Co-integrating intelligent Resource Allocation System and Economic Policy Planning: Use Cases in Uzbekistan
Annotatsiya
The role of intelligent decision support systems for the optimization of resource allocation in economic planning has received increasing attention by regional economic policy-makers. Public authorities are increasingly implementing data-driven and multi-criteria approaches to enhance fiscal coordination. However, integrating a predictive analytics functionality into such planning architectures remains a challenge since the quantification of subjective expert input in these frameworks is difficult. In this study, we propose a hybrid analytical framework to provide a systematic basis in assessing whether a resource allocation model offers a reliable way of delivering policy recommendations. In this context, a novel ranking aggregation process and structural evaluation model were developed for direct comparison of allocation priorities on policy objectives, and a multi-level mapping was realized. By collecting performance indicators and expert evaluations from sectoral domains, i.e., transport, energy, and agriculture, we derive priority scores that represent the dual perspectives contained in stakeholder assessments: one is the urgency of intervention areas and the other is the strategic weight given by decision agents; then we develop an evaluation matrix using the TOPSIS method. Evidence is found that institutional capacity and data accessibility both support the effectiveness of policy formulation, although strong institutional linkages are especially important. For the interaction between allocation metrics and policy targets, differences in the criteria weights and sectoral priority levels significantly impacted allocation outcomes. The proposed model enhances the potential of the Uzbekistan-specific framework to be applied in high impact national planning initiatives. The results shed new analytical insight on the dynamics of intelligent planning systems and thus have interesting policy and methodological implications.