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Model of Evaluating Smart City Projects by Groups of Investors Using a Multifactorial Approach

  • Lakhno Valeriy
  • Malyukov Volodymyr
  • Kryvoruchko Olena
  • Tsiutsiura Mykola
  • Desyatko AlyonaEmail author
  • Medynska Tetyana
Conference paper
  • 38 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1193)

Abstract

In this paper the authors introduce a model for the mathematical support of the decision-making process during the evaluation of investment projects. As an example, the sphere of investing in Smart City development projects is considered. The emphasis in the model is placed on the multifactorial nature of the task in the search of rational financial strategies carried out by investor groups. The model particularly allows groups of investors (players) to evaluate the attractiveness and financial potential of the analyzed projects. At the same time, players can exercise control over a dynamic system in multidimensional project spaces. The model implies subsequent software implementation in a decision support system (DSS) or an expert system for cross-platform software products.

The results presented in the article have been obtained through computational experiments based on the solution of a bilinear multi-step quality game with several terminal surfaces. The scientific novelty of the research lies in the fact that, in contrast to existing solutions and related to conceptual direction of research, the article first considers a new class of multi-step bilinear games. The proposed solution provides an opportunity to correctly and adequately describe the investment processes, given the multifactorial nature of the problem statement. Computational experiments were performed in the MatLab system to search for sets of investors’ preferences and their optimal financial strategies during the analysis of Smart City development projects. The results of computational experiments have proved and confirmed the correctness and adequacy of the model.

Keywords

Game theory Investment strategies Multidimensional case Decision support Multistep game Bilinear equations 

References

  1. 1.
    Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance, and initiatives. J. Urban Technol. 22(1), 3–21 (2015)CrossRefGoogle Scholar
  2. 2.
    Angelidou, M.: Smart cities: a conjuncture of four forces. Cities 47, 95–106 (2015)CrossRefGoogle Scholar
  3. 3.
    Glasmeier, A., Christopherson, S.: Thinking about smart cities. Camb. J. Reg. Econ. Soc. 8, 3–12 (2015)CrossRefGoogle Scholar
  4. 4.
    Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of things for smart cities. IEEE Internet of Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  5. 5.
    Lakhno, V., Malyukov, V., Bochulia, T., Hipters, Z., Kwilinski, A., Tomashevska, O.: Model of managing of the procedure of mutual financial investing in information technologies and smart city systems. Int. J. Civil Eng. Technol. 9(8), 1802–1812 (2018)Google Scholar
  6. 6.
    Paroutis, S., Bennett, M., Heracleous, L.: A strategic view on smart city technology: the case of IBM Smarter Cities during a recession. Technol. Forecast. Soc. Change 89, 262–272 (2014)CrossRefGoogle Scholar
  7. 7.
    Hollands, R.G.: Critical interventions into the corporate smart city. Camb. J. Reg. Econ. Soc. 8(1), 61–77 (2015)CrossRefGoogle Scholar
  8. 8.
    Mithas, S., Tafti, A., Mitchell, W.: How a firm’s competitive environment and digital strategic posture influence digital business strategy. MIS Q. 37(2), 511–536 (2013)CrossRefGoogle Scholar
  9. 9.
    Tiwana, A., Ramesh, B.: E-services: problems, opportunities, and digital platforms. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, pp. 8–pp. IEEE, January 2001Google Scholar
  10. 10.
    Mazzarol, T.: SMEs engagement with e-commerce, e-business and e-marketing. Small Enterp. Res. 22(1), 79–90 (2015)CrossRefGoogle Scholar
  11. 11.
    Solanas, A., et al.: Smart health: a context-aware health paradigm within smart cities. IEEE Commun. Mag. 52(8), 74–81 (2014)CrossRefGoogle Scholar
  12. 12.
    Mohammadzadeh, A.K., Ghafoori, S., Mohammadian, A., Mohammadkazemi, R., Mahbanooei, B., Ghasemi, R.: A Fuzzy Analytic Network Process (FANP) approach for prioritizing internet of things challenges in Iran. Technol. Soc. 53, 124–134 (2018)CrossRefGoogle Scholar
  13. 13.
    Selçuk, A.L.P., Özkan, T.K.: Job choice with multi-criteria decision making approach in a fuzzy environment. Int. Rev. Manag. Mark. 5(3), 165–172 (2015)Google Scholar
  14. 14.
    Kache, F., Seuring, S.: Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. Int. J. Oper. Prod. Manag. 37(1), 10–36 (2017)CrossRefGoogle Scholar
  15. 15.
    Akhmetov, B.B., Lakhno, V.A., Akhmetov, B.S., Malyukov, V.P.: The choice of protection strategies during the bilinear quality game on cyber security financing. Bull. Natl. Acad. Sci. Repub. Kazakhstan 3, 6–14 (2018)Google Scholar
  16. 16.
    Lakhno, V., Malyukov, V., Gerasymchuk, N., et al.: Development of the decision making support system to control a procedure of financial investment. Eastern-Eur. J. Enterp. Technol. 6(3), 24–41 (2017)Google Scholar
  17. 17.
    Smit, H.T., Trigeorgis, L.: Flexibility and games in strategic investment (2015)Google Scholar
  18. 18.
    Arasteh, A.: Considering the investment decisions with real options games approach. Renew. Sustain. Energy Rev. 72, 1282–1294 (2017)CrossRefGoogle Scholar
  19. 19.
    Gottschlich, J., Hinz, O.: A decision support system for stock investment recommendations using collective wisdom. Decis. Support Syst. 59, 52–62 (2014)CrossRefGoogle Scholar
  20. 20.
    Strantzali, E., Aravossis, K.: Decision making in renewable energy investments: a review. Renew. Sustain. Energy Rev. 55, 885–898 (2016)CrossRefGoogle Scholar
  21. 21.
    Lakhno, V., Malyukov, V., Parkhuts, L., Buriachok, V., Satzhanov, B., Tabylov, A.: Funding model for port information system cyber security facilities with incomplete hacker information available. J. Theor. Appl. Inf. Technol. 96(13), 4215–4225 (2018)Google Scholar
  22. 22.
    Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., Vilajosana, X.: Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Commun. Mag. 51(6), 128–134 (2013)CrossRefGoogle Scholar
  23. 23.
    Akhmetov, B., Balgabayeva, L., et al.: Mobile platform for decision support system during mutual continuous investment in technology for smart city. Stud. Syst. Decis. Control 199, 731–742 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Information Security, Computer Science and CommunicationNational University of Life and Environmental Sciences of UkraineKyivUkraine
  2. 2.Faculty of Computer Systems and CommunicationsNational University of Life and Environmental Sciences of UkraineKyivUkraine
  3. 3.Faculty of Accounting, Audit and Information TechnologiesKyiv National University of Trade and EconomicsKyivUkraine
  4. 4.Department of Information TechnologyKyiv National University of Construction and ArchitectureKyivUkraine

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