Abstract
A Science City (SC) is an urban development vision of a Smart City that includes a powerful city-forming scientific-industrial complex in an advanced fashion with a view to managing a city’s assets and human capital. Therefore, the strategic planning of the SC’s socioeconomic development requires as special scientific-industrial and cultural marketing analysis involving citizens in strategic processes (civic participation). Certain factors should be considered, such as the fact that SC strategic planning must be coordinated with the federal government and regional authorities, as citizens are demanding more accountability from the government and it is necessary to create conditions for networked group self-organization. So, SC strategic planning has become a transparent networked multilevel decision-making process where marketing analysis and civic participation are the challenge for Smart City strategic planning methodology. Under such conditions this paper aims to develop the author’s convergent approach (CA), which would help to accelerate the networked strategic meetings by applying Situational Center, Cognitive Modeling, Genetic Algorithm, and Big Data analysis technology for the verification of cognitive models. This study is an attempt to address the issue of applying CA for accelerating SC strategic planning with civic participation and marketing analysis. To portray the issue of scientific-industrial complex and cultural marketing analysis in these terms, Quality Function Deployment was used. This ground was tested in the SC strategic planning process in the SCs of Korolev and Fryazino, Moscow Region, Russia. It was shown that the CA helps to speed up the traditional strategic planning significantly.
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References
Bisello, A., Vettorato, D., Stephen, R., Elisei, P.: Smart and Sustainable Planning for Cities and Regions: Results of SSPCR. Springer International Publishing, Cham (2017). doi:10.1007/978-3-319-44899-2
Resin, V.I., Popkov, Y.S.: Urban Development in Transitional Economy. URSS, Moscow (2001)
Krogerus, M., Tschappäler, R.: The Decision Book. Fifty Models for Strategic Thinking. Profile Book Ltd, London (2008)
Mintzberg, H., Ahlstrand, B., Lampel, J.: Strategy Safari: A Guided Tour Through the Wilds of Strategic Management. Financial Times Prentice Hall, London (2009)
Ohmae, K.: The Mind of the Strategist. The Art of the Japanese Business. Tata McGraw-Hill Publishing Company Limited, New Delhi (2004)
Larichev, O., Moshkovich, H.: Verbal Decision Analysis for Unstructured Problems. Kluwer Academic Publishers, Boston (1997)
Saaty, T.: The analytic hierarchy process: planning, priority setting, resource allocation. University of Pittsburg, Mervis Hall (1988)
Gubanov, D., Korgin, N., Novikov, D., Raikov, A.: E-expertise: Modern Collective Intelligence. SCI, vol. 558. Springer, Cham (2014). doi:10.1007/978-3-319-06770-4
Simon, J., Bass, T., Boelman, V., Mulgan, G.: Digital democracy: the tools transforming political engagement. NESTA, UK, England and Wales 1144091 and Scotland SC042833 (2017)
Raikov, A.N., Panfilov, S.A.: Convergent decision support system with genetic algorithms and cognitive simulation. In: Proceedings of the IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2013, Saint Petersburg, Russia, 19–21 June, pp. 1142–1147 (2013). doi:10.3182/20130619-3-RU-3018.00404
Ivanov, V.K.: Incorrect problems in topological spaces. Siberian Math. J. 10(5), 785–791 (1969)
Raikov, A.N., Avdeeva, Z., Ermakov, A.: A big data refining on the base of cognitive modeling. In: 1st IFAC Conference on Cyber-Physical & Human-Systems, Florianopolis, Brazil. 7–9 December, pp. 147–152 (2016). doi:10.1016/j.ifacol.2016.12.205
Perkins, D.: The Eureka Effect. The Art and Logic of Breakthrough Thinking. W.W. Norton & Company, London (2000)
Sutherland, J.: Scrum: The Art of Doing Twice the Work in Half the Time. Crown Business, NY (2014)
Raikov, A.N.: Organizational structure optimization with the questions–criteria hierarchy. In: IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2016, 28–30 June 2016, Troyes, France, pp. 1590–1595, (2016). doi:10.1016/j.ifacol.2016.07.797
The socio-economic development strategy of the Science City Fryazino (in Russian). http://www.naukograd-fryazino.ru/assets/files/Strategy/Strategiya_30_05.docx
Acknowledgments
This work is partly funded by: the Russian Science Foundation, grant 17-18-01326, “Development of socio-humanitarian technologies for a distributed situational center system in Russia based on self-developing polysubject environment methodology”; Russian Foundation for Basic Research, grant 13-07-13165, “Information-analytical system for decision-making support based on the analysis of Big Data in a network of distributed situational centers and cloud computing environments with heterogeneous computing resources.”
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Raikov, A.N. (2017). Strategic Planning of Science City Socioeconomic Development. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2017. Communications in Computer and Information Science, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-69784-0_25
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DOI: https://doi.org/10.1007/978-3-319-69784-0_25
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