Blueprinting Sovereignty: A Reflex Analytics Paradigm for Adaptive Scenario Governance in the Turkish Defence Sector
DOI:
https://doi.org/10.65834/jdsi.11.7Keywords:
decision-making, digital governance, resilience, innovation, memory, reflex analyticsAbstract
In response to the growing complexity of public administration and defence governance, this study develops and evaluates an integrated reflex analytics platform designed to support sovereign, data-driven decision-making in the Turkish public sector. The platform combines multi-domain data fusion, modular artificial intelligence components, and dynamic scenario generation to analyze policy alternatives across economic, social, institutional, and risk dimensions and to quantify decision reflex through a novel Strategic Reflex Coefficient (SRC) metric. Within a virtualized ecosystem comprising 24 public sector organizations representing core layers of national governance—including ministries, regulatory bodies, regional authorities, and critical infrastructure operators—and 96 policy scenario streams spanning economic shocks, cyber incidents, supply chain disruptions, institutional reforms, and cross-sector crises, simulation results indicate an average 39.2% reduction in decision-making cycle time, a 27.1% increase in decision confidence under risk, a 35.8% improvement in alignment with stakeholder priorities, and a 45.6% enhancement in the traceability of institutional choices, while unresolved policy states in crisis-like conditions are reduced by 64.2%. These effects are derived from Monte Carlo analysis with repeated replications for each scenario type, and bootstrap-based 95% confidence intervals for the reported percentage improvements remain strictly positive, indicating that the gains are statistically robust rather than artefacts of a small number of favorable runs. Overall, the findings suggest that reflex analytics architectures of this kind can provide a transparent and reproducible framework for scenario-based governance, offering measurable gains in speed, robustness, and institutional learning, while also highlighting the need for further empirical validation in live public sector environments.
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