КОГНИТИВНОЕ МОДЕЛИРОВАНИЕ СЦЕНАРИЕВ РАЗВИТИЯ СОЦИО-ЭКОНОМИЧЕСКОЙ СИСТЕМЫ НА ПРИМЕРЕ ЮЖНОГО ФЕДЕРАЛЬНОГО ОКРУГА - Студенческий научный форум

IX Международная студенческая научная конференция Студенческий научный форум - 2017

КОГНИТИВНОЕ МОДЕЛИРОВАНИЕ СЦЕНАРИЕВ РАЗВИТИЯ СОЦИО-ЭКОНОМИЧЕСКОЙ СИСТЕМЫ НА ПРИМЕРЕ ЮЖНОГО ФЕДЕРАЛЬНОГО ОКРУГА

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SCENARIOS MODDELING FOR THE SOCIO-ECOLOGICAL-ECONOMIC SYSTEM DEVELOPMENT (BASED ON THE SOUTH FEDERAL REGION EXAMPLE)

Based on the growing value of environmental factors in the sustainable development in the regional socio-ecological-economic system, it is important to estimate scenario modeling task in the regional systems development and different strategies impact for the implementation for territorial-industrial complex.

Management decision-making in environmental management model competitive territorial industrial complex has not determinated perfectly in practice yet; economic decisions are made in the absence of predictability result influence. Methodology cognitive modeling is one of the most effective methods for the complex study at the conceptual and mathematical (pulsed) levels poorly structured social, ecological and economic systems and internal processes in it. We consider the main characteristics of socio-ecological-economic system the study to construct a cognitive map (conceptual level), in the South Federal region.

There are more than 1620 industrial natural resources enterprises in the South Federal region. This region is characterized by the concentration - electricity, - metallurgy, - construction, - fuel, chemical and petrochemical, - pulp and paper, glass, porcelain and faience, light, food and flour-and-cereals and feed mill capacity as a territorial- industrial complex. This negative (destructive) system impact on the environment. The government conservation and protection programs of natural resources in the South Federal region, namely by "Provision of energy conservation and energy efficiency of the South Federal region in 2016-2020 years", "protection, reproduction and use of natural resources in the South Federal region region in 2016-2020 years" "in the years 2016-2020 Development of forestry of the South Federal region region." It is not approved in the field of environmental safety programs. At the same time the federal legislation of the Russian Federation provides for a radical restructuring of the valuation of pollution sources of the principle of protection, considering the environmental safety as a part of national security. We can expect, that in the near future will be adopted by the project "Russian environmental safety strategy for the period up to 2025", which will be an incentive for the leadership of the South Federal region to the adoption of a regional in the area of the environmental security program.

The processes interdependence of the ecological, technological and social complex development dynamics in the of the South Federal region area considered by us with the help of a software product - decision support system The research was conducted by scientists: . Gorelova G. V., Prichina O.S1., Tselykh L.A 2., Thibeault I.V. This allows to take into account identified strong ties constructed cognitive maps for a large volume of processed empirical basis of the regional socio-ecological-economic and socio-technical systems. DSS in the "Igla" cognitive modeling algorithm territorial socio-ecological-economic system includes the following steps: Identify the factors (social, economic, environmental) that affect the performance of the enterprise, the region and the country. We have identified by 39 factors.

Firstly, it must be the key target factors identification. In this case, the target factors of social, ecological and economic systems include:

1. The deterioration of public health reduce in the working or living area.

2. Reducing the environmental stress level 3. The level of financial and budgetary performance increasing in the enterprise, increasing GRP GDP .Mutual positive impact factors is presented in Figure 1

Figure1. Relative positive impact factors (level 1 slice)

Consider the impact of communication on the target factor "level of environmental stress" the following factors:• The availability of environmental program of the enterprise (factor №20);• The level of scientific and technological development of production (factor №24);•The level of financial and budgetary efficiency (factor №39)Communication factors №20, №24, №39 to the target - "the level of environmental stress" (№38 factor) are direct, decisive in terms of (means - purpose), which is confirmed by the results of calculations presented in Table 4. In this case, the system analysis data interference factors considered (. Figure 1) shows that only the direct expenditure of funds for the factors: "the level of physical and moral deterioration of environmental protection equipment" (№2 factor), "conflict of vested interests' (factor №36)," ecological insurance "(factor №37) is ineffective because of their autonomy and the lack of direct links with the target factor.

Table 1.

