influence of factors on the transfer of the control object to the target state, taking into account the similarities and differences in the influence of various factors on the control object.

Cognitive analysis consists of several stages, each of which implements a specific task. Consistent solution of these problems leads to the achievement of the main goal of cognitive analysis.

We can single out the following stages, which are typical for the cognitive analysis of any situation:

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

2. The study of a complex situation from the standpoint of the goal: collection, systematization, analysis of existing statistical and qualitative information regarding the control object and its external environment, determination of the requirements, conditions and restrictions inherent in the situation under study.

3. Identification of the main factors influencing the development of the situation.

4. Determining the relationship between factors by considering cause-and-effect chains (building a cognitive map in the form of a directed graph).

5. Studying the strength of mutual influence of different factors. For this, both mathematical models are used that describe some precisely identified quantitative relationships between factors, as well as the subjective views of an expert regarding non-formalizable qualitative relationships between factors.

As a result of passing stages 3-5, a cognitive model of the situation (system) is built, which is displayed in the form of a functional graph. Therefore, we can say that stages 3 - 5 are cognitive modeling.

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

7. Using a cognitive model to determine possible options for the development of a situation (system), to find ways, mechanisms to influence the situation in order to achieve the desired results, prevent undesirable consequences, that is, develop a management strategy. Setting the target, desired directions and the strength of the change in the trends of the processes in the situation. Choosing a set of measures (a set of control factors), determining their possible and desired strength and direction of impact on the situation (concrete practical application of the cognitive model).

Classic cognitive map is a directed graph in which the privileged vertex is some future (usually target) state of the control object, the remaining vertices correspond to factors, the arcs connecting the factors with the state vertex have a thickness and sign corresponding to the strength and direction of influence of this factor on the transition of the control object into a given state, and the arcs connecting the factors show the similarity and difference in the influence of these factors on the control object.

A cognitive map consists of factors (elements of the system) and links between them.

In order to understand and analyze the behavior of a complex system, a block diagram of cause-and-effect relationships of system elements (situation factors) is built. Two elements of the system A and B are depicted on the diagram as separate points (vertices) connected by an oriented arc, if element A is connected to element B by a causal relationship: A à B, where: A is the cause, B is the effect.

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

Such schemes for representing cause-and-effect relationships are widely used to analyze complex systems in economics and sociology.

Example. A cognitive block diagram for analyzing the problem of energy consumption can look like this (Fig. 5.1):

Rice. 5.1. Cognitive block diagram for the analysis of the problem of energy consumption

The cognitive map reflects only the fact of the presence of influences of factors on each other. It does not reflect either the detailed nature of these influences, nor the dynamics of changes in influences depending on changes in the situation, nor temporary changes in the factors themselves. Taking into account all these circumstances requires a transition to the next level of information structuring, that is, to a cognitive model.

At this level, each relationship between the factors of the cognitive map is revealed by the corresponding dependencies, each of which can contain both quantitative (measured) variables and qualitative (not measured) variables. In this case, quantitative variables are presented in a natural way in the form of their numerical values. Each qualitative variable is associated with a set of linguistic variables that reflect the various states of this qualitative variable (for example, consumer demand can be “weak”, “moderate”, “rush”, etc.), and each linguistic variable corresponds to a certain numerical equivalent in the scale. With the accumulation of knowledge about the processes occurring in the situation under study, it becomes possible to reveal in more detail the nature of the relationships between factors.

Formally, a cognitive model of a situation can, like a cognitive map, be represented by a graph, but each arc in this graph already represents a certain functional relationship between the corresponding factors; those. the cognitive model of the situation is represented by a functional graph.

An example of a functional graph reflecting the situation in a conditional region is shown in fig. 5.2.

Fig.5. 2. Functional graph.

Note that this model is a demonstration model, so many environmental factors are not taken into account in it.

Such technologies are gaining more and more confidence from 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 makes it possible to develop and justify the strategy for the economic development of an enterprise in a short time, taking into account the impact of changes in the external environment.

The use of cognitive modeling technology makes it possible to act proactively and not to bring potentially dangerous situations to the level of threatening and conflict, and in case of their occurrence, to make rational decisions in the interests of the enterprise.