The Logic and Principles of Intelligent Machines’ Decision-Making in the Cyber Economy

  • Alexander V. Yudin
Part of the Contributions to Economics book series (CE)


This chapter focuses on the principles behind the functioning of intelligent systems and machines in the economic activities of the cyber economy. Using the example of an intelligent system for the management of the construction of a road, the author illustrates the possibilities for the automatization of business processes. It is shown that on the basis of data from remote probing of the Earth, processed with the help of AI methods, it is possible to determine the economic state of the subject of a space survey and solve the economic tasks connected to development, monitoring, and provision of the necessary resources for the subject without human participation. This allows for a reduction in the labor intensity of the processes and the likelihood of corruption, and connects the digitization of the Earth from space to the needs of the digital economy.

Mathematical tools are used to show the influence that the usage of intelligent systems and machines has on economic growth and labor efficiency.


Diversification Digital economy Cyber economy Stable economic development Intelligent systems 

JEL Code

O14 L25 


  1. Chursin A, Makarov Y (2015) Management of competitiveness: theory and practice [Text]. Springer, Heidelberg, p 378CrossRefGoogle Scholar
  2. Chursin A, Tyulin A (2017) Competence management and competitive product development: concept and implications for practice [Text]. Springer, Heidelberg, p 234Google Scholar
  3. Chursin A, Vlasov Y, Makarov Y (2017) Innovation as a basis for competitiveness: theory and practice [Text]. Springer, Heidelberg, p 327Google Scholar
  4. Chursin RA, Yudin AV, Grosheva PYu, Filippov PG, Butrova EV (2019) Tool for assessing the risks of R&D projects implementation in high-tech enterprises. In: IOP conference series: materials science and engineering, volume 476, 012005Google Scholar
  5. Kendal SL (2007) An introduction to knowledge engineering. In: Kendal SL, Green M (eds) Springer, London, 287 pGoogle Scholar
  6. Popovich V (2014) Intelligent GIS conceptualization. In: Popovich VV (ed) Information fusion and geographic information systems, Lecture notes in geoinformation and cartography, pp 17–44Google Scholar
  7. Shamin RV, Gurevich PL, Tikhomirov SB (2013) Reaction-diffusion equations with spatially distributed hysteresis. SIAM J Math Anal 45(3):1328–1355CrossRefGoogle Scholar
  8. Shamin RV, Chursin AA, Fedorova LA (2017) The mathematical model of the law on the correlation of unique competencies with the emergence of new consumer markets. Eur Res Stud J XX(3 Part A):39–56Google Scholar
  9. Tyulin A, Chursin A, Yudin A (2017) Production capacity optimization in cases of a new business line launching in a company. Espacios 38:20Google Scholar
  10. Voženilek V (2009) Artificial intelligence and GIS: mutual meeting and passing. In: Voženilek V (ed) 2009 International conference on intelligent networking and collaborative systems, pp 279–284Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander V. Yudin
    • 1
  1. 1.RUDN UniversityMoscowRussia

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