Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments

Vytautas Petrauskas; Raimundas Jasinevicius; Egidijus Kazanavicius; Zygimantas Meskauskas

doi:doi:10.11648/j.mcs.20200502.11

| Peer-Reviewed

Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments

Vytautas Petrauskas, Raimundas Jasinevicius, Egidijus Kazanavicius, Zygimantas Meskauskas

Published in Mathematics and Computer Science (Volume 5, Issue 2)

Received: 13 March 2020 Accepted: 30 March 2020 Published: 7 April 2020

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The paper advocates a new concept for risk control that makes up one organic closed loop feedback system, with the following stages: 1) the evaluation of the positive and negative features of situation under investigation through strengths, weaknesses, opportunities, and threats (SWOT) analysis, 2) the determination of the level of fuzzy risk concealed in this situation (using RISK evaluation), and 3) the proposal of leverage, recommendations, or actions (through LEVERAGE aggregation) enabling the improvement of target performance. Useful fundamental approaches, definitions, and particularities of this concept concerning SWOT, RISK and LEVERAGES are examined, and for the first time the network type called here the fuzzy SWOT map (FSM) is introduced. This newly proposed instrument appeared as a result of a natural extension of fuzzy cognitive maps paradigm enhanced by dynamic computing with words (CWW) elements and possibilities to use the explainable artificial intelligence (XAI) in the form of fuzzy inference rules. The concept serves for development of functional organization of control systems of complex and dynamically interacting projects or situations and for implementation of adequate set of tools satisfying the concrete system’s requirements. The results of conceptual modeling and the confirmation of the vitality of the approach are presented based on the simplified example of a risk-control system case covering three interacting projects in a complex environment of city development.

Published in	Mathematics and Computer Science (Volume 5, Issue 2)
DOI	10.11648/j.mcs.20200502.11
Page(s)	42-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Complex Systems Science, Dynamic SWOT Analysis, SWOT Engines Networking, Fuzzy SWOT Maps, CWW (Computing with Words), Risk Definition, Fuzzy Risk Evaluation Paradigm, Fuzzy Control System, Conceptual Modeling

References

[1]	S. Emmott and others. Towards 2020 Science, Microsoft Corporation, 2006. Accessed November 22, 2006. (http://research.microsoft.com/towards2020science/downloads/T2020S_ReportA4.pdf).
[2]	R. Jasinevicius, Why today’s systems theory can’t cope with global environmental or marine systems catastrophes and crises?, in: Proceedings of Baltic International Symposium (BALTIC), 2010 IEEE/OES US/EU, August 2010 Riga, Latvia, pp. 24–27, 2010.
[3]	R. Jasinevicius, V. Petrauskas, On fundamentals of global systems control science (GSCS), in: A. Sanayei, I. Zelinka, O. Rössler (Eds.) ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol. 8, pp. 77–87, Springer, Berlin, Heidelberg, 2014.
[4]	E. Gurel, M. Tat, SWOT analysis: A theoretical review. The Journal of International Social Research, vol. 10, no. 51, pp. 994–1006, 2017.
[5]	R. Jasinevičius, V. Petrauskas, Dynamic SWOT analysis as a tool for environmentalists. Environmental Research, Engineering and Management. vol. 1, no. 43, pp. 14–20, 2008.
[6]	R. Jasinevičius, V. Petrauskas, Dynamic SWOT analysis as a tool for system experts. Engineering Economics, vol. 5 no. 50, pp. 33–35, 2006.
[7]	T. Aven, Risk assessment and risk management: Review of recent advances on their foundation, European Journal of Operational Research, vol. 253, pp. 1–13, 2016.
[8]	V. Petrauskas, R. Jasinevicius, E. Kazanavicius, Z. Meskauskas, CWW elements to enrich SWOT analysis, Journal of Intelligent & Fuzzy Systems, vol. 34, no. 1, pp. 307–320, 2018
[9]	A. Balzekiene, E. Gaule, R. Jasinevicius, E. Kazanavicius, V. Petrauskas, Risk evaluation: the paradigm and tools, in: Dregvaite G., Damasevicius R. (eds) Information and Software Technologies. ICIST 2015. Communications in Computer and Information Science, vol. 538, Springer, Cham, pp. 330-342, 2015.
[10]	A. Šotic, R. Rajic, The review of the definition of risk, Online Journal of Applied Knowledge Management, vol. 3, no. 3, pp. 17–26, 2015.
[11]	K. T. Atanassov, On intuitionistic fuzzy sets theory. New York, NY: Springer, 2012.
[12]	L.-H. Chen, C.-C. Tu, Dual bipolar measures of Atanassov’ intuitionistic fuzzy sets, IEEE Transactions on Fuzzy Systems, vol. 22, No. 4, pp. 966–982, 2014.
[13]	B. Kosko, Fuzzy engineering. Prentice-Hall, N. J., 1997.
[14]	A. Konar, Computational intelligence: Principles, techniques and applications, Berlin: Springer, 2005.
[15]	R. Jasinevičius, V. Petrauskas, Sprendimų pagrindimo kompiuterizavimas (Computerization of decision making), Kaunas, Lithuania: Technologija, 2011.
[16]	H. Liao, X. Mi, Z. Xu, J. Xu, F. Herrera, Intuitionistic fuzzy analytic network process, IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2578–2590, 2018.
[17]	E. Herrera-Viedma, F. J. Cabrerizo, J. Kacprzyk, and W. Pedrycz, A review of soft consensus models in a fuzzy environment, Information Fusion, vol. 17, pp. 4–13, 2014.
[18]	Z. Xu, Hesitant fuzzy sets theory, Studies in Fuzziness and Soft Computing. Springer, 2014.
[19]	R. Axelrod, Structure of decision: The cognitive maps of political elites, Princeton, NJ: Princeton University Press, 1976.
[20]	B. Kosko, Fuzzy cognitive maps, International Journal of Man-Machine Studies, Vol. 24, pp. 65–75, 1986.
[21]	R. Jasinevicius, V. Petrauskas, Fuzzy expert maps: The new approach, in: Proceedings of 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Hong Kong, China, pp. 1511–1517, 2008.

