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Adapting, Learning, and Control the Supply of a Vital Commodity Such as COVID-19 Vaccine

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Creativity in Intelligent Technologies and Data Science (CIT&DS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1448))

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Abstract

The article examines the problem of managing a democratic socio-economic system in the face of a shortage of a vital commodity (such as the COVID-19 vaccine). The citizens’ approval of the actions of the authorities to increase the production and supply of this product contributes to political stability. The possibilities of increasing the supply of a vital commodity depend on random factors. In the face of such uncertainty, in the age of artificial intelligence, the management of a socio-economic system can be based on machine learning and adaptation. In this case, it is necessary to take into account the activity of the elements of the system associated with the presence of their own goals, which do not necessarily coincide with the goal of the system as a whole. These elements can influence adaptation and machine learning procedures to achieve their goals. The research is carried out on a three-level model of a democratic socio-economic system. At its top level is a member of society - a citizen who evaluates the politician who is at the middle level of the system. In turn, the politician can influence the increase in the supply of a vital commodity, including both its purchase on the market and production at a local plant belonging to the lower level of the system. Political stability is guaranteed if the citizen regularly approves the actions of the politician to increase the supply of vital goods. But the plant’s management knows its own production potential better than the politician. Thus, this leadership can manipulate the volume of its own production in order to gain more support from the politician. A politician may also manipulate the opportunities available to him in order to achieve personal goals. To avoid manipulation of the supply of a vital product under conditions of uncertainty, a socio-economic management mechanism is proposed, including an economic and political mechanism. The economic mechanism includes a procedure for adaptive forecasting of the production of a vital commodity, as well as a procedure for supporting this production. The political mechanism includes a procedure for machine self-learning of a citizen, as well as a procedure for assessing the activity of a politician. Sufficient conditions for the synthesis of the optimal mechanism of socio-economic management are found, in which random opportunities to increase the supply of a vital commodity are fully used, including both purchases on the market and production at a local plant. An example of such a socio-economic mechanism is considered on the example of the supply of the COVID-19 vaccine to England.

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References

  1. Soros, G.: The Crisis of Global Capitalism. Little, Brown & Company, USA (1998)

    Google Scholar 

  2. Lipman-Blumen, J.: The Allure of Toxic Leaders. Oxford University Press, USA (2004)

    Google Scholar 

  3. Schultz, V.: Oligarchy, ontology, cycles, and change in a globalizing world. Sotsiologicheskie Issledovaniya 2, 3–15 (2009)

    Google Scholar 

  4. Boddy, C., Ladyshewsky, R., Galvin, P.: Leaders without ethics in global business: corporate psychopaths. J. Public Aff. 10, 121–138 (2010)

    Article  Google Scholar 

  5. Tsyganov, V.: Limits of global growth, stagnation, creativity and international stability. AI Soc. 29(1), 259–266 (2013). https://doi.org/10.1007/s00146-013-0483-x

    Article  Google Scholar 

  6. Tsyganov, V.: Socio-political stability, voter’s emotional expectations, and information management. AI Soc. (2020). https://doi.org/10.1007/s00146-020-01017-8

  7. Blanchet, M., Rinn, T., Thaden, G., Thieulloy, G.: Industrie 4.0 - the new industrial revolution. How Europe will succeed. Roland Berger Strategy, MĂĽnchen (2014)

    Google Scholar 

  8. Borodin, D., Gurlev, I., Klukvin, A.: Adaptive mechanisms for sustainable development. Syst. Sci. 30, 89–95 (2004)

    MathSciNet  MATH  Google Scholar 

  9. Tsyganov, V.: Learning mechanisms in digital control of large-scale industrial systems. In: Proceedings of the Global Smart Industry Conference, pp. 1–6. IEEE, Chelyabinsk (2018)

    Google Scholar 

  10. AI governance: a holistic approach to implement ethics into AI. White Paper. World Economic Forum Homepage. https://www.weforum.org. Accessed 12 Apr 2021

