Decision-Making Models in Smart Policymaking
Keywords:
Adaptive decision-making, smart policymaking, data-driven governance, institutional learning, continuous feedback, artificial intelligenceAbstract
Objective: This study aims to explore the conceptual foundations, theoretical frameworks, components, and outcomes of adaptive decision-making models in smart policymaking, emphasizing data-driven governance, continuous feedback, and institutional learning.
Methods and Materials: This qualitative review was conducted through a systematic analysis of the literature published between 2015 and 2025. Twelve peer-reviewed articles on adaptive decision-making, data-driven governance, and smart policymaking were selected based on relevance and quality. Thematic analysis was performed using NVivo 14 software through open, axial, and selective coding until theoretical saturation was reached.
Findings: The results revealed that adaptive decision-making models are structured around three key dimensions: theoretical foundations (complex systems theory and institutional learning), core components (data-driven decision-making, continuous feedback, institutional flexibility, and stakeholder participation), and practical outcomes (policy effectiveness, decision transparency, and institutional resilience). Integration of artificial intelligence, machine learning, and big data analytics enhances adaptive decision-making as a critical tool for digital governance.
Conclusion: Adaptive decision-making provides a dynamic and learning-oriented framework for smart policymaking, emphasizing responsiveness and continuous improvement. Its successful implementation requires robust data infrastructure, analytical capacity building among policymakers, and institutionalization of learning loops within governance systems.
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