Network centrality drives optimal protection investment against systemic risk propagation in complex systems

The aim of this study is to investigate the mechanisms underlying systemic risk mitigation in scale-free networks by modeling the roles of memorized capital, social learning, and centrality-based heuristics. The study employs a network-agent dynamic approach to examine how node centrality shapes protection decisions and vulnerability distributions. Using advanced computational methods and interactive simulations, the study systematically tracks key state variables and shows that nodes with higher centrality tend to invest more substantially in protection, indicating a positive relationship between centrality and proactive risk management. These findings provide new insights into risk propagation and highlight that local decision rules may converge to suboptimal equilibria when left uncoordinated. By demonstrating the critical role of strategic collaboration and regulatory oversight, the results outline potential pathways toward enhanced network resilience, offering both theoretical and practical contributions to systemic risk mitigation across interconnected domains.