A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches

The approach of evaluating the fnal scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to fnancial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on diferent data structures more comprehensively. Moreover, not only the fnal MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to afect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have diferent data structures, were calculated over ten diferent periodic decision matrices. Ten diferent crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with diferent capabilities were used to better visualize the big picture. The relationships between two diferent real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identifed using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min-max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the "Maximum normalization-based fuzzy integrated CODAS procedure" was proposed to decision-makers to measure the economic performance of the countries.