A multi-criteria decision making method based on DNMA and CRITIC with linguistic D numbers for blockchain platform evaluation

Since more and more blockchain platforms have been utilized in diverse business applications, the blockchain platform evaluation becomes significant for clients. There are challenges regarding the blockchain platform evaluation in terms of information uncertainty, multiple types of criteria, and the correlations between criteria. This study dedicates to proposing a method to solve these problems by integrating linguistic D numbers (LDNs), double normalization-based multiple aggregation (DNMA) method, and Criteria Importance Through Inter-criteria Correlation (CRITIC) method. Firstly, a conversion rule of LDNs is introduced to enhance the comparative rule of LDNs. Then, an integrated multiple criteria decision making framework is proposed by incorporating DNMA with LDNs. This method not only can effectively capture the incomplete or uncertain decision-making information with respect to cost, benefit, and target criteria, but also can reduce the loss of decision information caused by single normalized technology. The CRITIC method is integrated in the LDN-based DNMA method to reflect the correlations between criteria in the blockchain platform evaluation process. To investigate the efficiency of the proposed method, a numerical example of blockchain platform evaluation is given. The sensitivity analysis demonstrates the robustness and stability of the developed method. The comparative analysis shows that our method can identify the potentially important criteria in the decision-making process effectively.

Automated summary

This study proposes a comprehensive method to address challenges in blockchain platform evaluation, including information uncertainty, multiple criteria types, and inter-criteria correlations. By integrating LDNs, DNMA, and CRITIC methods, the proposed approach effectively captures decision-making information, reduces information loss, and identifies important criteria in the decision-making process.