InterCriteria Decision Making Approach to EU Member States Competitiveness Analysis: Temporal and Threshold Analysis

Abstract

for the current 28 Member States of the European Union. The developed approach which employs the apparatuses of index matrices and intuitionistic fuzzy sets is designed to produce from an existing index matrix with multiobject multicriteria evaluations a new index matrix that contains intuitionistic fuzzy pairs with the correlations revealed to exist in between the set of evaluation criteria, which are not obligatory there 'by design' of the WEF's methodology but exist due to the integral, organic nature of economic data. Here, we analyse the data from the six-year period within a reasonably chosen intervals for the thresholds of the intuitionistic fuzzy functions of membership and non-membership, and make a series of observations about the current trends in the factors of competitiveness of the European Union. The whole research and the conclusions derived are in line with WEF's address to state policy makers to identify and strengthen the transformative forces that will drive future economic growth.

InterCriteria Analysis of a Model Parameters Identification using Genetic Algorithm

“…It is based on the apparatus of the Index Matrices (IM) [10], [11], and the Intuitionistic Fuzzy Sets [12] and can be applied for decision making in different areas of science and practice. The approach has been discussed in a several papers [10], [14], [15], [16]. In [8] a possibility of the ICA method for criterion value prediction, proposing two algorithms, is presented.…”

“…In [16] a discussion on the threshold values in the ICA was further elaborated. But, up to now, considering ICA application, the only applications reported are in one area: namely, EU member states competitiveness analysis [14], [15]. Encouraging results of these first applications of the ICA provoke us to use the method for establishing and identifying the relations between parameters of the mathematical model of an E. coli fedbatch cultivation process.…”

Interpretation in the Intuitionistic Fuzzy Triangle of the Results, Obtained by the InterCriteria Analysis

“…We are interested to compare the results here with those reported in [11], obtained with the proposed there algorithm for max-row-aggregation of the M μ and minrow-aggregation of the M ν , see Table 3 in [11]. Following the logic from that previous step of our research, we look at the first subtable for M μ and identify for the threshold α = 0.796 which are the individual topcorrelating criteria, six in number: '11', '12', '1', '9', '5' and '2', and the number of intercriteria pairs formed between them, 11 in number.…”

“…The progress in this leg of our research has been described in details consequently in [9,10,11,12]. In all these parts of the research, we have illustrated the approach with data from the World Economic Forum's annual Global Competitiveness Reports (GCRs), for the 28 EU member states from years 2008-2009 to 2013-2014, taking as a motivation the WEF's general address to policy makers to 'identify and strengthen the transformative forces that will drive future economic growth' [13].…”

“…This is worth noting, because in the previous steps of our research, the initial idea was to shortlist the individual criteria with respect firstly to one of the thresholds (usually the membership), and only then to proceed with respect to the other, using for this purpose first the max-row-aggregation operation of IMs [11] and, then, for comparison, the average-row-aggregation operation [12]. Now, we may prefer to work with this set of shortlisted intercriteria pairs as a whole, or we may still wish to find how to rank them.…”