Cite This Article

Plain Text BibTeX RIS

APA Style

Vytautas Petrauskas, Raimundas Jasinevicius, Egidijus Kazanavicius, Zygimantas Meskauskas. (2020). Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Mathematics and Computer Science, 5(2), 42-55. https://doi.org/10.11648/j.mcs.20200502.11

Copy | Download

ACS Style

Vytautas Petrauskas; Raimundas Jasinevicius; Egidijus Kazanavicius; Zygimantas Meskauskas. Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Math. Comput. Sci. 2020, 5(2), 42-55. doi: 10.11648/j.mcs.20200502.11

Copy | Download

AMA Style

Vytautas Petrauskas, Raimundas Jasinevicius, Egidijus Kazanavicius, Zygimantas Meskauskas. Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Math Comput Sci. 2020;5(2):42-55. doi: 10.11648/j.mcs.20200502.11

Copy | Download

@article{10.11648/j.mcs.20200502.11,
  author = {Vytautas Petrauskas and Raimundas Jasinevicius and Egidijus Kazanavicius and Zygimantas Meskauskas},
  title = {Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments},
  journal = {Mathematics and Computer Science},
  volume = {5},
  number = {2},
  pages = {42-55},
  doi = {10.11648/j.mcs.20200502.11},
  url = {https://doi.org/10.11648/j.mcs.20200502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20200502.11},
  abstract = {The paper advocates a new concept for risk control that makes up one organic closed loop feedback system, with the following stages: 1) the evaluation of the positive and negative features of situation under investigation through strengths, weaknesses, opportunities, and threats (SWOT) analysis, 2) the determination of the level of fuzzy risk concealed in this situation (using RISK evaluation), and 3) the proposal of leverage, recommendations, or actions (through LEVERAGE aggregation) enabling the improvement of target performance. Useful fundamental approaches, definitions, and particularities of this concept concerning SWOT, RISK and LEVERAGES are examined, and for the first time the network type called here the fuzzy SWOT map (FSM) is introduced. This newly proposed instrument appeared as a result of a natural extension of fuzzy cognitive maps paradigm enhanced by dynamic computing with words (CWW) elements and possibilities to use the explainable artificial intelligence (XAI) in the form of fuzzy inference rules. The concept serves for development of functional organization of control systems of complex and dynamically interacting projects or situations and for implementation of adequate set of tools satisfying the concrete system’s requirements. The results of conceptual modeling and the confirmation of the vitality of the approach are presented based on the simplified example of a risk-control system case covering three interacting projects in a complex environment of city development.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments
AU  - Vytautas Petrauskas
AU  - Raimundas Jasinevicius
AU  - Egidijus Kazanavicius
AU  - Zygimantas Meskauskas
Y1  - 2020/04/07
PY  - 2020
N1  - https://doi.org/10.11648/j.mcs.20200502.11
DO  - 10.11648/j.mcs.20200502.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 42
EP  - 55
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20200502.11
AB  - The paper advocates a new concept for risk control that makes up one organic closed loop feedback system, with the following stages: 1) the evaluation of the positive and negative features of situation under investigation through strengths, weaknesses, opportunities, and threats (SWOT) analysis, 2) the determination of the level of fuzzy risk concealed in this situation (using RISK evaluation), and 3) the proposal of leverage, recommendations, or actions (through LEVERAGE aggregation) enabling the improvement of target performance. Useful fundamental approaches, definitions, and particularities of this concept concerning SWOT, RISK and LEVERAGES are examined, and for the first time the network type called here the fuzzy SWOT map (FSM) is introduced. This newly proposed instrument appeared as a result of a natural extension of fuzzy cognitive maps paradigm enhanced by dynamic computing with words (CWW) elements and possibilities to use the explainable artificial intelligence (XAI) in the form of fuzzy inference rules. The concept serves for development of functional organization of control systems of complex and dynamically interacting projects or situations and for implementation of adequate set of tools satisfying the concrete system’s requirements. The results of conceptual modeling and the confirmation of the vitality of the approach are presented based on the simplified example of a risk-control system case covering three interacting projects in a complex environment of city development.
VL  - 5
IS  - 2
ER  -