  11. Gill, K.S.: Prediction paradigm: the human price for instrumentalism. AI Soc 35, 3 (2020)

    Google Scholar 

  12. Creating a digital society. EU Homepage. https://ec.europa.eu/digital-single-market/en/creating-digital-society. Accessed 19 Oct 2020

  13. Tsyganov, V.: Intelligent information technologies in social safety. Commun. Comput. Inf. Sci. 1084, 270–284 (2019)

    Google Scholar 

  14. De Fine Licht, K., de Fine Licht, J.: Artificial intelligence, transparency, and public decision-making. AI Soc. 35(4), 917–926 (2020). https://doi.org/10.1007/s00146-020-00960-w

    Article  Google Scholar 

  15. De Laat, P.B.: Algorithmic decision-making based on machine learning from big data: can transparency restore accountability? Philos. Technol. 31, 525–541 (2018)

    Article  Google Scholar 

  16. Spiegelhalter, D.: Should we trust algorithms? Harv. Data Sci. Rev. 2(1), 1–12 (2020)

    MathSciNet  Google Scholar 

  17. Recht, B.: Reflections on the learning-to-control renaissance. In: Proceedings of the 21st IFAC World Congress, p. 4707. Elsevier, Berlin (2020)

    Google Scholar 

  18. Tsyganov, V.: Emotional expectations and social stability. IFAC-PapersOnLine 51(30), 112–117 (2018)

    Article  Google Scholar 

  19. Burkov, V., Gubko, M., Kondratiev, V., Korgin, N., Novikov, D.: Mechanism Synthesis and Management. NOVA Publishers, New York (2013)

    Google Scholar 

  20. Bagamaev, R., Gurlev, I.: Adaptive mechanism for mastering capital and improving international stability. IFAC-PapersOnLine 16, 42–45 (2005)

    Google Scholar 

  21. Arifovic, J., Ledyard, J.: A behavioral model for mechanism design: individual evolutionary learning. J. Econ. Behav. Organ. 78, 375–395 (2011)

    Article  Google Scholar 

  22. Kossiakoff, A., Sweet, W., Seymour, S., Biemer, S.: Systems Engineering: Principles and Practice. John Wiley, New York (2011)

    Book  Google Scholar 

  23. Dibrivniy, O., Onyshchenko, V., Grebenyuk, V.: Forecasting based on the trend model and adaptive Brown’s model. In: 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, pp. 944–947. Lviv University, Ukraine (2018)

    Google Scholar 

  24. Auster, S.: Asymmetric awareness and moral hazard. Games Econ. Behav. 82, 503–521 (2013)

    Article  MathSciNet  Google Scholar 

  25. Schultz, V.: Humanitarian high technologies in political system of society. Sotsiologicheskie Issledovaniya 8, 85–93 (2012)

    Google Scholar 

  26. Diallo, S.Y., Shults, F.L., Wildman, W.J.: Minding morality: ethical artificial societies for public policy modeling. AI Soc. 36(1), 49–57 (2020). https://doi.org/10.1007/s00146-020-01028-5

    Article  Google Scholar 

  27. Tsyganov, V.: Models of voters and the politicians in a digital society under uncertainty. IFAC PaperOnline 52(25), 275–280 (2019)

    Article  Google Scholar 

  28. Ahuja, S., Reddy, V., Marques, O.: Artificial intelligence and COVID-19: a multidisciplinary approach. Integr. Med. Res. 9, 100434 (2020)

    Article  Google Scholar 

  29. Naudé, W.: Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. 35(3), 761–765 (2020). https://doi.org/10.1007/s00146-020-00978-0

    Article  Google Scholar 

  30. Coronavirus (COVID-19) in the UK. GOV.UK Homepage. https://coronavirus.data.gov.uk/details/vaccinations. Accessed 12 Apr 2021

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Correspondence to Vladimir V. Tsyganov .

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Tsyganov, V.V. (2021). Adapting, Learning, and Control the Supply of a Vital Commodity Such as COVID-19 Vaccine. In: Kravets, A.G., Shcherbakov, M., Parygin, D., Groumpos, P.P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2021. Communications in Computer and Information Science, vol 1448. Springer, Cham. https://doi.org/10.1007/978-3-030-87034-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-87034-8_2

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