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Compartmental market models in the digital economy—extension of the Bass model to complex economic systems

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  • Øverby, Harald
  • Audestad, Jan A.
  • Szalkowski, Gabriel Andy

Abstract

Compartmental models are widely used in epidemiology, engineering, and physics to describe the temporal behavior of complex systems. This paper presents how compartmental models may be applied to the digital economy—more specifically, how the Bass model can be extended to more complex economics systems such as markets with customer churning, competition, multisided platforms, and online games. It is demonstrated that it is straightforward to establish the equations describing the various economic systems under study, however, the equations are often too complex to be solved analytically in the general case. Though the paper presents simple and idealized cases, the solutions may, nevertheless, uncover important strategic aspects that otherwise may be hidden by complexity in the general case, for example, the reasons for slow initial market growth. The paper also discusses how the developed models may be used to evaluate digital economic market evolution and business policy.

Suggested Citation

  • Øverby, Harald & Audestad, Jan A. & Szalkowski, Gabriel Andy, 2023. "Compartmental market models in the digital economy—extension of the Bass model to complex economic systems," Telecommunications Policy, Elsevier, vol. 47(1).
  • Handle: RePEc:eee:telpol:v:47:y:2023:i:1:s0308596122001434
    DOI: 10.1016/j.telpol.2022.102441
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    References listed on IDEAS

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    1. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    2. Zhongzhi Yang & Pengzhi Kong & Boying Li & Bo Chao, 2019. "A compartment model and numerical analysis of circulatory economy," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 88-105, January.
    3. Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    4. Jan A. Audestad, 2015. "Some Dynamic Market Models," Papers 1511.07203, arXiv.org.
    5. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Vahideh Manshadi & Sidhant Misra & Scott Rodilitz, 2020. "Diffusion in Random Networks: Impact of Degree Distribution," Operations Research, INFORMS, vol. 68(6), pages 1722-1741, November.
    8. Fabio Tramontana, 2010. "Economics as a compartmental system: a simple macroeconomic example," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 57(4), pages 347-360, December.
    9. Alexandros Leontitsis & Abiola Senok & Alawi Alsheikh-Ali & Younus Al Nasser & Tom Loney & Aamena Alshamsi, 2021. "SEAHIR: A Specialized Compartmental Model for COVID-19," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
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    Cited by:

    1. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Giovanni Modanese, 2023. "The Network Bass Model with Behavioral Compartments," Stats, MDPI, vol. 6(2), pages 1-13, March.

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