MODELING THE STRATEGIC PROTECTION OF THE NATIONAL ECONOMY UNDER DIGITAL TRANSFORMATION USING THE MONTE CARLO METHOD
DOI:
https://doi.org/10.32999/ksu2307-8030/2026-60-13Keywords:
Monte Carlo method, strategic protection of the national economy, digital transformation, integrated index, probabilistic modelling, digital sector, public financing, digital infrastructureAbstract
The relevance of the study is determined by the growing role of digital transformation in maintaining the continuity, adaptability, and long-term capacity of the national economy under conditions of war, infrastructure disruption, fiscal constraints, unstable investment flows, and increasing technological dependence. In such an environment, the strategic protection of the national economy cannot be assessed solely through fixed values of macroeconomic indicators, because the parameters of digital development may change considerably under the influence of external shocks, resource shortages, policy decisions, and structural shifts. The article discusses the methodological problem of evaluating the strategic protection of Ukraine’s national economy when the scale of the digital sector, public financing of digital transformation, and the availability of digital infrastructure are subject to uncertainty. Particular attention is paid to the choice of indicators that can reflect the economic, financial, and resource dimensions of digital transformation in a comparable form. The discussion covers the use of computer services exports as a measure of the digital sector’s economic capacity, government expenditure on digital transformation as an indicator of public support for technological change, and Internet penetration as a proxy for the accessibility of digital infrastructure. The paper considers the limitations of deterministic assessment, in which each variable is represented by a single value, and explains the advantages of a probabilistic approach for strategic analysis. The Monte Carlo method is examined as a tool for generating a wide range of plausible combinations of input parameters and for describing uncertainty through distributions rather than isolated point estimates. The article also addresses the formation of pessimistic, baseline, and optimistic scenarios, the justification of triangular distributions in the presence of short statistical series, the normalization of heterogeneous indicators, and the construction of an integrated index based on weighted variables. Separate consideration is given to the interpretation of different levels of strategic protection, the assessment of the probability of unfavorable and target scenarios, and the use of sensitivity analysis to identify the factors that may require priority attention from public authorities. The proposed discussion is intended to support the development of a transparent analytical framework for scenario planning, monitoring digital transformation, and substantiating decisions aimed at strengthening the protection of the national economy.
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3. Andrle, M., & Hunt, B. L. (2020). Model-based globally-consistent risk assessment (IMF Working Paper no. 2020/064 p. 28). International Monetary Fund. DOI: https://doi.org/10.5089/9781513536460.001
4. Copestake, A., Estefania-Flores, J., & Furceri, D. (2024). Digitalization and resilience. Research Policy, no. 53(3), 104948. DOI: https://doi.org/10.1016/j.respol.2023.104948
5. Ji, Z., & Huang, Y. (2024). Does digital transformation promote economic resilience? Urban-level evidence from China. Heliyon, no. 10(4), e26461. DOI: https://doi.org/10.1016/j.heliyon.2024.e26461
6. OECD. (2022). Administrative service delivery during war time. OECD Publishing. DOI: https://doi.org/10.1787/23d5a973-en
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