Modelling budgetary decision utility and institutional risk: Evidence from the Portuguese Navy

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Type of the article: Research Article

Abstract
Budgetary decision-making in the defense sector involves making trade-offs between interdependent capabilities, subject to financial, operational and institutional constraints. The analysis of aggregate expenditure is insufficient to evaluate these decisions, as the impact of the allocated resources depends on their composition and the interplay between categories of expenditure. The study analyzes the budgetary headings of the Portuguese Navy between 2023 and 2025 and assesses their compatibility with an institutional utility function. To this end, possible adjusted structural model has been proposed, which includes the complementarity between maintenance and investment, the adequacy of personnel expenditure, potential performance and institutional stability. The robustness of the optimization was assessed through 200,000 Monte Carlo simulations, with simultaneous variation of parameters, thresholds and weights. Total expenditure increased by 20.66 per cent over the period, but the highest utility was achieved in 2024 (0.691554), followed by 2025 (0.683443) and 2023 (0.659761). The 2024 financial year ranked first in 91.64% of the simulations and outperformed 2023 in all configurations analyzed. However, alternative normalization of the maintenance-investment component altered the ranking to 2023 > 2024 > 2025, thereby distinguishing the model’s parametric robustness from its structural robustness. The model enables to explain budgetary commitments and test the stability of decisions, but it does not constitute a causal measure of operational readiness, efficiency or institutional risk. The model’s relevance lies in the transparent support it provides for comparing the various allocation alternatives.

Acknowledgment
This article is financed by CINAV–Navy Research Centre [CINAV–Centro de Investigação Naval].

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    • Figure 1. Monte Carlo utility distributions by budget year
    • Figure 2. Frequency of first place across Monte Carlo simulations
    • Figure 3. Frequency of complete ranking orders
    • Figure 4. Empirical cumulative distributions of pairwise utility differences
    • Figure 5. Distribution of U2025 − U2024
    • Figure 6. Sensitivity of utility levels to the specification of the maintenance-investment component
    • Table 1. Changes in the budget breakdown
    • Table 2. Utility components in the baseline configuration
    • Table 3. Results of applying the Monte Carlo model with 200,000 simulations
    • Table A1. Integrated evidence on institutional utility, ranking stability and model sensitivity
    • Conceptualization
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    • Formal Analysis
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