The approach of evaluating the final 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 financial 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 different data structures more comprehensively. Moreover, not only the final 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 affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different 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 identified 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.

中文翻译：

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经济数据的综合 MCDM 评估：最大标准化、CODAS 和模糊方法的成功分析

根据与现实生活排名的关联强度来评估多标准决策（MCDM）方法的最终分数的方法对于比较 MCDM 方法很有趣。这种方法最近主要应用于财务数据。在这些研究中，强调某些方法显示出更稳定的成功，通过更全面地测试不同数据结构上的方法来查看将出现的结果将是有用的。此外，不仅可以使用相同的方法比较最终的 MCDM 结果，还可以比较作为 MCDM 组成部分的标准化技术和数据类型（模糊或清晰）的性能。这些成分也有可能直接影响 MCDM 结果。在这个方向上，我们的研究通过十个不同的周期决策矩阵计算了具有不同数据结构的二十国集团（G-20）国家的经济表现。使用具有不同功能的十种不同的基于清晰的 MCDM 方法（COPRAS、CODAS、MOORA、TOPSIS、MABAC、VIKOR（S、R、Q）、FUCA 和 ELECTRE III）来更好地可视化全局。两个不同的现实生活参考锚点和MCDM方法之间的关系被用作比较的基础。CODAS 方法在大多数时期都与两个锚点建立了高度相关性。使用这两个锚点确定了最合适的 CODAS 标准化技术。有趣的是，最大归一化技术是替代方案（最大、最小-最大、向量、总和和基于替代排名）中最成功的。此外，我们通过比较基于清晰和基于模糊的 CODAS 的相关结果来比较两种主要数据类型。结果非常一致，并向决策者提出了“基于最大归一化的模糊综合CODAS程序”来衡量各国的经济表现。