Using decision analysis to achieve the desired image of the future
Research Article
How to Cite
Kharchenko V.S. Using decision analysis to achieve the desired image of the future. Science. Culture. Society. 2022. Vol. 28. No. 3. P. 81-92. DOI: https://doi.org/10.19181/nko.2022.28.3.6 (in Russ.).
Abstract
The article is devoted to the methodology and features of the scientific direction of decision analysis, which make it essential for the state strategy development in order to achieve the desired image of the future. The history of decision analysis is studied, the basics of the scientific direction are considered: systems analysis, decision theory, probabilistic analysis and cognitive psychology. Organizations that actively use the methodology and most popular computer programs for the practical implementation of decision analysis methods are represented. The domestic contribution to the decision analysis is shown. A promising sub-direction which allows strategy development in conditions of conflicting objectives (multicriteria decision analysis) is presented. The modern methodology of decision analysis is considered: the system of values and objectives of the organization, decision context, identification and evaluation of the mathematical model elements, graphical representation of the model using influence diagrams and decision trees, the procedure of mathematical modeling, optimal strategy determination based on the criterion of maximizing/minimizing the objective function value and the criterion of risk profile dominance and sensitivity analysis methods: one-way and two-way sensitivity analysis, probability sensitivity analysis and strategy dominance analysis. The article concludes that the scientific direction of decision analysis has a number of advantages that make it suitable for use in the field of state strategy development.
Keywords:
image of the future, strategy, quantitative methods, decision analysis, mathematical methods, optimization and simulation modeling, sensitivity analysis
References
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2. Howard, R. A. (1984) The Evolution of Decision Analysis. In: R. A. Howard & J. E. Matheson [eds.] Readings on the Principles and Applications of Decision Analysis. Menlo Park, CA, Strategic Decisions Group. Pp. 7-11 (in Eng.).
3. Jaynes, E. T. (2003) Probability Theory: The Logic of Science. Cambridge, UK, Cambridge University Press. 753 pp. ISBN 978-0521592710 (in Eng.).
4. Larichev, O. I. (2000) Teoriya i metody prinyatiya reshenii [Theory and methods of decision-making]. Moscow, Logos Publ. 296 p. ISBN 5-88439-046-7 (in Russ.).
5. Pesce, M., Terzi, S., Al-Jawasreh, R. I. M. [et al] (2018) Selecting sustainable alternatives for cruise ships in Venice using multi-criteria decision analysis. Science of the Total Environment. Vol. 642. Pp. 668–678. DOI 10.1016/j.scitotenv.2018.05.372 (in Eng.).
6. Kharchenko, V. S. (2018) Foreign experience of decision analysis application in solving environmental problems. Scientific Notes of the Russian Academy of Entrepreneurship. Vol. 17. No. 4. Pp. 341-347 (in Russ.).
7. Huang, I. B., Keisler, J., Linkov, I. (2011) Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of the Total Environment. Vol. 409. Issue 19. Pp. 3578–3594. DOI 10.1016/j.scitotenv.2011.06.022 (in Eng.).
8. Kiker, G.A., Bridges, T.S., Varghese, A. [et al] (2005) Application of multicriteria decision analysis in environmental decision making. Integrated Environmental Assessment and Management. Vol. 1. Issue 2. Pp. 95-108. DOI 10.1897/IEAM_2004a-015.1 (in Eng.).
9. Linkov, I, Satterstrom, F. K., Kiker, G. [et al] (2006) From comparative risk assessment to multi-criteria decision analysis and adaptive management: Recent developments and applications. Environment International. Vol. 32. Issue 8. Pp. 1072-1093. DOI 10.1016/j.envint.2006.06.013 (in Eng.).
Article
Received: 22.07.2022
Accepted: 30.09.2022
Citation Formats
Other cite formats:
APA
Kharchenko, V. S. (2022). Using decision analysis to achieve the desired image of the future. Science. Culture. Society, 28(3), 81-92. https://doi.org/10.19181/nko.2022.28.3.6
Section
Sociology of science and scientific knowledge
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