A decision making algorithm for economic growth in the digital economy using CRITIC WASPAS based circular picture fuzzy information

In this study, we explore the use of fuzzy set (FS) theory to address uncertainty in evaluating strategies that drive economic growth within the digital economy. Specifically, we employ a circular picture fuzzy (CPF) set (CPFS). As economies become increasingly digital, decision-makers face complex challenges in selecting the most effective strategies for growth, ranging from digital infrastructure investment to innovation support and policy reform. To assess these strategic options, we consider five alternatives, each evaluated against five key attributes. To ensure objectivity in weighing these attributes, we first apply the CRITIC (Criteria Importance Through Intercriteria Correlation) method, which calculates weights based on the contrast intensity and interdependencies among the attributes. Next, we use the WASPAS (Weighted Aggregated Sum Product Assessment) method to rank the alternatives. Furthermore, we develop a decision-making algorithm tailored for multi-attribute group decision-making (MAGDM), integrating the CRITIC-WASPAS approach within the CPFS framework. This model is applied to a hypothetical case study focused on digital economy growth strategies, demonstrating its practical utility in uncertain and complex decision environments. After the evaluation, the second alternative, E-Governance and Digital Policy Reform, emerged as the most optimal strategy for promoting economic growth in the digital economy. To validate the robustness of the proposed approach, we conduct a sensitivity analysis and a comparative evaluation with existing methods. The results confirm that our model provides reliable and effective support for strategic decision-making aimed at fostering economic growth in the digital age.