Integrated System-Dynamics and Multi-Objective Optimization of Sustainable Development Goal Interdependencies and Cross-Sector Policy Interventions

Abstract

The Sustainable Development Goals (SDGs) form an indivisible agenda, yet policy design and implementation remain dominated by sector-specific programmes that can generate unrecognized spillovers, delayed effects and burden shifting. This study develops a dynamic systems-of-systems (SoS) decision-support framework that represents all 17 SDGs as operationally distinct but interdependent constituent systems. The framework integrates a signed and directed interaction matrix, causal-loop analysis, stock-flow system dynamics, seven cross-sector policy levers, constrained multi-objective optimization and Monte Carlo uncertainty analysis. A transparent proof-of-concept application uses normalized goal-level states and literature-informed interaction directions to compare business-as-usual, siloed growth, green-transition, humandevelopment and optimized SoS portfolios over 2025-2035. The optimized portfolio increases the mean normalized SDG score from 0.601 under business-as-usual to 0.710 by 2030 and from 0.693 to 0.835 by 2035. It also raises the weakest-goal score from 0.452 to 0.631 by 2030 and from 0.512 to 0.753 by 2035. In contrast, the siloed-growth scenario improves the aggregate score but depresses the weakest-goal and environmental outcomes, demonstrating that aggregate progress can conceal systemic deterioration. Network out-strength identifies SDGs 16, 4, 17, 13 and 5 as high-leverage goals. Across 1,000 uncertain parameter realizations, the optimized portfolio retains a narrow 2035 mean-score interval of 0.829-0.840. The numerical results are illustrative rather than forecasts, but they demonstrate how systems engineering can convert qualitative SDG interlinkage knowledge into an auditable, adaptive and optimization-ready policy architecture.