Copy | Download

Author Information

Vytautas Petrauskas

Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania
Raimundas Jasinevicius

Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania
Egidijus Kazanavicius

Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania
Zygimantas Meskauskas

Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Vytautas Petrauskas, Raimundas Jasinevicius, Egidijus Kazanavicius, Zygimantas Meskauskas. (2020). Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Mathematics and Computer Science, 5(2), 42-55. https://doi.org/10.11648/j.mcs.20200502.11

Copy | Download

ACS Style

Vytautas Petrauskas; Raimundas Jasinevicius; Egidijus Kazanavicius; Zygimantas Meskauskas. Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Math. Comput. Sci. 2020, 5(2), 42-55. doi: 10.11648/j.mcs.20200502.11

Copy | Download

AMA Style

Vytautas Petrauskas, Raimundas Jasinevicius, Egidijus Kazanavicius, Zygimantas Meskauskas. Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments. Math Comput Sci. 2020;5(2):42-55. doi: 10.11648/j.mcs.20200502.11

Copy | Download

@article{10.11648/j.mcs.20200502.11,
  author = {Vytautas Petrauskas and Raimundas Jasinevicius and Egidijus Kazanavicius and Zygimantas Meskauskas},
  title = {Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments},
  journal = {Mathematics and Computer Science},
  volume = {5},
  number = {2},
  pages = {42-55},
  doi = {10.11648/j.mcs.20200502.11},
  url = {https://doi.org/10.11648/j.mcs.20200502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20200502.11},
  abstract = {The paper advocates a new concept for risk control that makes up one organic closed loop feedback system, with the following stages: 1) the evaluation of the positive and negative features of situation under investigation through strengths, weaknesses, opportunities, and threats (SWOT) analysis, 2) the determination of the level of fuzzy risk concealed in this situation (using RISK evaluation), and 3) the proposal of leverage, recommendations, or actions (through LEVERAGE aggregation) enabling the improvement of target performance. Useful fundamental approaches, definitions, and particularities of this concept concerning SWOT, RISK and LEVERAGES are examined, and for the first time the network type called here the fuzzy SWOT map (FSM) is introduced. This newly proposed instrument appeared as a result of a natural extension of fuzzy cognitive maps paradigm enhanced by dynamic computing with words (CWW) elements and possibilities to use the explainable artificial intelligence (XAI) in the form of fuzzy inference rules. The concept serves for development of functional organization of control systems of complex and dynamically interacting projects or situations and for implementation of adequate set of tools satisfying the concrete system’s requirements. The results of conceptual modeling and the confirmation of the vitality of the approach are presented based on the simplified example of a risk-control system case covering three interacting projects in a complex environment of city development.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Concept of a System Using a Dynamic SWOT Analysis Network for Fuzzy Control of Risk in Complex Environments
AU  - Vytautas Petrauskas
AU  - Raimundas Jasinevicius
AU  - Egidijus Kazanavicius
AU  - Zygimantas Meskauskas
Y1  - 2020/04/07
PY  - 2020
N1  - https://doi.org/10.11648/j.mcs.20200502.11
DO  - 10.11648/j.mcs.20200502.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 42
EP  - 55
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20200502.11
AB  - The paper advocates a new concept for risk control that makes up one organic closed loop feedback system, with the following stages: 1) the evaluation of the positive and negative features of situation under investigation through strengths, weaknesses, opportunities, and threats (SWOT) analysis, 2) the determination of the level of fuzzy risk concealed in this situation (using RISK evaluation), and 3) the proposal of leverage, recommendations, or actions (through LEVERAGE aggregation) enabling the improvement of target performance. Useful fundamental approaches, definitions, and particularities of this concept concerning SWOT, RISK and LEVERAGES are examined, and for the first time the network type called here the fuzzy SWOT map (FSM) is introduced. This newly proposed instrument appeared as a result of a natural extension of fuzzy cognitive maps paradigm enhanced by dynamic computing with words (CWW) elements and possibilities to use the explainable artificial intelligence (XAI) in the form of fuzzy inference rules. The concept serves for development of functional organization of control systems of complex and dynamically interacting projects or situations and for implementation of adequate set of tools satisfying the concrete system’s requirements. The results of conceptual modeling and the confirmation of the vitality of the approach are presented based on the simplified example of a risk-control system case covering three interacting projects in a complex environment of city development.
VL  - 5
IS  - 2
ER  -

Copy | Download