Advertisement

Decision Support System to Prevent Crisis Situations in the Socio-political Sphere

  • Andrey ProletarskyEmail author
  • Dmitry Berezkin
  • Alexey Popov
  • Valery Terekhov
  • Maria Skvortsova
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)

Abstract

A statement and a general structure for solving the problem of assessing possible crisis situations in the Socio-political sphere is proposed. An approach to analyzing and forecasting the development of crisis situations has been implemented on the basis of continuous monitoring of heterogeneous data from various information sources and summarizing the results of assessing threats obtained using various methods. A model for the development of a crisis situation is presented, which considers the situation as the result of the interaction of various agents in a complex network. The method of historical analogy was applied to the situational forecast. The issues of hardware acceleration of analyzing large data streams are considered through the use of a Leonhard processor that processes large amounts of data due to parallelism. When designing the system, an agent-based development methodology is used. The structure of the system and the results of its application for the analysis of the possible development of crisis situations during political rallies are given.

Keywords

Threat Crisis situation Decision support system Hierarchy analysis method Intelligent agent Forecasting Monitoring Cognitive graphics Game theory 

References

  1. 1.
    Schwab, K.: The Fourth Industrial Revolution, p. 198. Crown Publishing Group, New York (2016)Google Scholar
  2. 2.
    Luhmann, N.: Soziologie des Risikos. Berlin, New York: Walter de Gruyter, pp. 9–40 (1991)Google Scholar
  3. 3.
    Beck, U.: From industrial society to the risk society. Theor. Cult. Soc. 9(1), 97–123 (1992)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Castells, M.: The information age: economy, society and culture. In: The Rise of the Network Society, vol. 1, 2nd edn, p. 597. Blackwell Publishers, UK (2010)Google Scholar
  5. 5.
    Saaty, T.L.: The Analytic Hierarchy Process, p. 287. McGraw-Hill, New York (1980)Google Scholar
  6. 6.
    Yeung, D.W.K., Petrosyan, L.A.: Subgame Consistent Economic Optimization, p. 396. Springer Science, NY (2012)Google Scholar
  7. 7.
    Strekalovsky, A.S. (2000).: One way to construct a global search algorithm for dc minimization problems. Nonlinear optimization and related topics. In: Di Pillo, G., Giannessi, F. (eds.) Applied Optimization Series, vol. 36, pp. 429–443. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  8. 8.
    Saaty, T.L.: Mathematical Models of Arms Control and Disarmament, p. 190. John Wiley & Sons, Inc. (1968)Google Scholar
  9. 9.
    Proletarsky, A.V., Berezkin, D.V., Terekhov, V.I.: Identifying information threats to the security of the Russian Federation, predicting their consequences and developing proposals for their prevention. Dyn. Complex Syst. XXI Century 11(4), 22–31 (2017)Google Scholar
  10. 10.
    Chkhartishvili, A.G., Gubanov, D.A., Novikov, D.A.: Social Networks: Models of Information Influence, Control and Confrontation, vol. 189. SpringerGoogle Scholar
  11. 11.
    Skvortsova, M., Terekhov V., Grout, V.: A hybrid intelligent system for risk assessment based on unstructured data. In: 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, pp. 560–564.  https://doi.org/10.1109/eiconrus.2017.7910616 (2017)
  12. 12.
    Lidong, W., Guanghui, W., Cheryl, A.A.: Big data and visualization: methods, challenges and technology progress. Digit. Technol. 1(1), 33–38 (2015)Google Scholar
  13. 13.
    Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: Proceedings of the 2012 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 624–635 (2012)Google Scholar
  14. 14.
    Andreev, A., Berezkin, D., Kozlov, I.: Approach to forecasting the development of situations based on event detection in heterogeneous data streams. In: Kalinichenko, L., Manolopoulos, Y., Malkov, O., Skvortsov, N., Stupnikov, S., Sukhomlin, V. (eds.) Data Analytics and Management in Data Intensive DoHAMns. DAMDID/RCDL 2017. Communications in Computer and Information Science, vol. 822, pp. 213–229. Springer, Cham (2017)Google Scholar
  15. 15.
    Keim, D. et al.: Visual Analytics: Definition, Process, and Challenges. Information Visualization, pp. 154–175. Springer, Berlin (2008)Google Scholar
  16. 16.
    Gusein-Zade, S.M., Tikunov, V.S.: A new technique for constructing continuous cartograms. Cartography Geogr. Inform. Syst. 20(3), 167–173 (1993)CrossRefGoogle Scholar
  17. 17.
    Berezkin, D.V., Terekhov, V.I.: Application of the anamorphizing method for modeling and evaluating changes in geopolitical borders. Artif. Intell. Decis. Making 3, 3–9 (2017)Google Scholar
  18. 18.
    Sakulin, S., Alfimtsev, A., Solovyev, D., Sokolov, D.: Web page interface optimization based on nature-inspired algorithms. Int. J. Swarm Intell. Res. (IJSIR) 9(2), 28–46 (2018)CrossRefGoogle Scholar
  19. 19.
    Nguyen, D.N. et al.: A methodology for developing an agent systems reference architecture. In: Weyns D., Gleizes, M.P. (eds.) Agent-Oriented Software Engineering XI. AOSE 2010. Lecture Notes in Computer Science, vol. 6788, pp. 177–188. Springer, Berlin (2011)Google Scholar
  20. 20.
    Patel, R., Kumar, S.: Visualizing effect of dependency in superscalar pipelining. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), pp. 1–5, Dhanbad (2018)Google Scholar
  21. 21.
    Patel, R., Kumar, S.: The effect of dependency on scalar pipeline architecture. IUP J. Comput. Sci. 11(1), 38–50. Available at SSRN: https://ssrn.com/abstract=3103485 (2017)
  22. 22.
    Popov, A.: An introduction to the MISD technology. In: Proceedings of 50th Hawaii International Conference on System Sciences (HICSS50), pp. 1003–1012 (2017)Google Scholar
  23. 23.
    Chkhartishvili, A.G., Novikov, D.A.: Reflexion and Control: Mathematical Models, p. 373. CRC Press.  https://doi.org/10.1201/b16625 (2014)
  24. 24.
    Chernenkiy, V.M., Gapanyuk, Y.E., Kaganov, Y.T., Dunin, I.V., Lyaskovsky, M.A., Larionov, V.S.: Storing metagraph model in relational, document-oriented, and graph databases. In: Proceedings of the XX International Conference “Data Analytics and Management in Data Intensive Domains” (DAMDID/RCDL’2018), pp. 82–89. http://ceur-ws.org/Vol-2277/paper17.pdf (2018)
  25. 25.
    Chernenkiy, V., Gapanyuk, Y., Revunkov, G., Kaganov, Y., Fedorenko, Y.: Metagraph approach as a data model for cognitive architecture. In: Biologically Inspired Cognitive Architectures Meeting, pp. 50–55. Springer, Cham.  https://doi.org/10.1007/978-3-319-99316-4_7 (2018)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussia

Personalised recommendations