Portfolio Management of Military Capabilities Development Using Bayesian Decision-Making Approach
Abstract
Purpose. Development and substantiation of a methodological approach to enhancing the efficiency of troops (forces) capability development portfolio management by integrating bayesian decision theory into the evaluation and selection of alternatives.
Method. The research is based on the integration of portfolio management (PM²-PfM) with Bayesian decision theory to improve the validity of the selection of capacity development projects under uncertainty.
Findings. This study develops an integrated approach to portfolio management of military capability development, combining multi-criteria decision analysis (MCDA) with a Bayesian decision-making framework.
Theoretical implications. Development of a methodological framework for portfolio management of military capability development by integrating deterministic and probabilistic decision-making models.
Practical implications. The proposed approach enables more effective decision-making in military capability development under uncertainty and resource constraints.
Value. The integration of the capability development portfolio management methodology (PM²-PfM) with Bayesian decision theory enables a transition from traditional deterministic approaches to a probabilistic framework for evaluating decision alternatives.
Future research. Future research will focus on developing and evaluating approaches to implementing portfolio management within the Ministry of Defence of Ukraine.
Paper type. Theoretical.
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References
Davis P. K., Shaver R. D., Beck J. L. Portfolio Analysis Methods for Assessing Capability Options. Santa Monica : RAND Corporation, 2008. 98 p. URL: https://www.rand.org/pubs/monographs/MG662.html (Retrieved: 12.02.2026).
European Commission. PM² Portfolio Management Guide (PM²-PfM). Luxembourg : Publications Office of the European Union, 2021. URL: https://commission.europa.eu/system/files/2021-10/pm2-portfolio-management-guide_en.pdf (Retrieved: 03.03.2026).
Strube M. J., Loren M. Capability-Based Planning: The Link between Strategy and Force Structure. Santa Monica : RAND Corporation, 2011. URL: https://www.rand.org/pubs/research_reports/RR113.html (Retrieved: 18.02.2026).
Shafi K., Abbass H. A., Deb K. Multi-objective optimization for capability planning under uncertainty. Expert Systems with Applications. 2017. Vol. 80. P. 38–50. DOI: https://doi.org/10.1016/j.eswa.2017.02.042 (Retrieved: 27.02.2026).
Filinkov A., Dortmans P. J. Capability Portfolio Planning: The Australian Experience. Defence Science and Technology Organisation, 2014. URL: https://apps.dtic.mil/sti/pdfs/ADA602609.pdf (Retrieved: 09.03.2026).
Tagarev T., Ivanova P. Analytical Support to Defence Transformation. Information & Security. 2008. Vol. 23. P. 31–45. URL: https://procon.bg/article/analytical-support-defence-transformation (Retrieved: 21.02.2026).
Davendralingam N., DeLaurentis D. A. Systems-of-Systems Portfolio Management: A Value-Based Approach. Procedia Computer Science. 2014. Vol. 28. P. 491–500. DOI: https://doi.org/10.1016/j.procs.2014.03.059 (Retrieved: 05.03.2026).
Henshaw M. та ін. Portfolio, Programme and Project Management in Defence. Defence Academy of the United Kingdom, 2012. URL: https://www.da.mod.uk/research (Retrieved: 14.02.2026).
Parnell G. S., Driscoll P. J., Henderson D. L. Decision Making in Systems Engineering and Management. 2nd ed. Hoboken : Wiley, 2013. 544 p. URL: https://www.wiley.com/en-us/Decision+Making+in+Systems+Engineering+and+Management-p-9781118505366 (Retrieved: 02.03.2026).
Saaty T. L. Decision making with the analytic hierarchy process. International Journal of Services Sciences. 2008. Vol. 1(1). P. 83–98. DOI: https://doi.org/10.1504/IJSSCI.2008.017590 (Retrieved: 25.02.2026).
Berger J. O. Statistical Decision Theory and Bayesian Analysis. 2nd ed. New York : Springer, 1985. 618 p. DOI: https://doi.org/10.1007/978-1-4757-4286-2 (Retrieved: 07.03.2026).
Bernardo J. M., Smith A. F. M. Bayesian Theory. Chichester : Wiley, 1994. 586 p. URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470317105 (Retrieved: 16.02.2026).
Gelman A., Carlin J. B., Stern H. S., Dunson D. B., Vehtari A., Rubin D. B. Bayesian Data Analysis. 3rd ed. Boca Raton : CRC Press, 2013. URL: https://sites.stat.columbia.edu/gelman/book/BDA3.pdf (Retrieved: 11.03.2026).
Robert C. P. The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation. New York : Springer, 2007. URL: https://www.amazon.com/Bayesian-Choice-Decision-Theoretic-Computational-Implementation/dp/0387715983 (Retrieved: 19.02.2026).
French S., Insua D. R., Ruggeri F. Bayesian Decision Analysis. Cambridge : Cambridge University Press, 2012. URL: https://www.cambridge.org/core/books/bayesian-decision-analysis (Retrieved: 06.03.2026).
DeGroot M. H. Optimal Statistical Decisions. New York : McGraw-Hill, 1970. URL: https://en.wikipedia.org/wiki/Morris_H._DeGroot (Retrieved: 28.02.2026).
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