Modeling of management of the information potential of complex economic systems under conditions of risk

Authors

DOI:

https://doi.org/10.15587/2312-8372.2017.98275

Keywords:

economic systems, management of information capacities, conditions of risk, information potential

Abstract

The object of this study is the modeling of management of information capacities of complex economic systems under conditions of risk.

The disadvantages of existing methods of  management are: multidimensionality due to presence of a large number of interrelated elements in some economic system; the variety of management structures due to the uncertainty of influence of the environment; risks and loss due to wrong decision-making; the diversity of objectives of subsystems; multi-functionality.

An approach to modeling of management of information capacities of multilevel and hierarchical systems under conditions of risk is proposed. This approach allowed to develop mathematical tools for management on the basis of information analysis of complex systems and synthesis of modeling methods. It also gives practical recommendations for the integrated use of levels and management approaches under conditions of uncertainty.

The resulting quantitative economic characteristics of the selected management option: net profit, production costs, production volumes, competitiveness in the overall spectrum the crucial tasks – are defined by possibilities, constraints and resources.

The expectation of the effect to be achieved, in the author's interpretation of the results, is due to the reliability of the selected management option and the optimality of economic efficiency criteria formation.

Author Biographies

Margarita Sharko, Kherson National Technical University, 24, Berislav w., Kherson, Ukraine, 73008

Doctor of Economic Sciences, Professor, Head of Department

Department of Economics and Entrepreneurship 

Juliya Burenko, Kherson National Technical University, 24, Berislav w., Kherson, Ukraine, 73008

PhD, Associate Professor

Department of Economics and Entrepreneurship

Nataliya Gusarina, National University of Shipbuilding, 9, Heroes of Ukraine ave., Mykolayiv, Ukraine, 54000

PhD, Associate Professor,

Department of Economics and Organization of Production

References

  1. Mikoni, S. V. (2009). Multicriteria choice on a finite set of alternatives. St. Petersburg: Lan, 272.
  2. Voloshin, A. F., Kudin, V. I. (2015). A sequential analysis of variants in the problems of research and design of complex systems. Kyiv: Kyiv University, 351.
  3. Sharko, M. V. (2015). Formalization of Parameters of Value-Oriented Management of the Development of Industrial Production. Bulletin of Lviv Commercial Academy, 49, 105–109.
  4. Zaichenko, Yu. P., Zaichenko, O. Yu. (2016). Multi-criteria decision-making problems in fuzzy conditions. Proceedings of the VIII International school seminar «Decision theory». Uzhgorod: Uzhgorod National University, 121–122.
  5. Sharko, M. V. (2015). Commercialization of intellectual property in the transfer of technology to the real sector of the economy. Problems of economics, 1, 168–173.
  6. Sharko, M. V., Panchenko, Y. V. (2014). Formation of the policy of intellectual capacity building. Actual problems of economics, 6 (156), 30–40.
  7. Fiser, J., Mashkov, V., Lytvynenko, V. (2015). Representation of System Level Self-Diagnosis in Python Programming Language. Electrotechnic and Computer Systems, 17 (93), 48–54.
  8. Mashkov, V., Smolarz, A., Lytvynenko, V. (2016). Development issues in algorithms for system level self-diagnosis. Informatics, Control, Measurement in Economy and Environment Protection, 6 (1), 26–28. doi:10.5604/20830157.1194261
  9. Baldi, P., Sadowski, P. (2014). The dropout learning algorithm. Artificial Intelligence, 210, 78–122. doi:10.1016/j.artint.2014.02.004
  10. Gupta, M., Mohanty, B. K. (2016). An algorithmic approach to group decision making problems under fuzzy and dynamic environment. Expert Systems with Applications, 55, 118–132. doi:10.1016/j.eswa.2016.02.002
  11. André, É., Liu, Y., Sun, J., Dong, J.-S. (2014). Parameter synthesis for hierarchical concurrent real-time systems. Real-Time Systems, 50 (5-6), 620–679. doi:10.1007/s11241-014-9208-6
  12. Guangyan, L., Peishun, L., Xiaofeng, L., Caiping, X. (2012). Assessment on Reform Solution of Enterprise Management and Control Model Based on Group Hierarchy Grey Method. Procedia Engineering, 37, 42–48. doi:10.1016/j.proeng.2012.04.199
  13. Savina, G., Kavun, S., Caleta, D., Vrsec, M. (2013). Estimation of the Effectiveness and Functioning of Enterprises in Boards of Corporate Security. European Journal of Scientific Research, 104 (2), 304–323.
  14. Pankratova, N., Kondratova, L. (2016). System evaluation of engineering objects’ operating taking into account the margin of permissible risk. Eastern-European Journal of Enterprise Technologies, 3(4(81)), 13–19. doi:10.15587/1729-4061.2016.71126
  15. Ruffino, D., Treussard, J. (2007). Financial Frictions and Risky Corporate Debt. Economic Notes, 36 (1), 77–87. doi:10.1111/j.1468-0300.2007.00172.x
  16. State Statistics Service of Ukraine. Available: http://www.ukrstat.gov.ua
  17. Federal State Statistics Service. Available: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/icstatistics/incomparisons/

Published

2017-03-30

How to Cite

Sharko, M., Burenko, J., & Gusarina, N. (2017). Modeling of management of the information potential of complex economic systems under conditions of risk. Technology Audit and Production Reserves, 2(4(34), 14–19. https://doi.org/10.15587/2312-8372.2017.98275

Issue

Section

Economics and Enterprise Management: Original Research