The selection of an appropriate blockchain platform is a critical and non-trivial task in the design of central bank digital currency (CBDC) systems, as such platforms must simultaneously satisfy stringent technical, regulatory, and operational requirements under conditions of substantial uncertainty. Conventional multi-criteria group decision making (MCGDM) models are often inadequate for this task due to their limited ability to represent expert hesitation, conflicting assessments, and nonlinear uncertainty structures. To address these challenges, this study proposes a novel group decision making (DM) framework based on interval valued fractional orthopair fuzzy sets (IVFOFS). The proposed framework integrates the analytic hierarchy process to determine expert importance, an entropy-based approach to derive objective criteria weights, and a technique for order preference by similarity to ideal solution (TOPSIS) based ranking mechanism developed within the IVFOFS environment. The decision problem is structured around a panel of domain experts evaluating fifteen candidate blockchain platforms across criteria encompassing performance, security, governance, scalability, interoperability, compliance capability, and long-term sustainability. The interval valued fractional orthopair fuzzy representation enables a more flexible and expressive modeling of expert judgments, allowing both uncertainty intensity and hesitation to be captured simultaneously. The resulting rankings exhibit strong stability and discrimination power under varying parameter settings, supporting reliable decision outcomes. The proposed framework provides central banks and policy institutions with a rigorous, transparent, and uncertainty aware decision support tool for CBDC infrastructure selection, contributing to more informed and resilient digital currency system design.
Khan et al. (Tue,) studied this question.