Table 1A fragment of the matrix of mutual positive influence

 

30

31

32

33

34

35

36

37

38

39

20

0.3072

0

0

0

0.384

0.384

0

0.8

1

1

21

0.378

0.384

0.70

0.384

0.2268

0.288

0.384

0.2592

0.54

0.54

22

0.8

0.384

0.384

0.384

0.48

0.48

0.64

0.6

0.48

0.48

23

1

0.2458

0.4096

0.2458

0.6

0.6

0.8

0.64

0.3072

0.3072

24

0.360

0.1382

0.1382

0.1382

0.288

0.288

0.288

0.48

1

1

25

0.8

0.384

0.384

0.384

0.48

0.48

0.64

0.6

0.48

0.48

26

0.54

0.1843

0.3072

0.1843

0.36

0.36

0.48

0.54

0.48

0.48

27

0.1843

0

0

0

0.2304

0.2304

0

0.384

0.8

1

28

0.512

0

0

0

0.384

0.384

0.64

0.64

0.512

0.512

29

0.2765

0

0

0

0.3456

0.3456

0

0.72

0.648

0.648

30

0.8

0.1475

0.2458

0.1475

0.6

0.6

0.8

0.9

0.54

0.54

31

0.1475

0

0

0

0.1843

0.1843

0

0.3072

0.8

0.8

32

0.2458

0

0.64

0

0.3072

0.3072

0

0.512

0.8

0.8

33

0.1475

0

0

0

0.1843

0.1843

0

0.3072

0.64

0.8

34

0.6

0.1843

0.3072

0.1843

0.3240

0.3240

0.6

0.54

0.324

0.324

35

0.6

0.1843

0.3072

0.1843

0.324

0.324

0.6

0.54

0.6

0.6

36

0.8

0

0

0

0.6

0.6

0.64

1

0.8

0.8

37

0.9

0.3072

0.512

0.3072

0.54

0.54

1

0.8

0.48

0.48

38

0.54

0.8

0.8

0.64

0.324

0.6

0.8

0.48

0.8

1

39

0.54

0.8

0.8

0.8

0.324

0.6

0.8

0.48

1

0.8

Held at the state level monitoring only for the following indicators: "annual gross emissions of pollutants into the air" (factor №7), «annual total emissions of substances 1 and 2 classes of danger" (factor №8), «annual gross discharge of pollutants from wastewater "(factor №9) and" gross annual discharge of substances 1 and 2 classes of danger with sewage "(№10 factor) will not bring the proper result because identified the factors considered relationships are a logical consequence of the relationship with the parent (in system performance) controlling factor "presence of the company environmental program." The mutual negative influence of factors is presented in Figure 2.

Figure 2.The mutual negative influence of factors (level 1 slice)

To check the reliability of connections and their impact construct graphs mutual consonance (Figure 3) and mutual dissonance (Figure 4).

Figure 3.Mutual consonance (level 1 slice)

Figure 4. Mutual dissonance (level slice 1)

Analysis of consonance and dissonance (Fig.3 and Fig.4) indicates that the indicators available in other factors absent communication between the slice, so they have a high degree of reliability. Modeling the behavior of social, ecological and economic systems in decision support system allows the official decision-maker (the official, the founder, director, chief engineer, etc.) reasonably predict, select and fund projects (programs, grants), whose contribution to the complex territory development and industrial complex enterprises on it on target factors is maximal. Dynamic modeling of 39 factors affecting the socio-ecological-economic system has allowed to consider alternatives to 10704 of the situation and choose from the 13 most responsible for the targeting criteria 3 best case scenario. Changes in the target states of the factors in the impact of the scenarios on the three selected alternatives is represented in Figures 5.6.The y-axis reflects the change in value of the selected concept. The x-axis simulation cycles, which are very weakly correlated with the real time in a simulated system, but reflect the dynamics of the flow pulse process fuzzy cognitive map. Figures 5.6 show the results of all 3 alternatives to reach the target state by the factor of "the level of deterioration of the health of the population", while as on the factor "level of environmental stress" as close as possible to the desired result is the only alternative to №10704, presented in table 5.

Table 2.

Parameters controllable factors alternative №10704

Stepг

Concept

Level

1

The level of technological discipline

Very high

1

Application of management in the stages of development of technical documentation

Very high

1

The presence of the company environmental program

Very high

1

The level of scientific and technological development of production

Very high

1

The deficit of working capital units of the technological chain

Very high

1

Ecological insurance

Very high

Figure 5.

