Purpose Traditional social indices often fail to fully assess the state of society in the context of modern digital transformation. This poses challenges for governance and decision-making in the context of a digital state. To address this issue, we propose an Artificial Intelligence (AI) – based weighting system that combines different international indices and optimization methods into a single model to enhance the precision of societal assessment. Design/methodology/approach We propose the SCS-SWBI Model based on a two-tier calculation framework: first, an individual Social Credit Score (SCS) is determined, followed by a collective Social Well-Being Index (SWBI). The model's structural weights (α, β, ω, and γ) are optimized using a hybrid application of Gradient Descent and Reinforcement Learning. Findings A simulated synthetic population of 1,000 profiles was used in the analysis. The study's result (social system equilibrium point: Vmean = 0.755) demonstrates that the SCS-SWBI Model can provide a rapid assessment of socioeconomic processes, monitoring of societal well-being, and policy impact in real time. Research limitations/implications Although the study uses reliable synthetic variables, the findings need to be further validated using large-scale, real-world empirical data. Practical implications The SCS-SWBI Model provides government agencies with a strategic tool for conducting real-time assessments of socio-economic processes. Originality/value The originality lies in the hybrid application of Gradient Descent and Reinforcement Learning to dynamically adjust societal weights, providing a more accurate reflection of the non-linear relationship between individual behavior and macro-level social progress than traditional statistical models.
Irada Alakbarova (Wed,) studied this question.