<![CDATA[This research develops a dynamic decision-making model for regional governance based on adaptive preference learning to address the limitations of traditional static policy frameworks. The study integrates decision theory, reinforcement learning, Bayesian preference modeling, and multi-criteria decision-making (MCDM) into a unified system capable of capturing evolving stakeholder preferences and responding to rapidly changing socio-economic conditions. The model consists of four core components data input layer, preference learning engine, policy decision module, and real-time feedback system which collectively enable continuous updating of decision parameters and ongoing evaluation of policy outcomes. Using a mixed-method approach that combines stakeholder surveys, historical governance data, performance indicators, and computational simulations, the study demonstrates that the adaptive model significantly improves decision accuracy, responsiveness, and alignment with citizen needs. The system’s dynamic feedback loops allow policies to be refined in real time, enhancing predictive capability and reducing the risks associated with rigid or outdated policy assumptions. Results show that the model outperforms traditional governance approaches in terms of decision efficiency, data-driven fairness, and the ability to anticipate emerging issues. Although challenges remain such as data sparsity, computational complexity, infrastructure limitations, and potential resistance from policymakers the findings highlight the model’s practical value for modern regional governance. The research contributes theoretically by advancing the application of adaptive learning in public policy decision-making and practically by offering a framework that supports faster, smarter, and more citizen-centric governance. Overall, the study underscores the potential of adaptive preference learning to transform regional decision-making in increasingly complex and uncertain environments.]]>