Projected dynamics of changes in the state factor "level of deterioration of the health of the population"

Figure 6. Projecteddynamics of change factor state "level of environmental stress"

Conclusion: The article on the example of the South Federal region discussed scenario forecasting algorithm for the task difficult, semi structured territorial socio-ecological-economic system. This study allows to set:1. Within the framework of static modeling to identify nodal sections negative interactions influence and territorial factors caused the economic development component of the target environmental performance - the level of environmental stress. In particular, it is determined that conducted at state level monitoring of emissions performance in the air and waste water will not bring proper integral effect for the whole socio-ecological-economic system, as identified as a result of the factors considered relationships modeling is a logical consequence of correlation factor of a higher order of importance (in system performance) - "Presence of the company environmental program," and in this case, as practice shows - its absence.2. As part of the dynamic simulation scenarios get 10704 changes the behavior of socio-ecological-economic system of the South Federal region as a result of specific management actions, including the concept of "the presence of the company environmental program" as the dominant factor in reducing the environmental stress level of socio-ecological-economic system of the South Federal region region.3. Select from the 13 most responsible for the targeting criteria - 3 scenarios of the likely socio-ecological-economic system (at the level of consonance), select and compare the effects of these scenarios (for effective system component) for management decision making in the selection of projects, programs, grants, aimed at the development of environmental management of the enterprise (site).

References:

  1. Распоряжение Правительства РФ от 07.02.2011 N 165-р (ред. от 26.12.2014) http://www.consultant.ru/document/cons_doc_LAW_110784/

  2. Исмиханов З.Н. Моделирование социально-экономического развития региона на основе когнитивного подхода (на примере республики Дагестан) // Бизнес-информатика. 2015. № 2 (32). С. 59-68.

  3. Каранашев А.Х., Целых Л.А., Причина О.С. Теоретическое обоснование использования положений нечеткой логики в моделях выбора корпоративной культуры с учетом внешних и внутренних факторов ее функционирования // Известия Кабардино-Балкарского государственного университета. 2015. Т. V. № 4. С. 33-37.

  4. Коростелев, Д.А. Система поддержки принятия решений на основе нечетких когнитивных моделей «ИГЛА» / Д.А. Коростелев, Д.Г. Лагерев, А.Г. Подвесовский // Одиннадцатая национальная конференция по искусственному интеллекту с международным участием КИИ-2008 (28 сентября – 3 октября 2008 г., г. Дубна, Россия): Труды конференции. В 3-х т. Т. 3. – М.: ЛЕНАНД, 2008. – С. 329-336.

  5. Перфильев А.Д. Когнитивная структуризация факторов системы управления малыми инновационными предприятиями / Экономика и предпринимательство, Москва, 2015. № 11-1 (64-1). С. 517-521.

  6. Подвесовский А.Г. Применение нечетких когнитивных моделей для формирования множества альтернатив в задачах принятия решений / А.Г. Подвесовский, Д.Г. Лагерев, Д.А. Коростелев // Вестник Брянского государственного технического университета. - 2009. - № 4 (24). – С. 77-84)

  7. Причина О.С., Опыт использования методов когнитивного моделирования в практике корпоративного управления // Научные труды Московского университета имени С.Ю. Витте сборник научных статей. Москва, 2015. С. 159-169.

  8. Tselykh L.A., Panfilova E.A., Prichina O.S., Karasheva A.G., Karanashev A.K. Methods of fuzzy set theory in the purpose of expansion of the value chains based on the main factors of corporate culture / Mediterranean Journal of Social Sciences, Scopus 2015. Т. 6. № 5 S3. С. 249-258.

  9. Thibeault I.V., Prichina O.S., Gorelova G.V. Cognitive Russian modeling in the system of corporate governmance / Mediterranean Journal of Social Sciences. Scopus 2015. Т. 6. № 2. С. 442-452.

  10. Регионы России. Социально-экономические показатели. 2015: Р32 Стат. сб. / Росстат. М., 2015. 1266 с. http://www.gks.ru/free_doc/doc_2015/region/reg-pok15.pdf

1 Причина О.С., Опыт использования методов когнитивного моделирования в практике корпоративного управления // Научные труды Московского университета имени С.Ю. Витте сборник научных статей. Москва, 2015. С. 159-169.

2 Tselykh L.A., Panfilova E.A., Prichina O.S., Karasheva A.G., Karanashev A.K. Methods of fuzzy set theory in the purpose of expansion of the value chains based on the main factors of corporate culture / Mediterranean Journal of Social Sciences, Scopus 2015. Т. 6. № 5 S3. С. 249-258.

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