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" Computational intelligence for measuring macro-knowledge competitiveness "
Al-Shami, A. Q.
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Latin Dissertation
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808796
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TLets629322
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Al-Shami, A. Q.
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Computational intelligence for measuring macro-knowledge competitiveness\ Al-Shami, A. Q.
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Nottingham Trent University
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2013
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2013
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Thesis (Ph.D.)
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| Abstract
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The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators. To illustrate the method of construction for the proposed iSCI, a fully worked application is presented. The presented application employs Information and Communication Technology (ICT) real variables to form a new unified ICT index. The weighting and aggregation results obtained were compared against classical approaches namely Vector Quantisation and Principal Component Analysis, Factor Analysis and the Geometric mean to weight and aggregate synthetic composite indicators. This study also compares and contrasts Optimal Completion Strategy and the Nearest Prototype Strategy to substitute missing values. The validity and robustness of the techniques are evaluated using Monte Carlo simulation. The developed iSCI concept is generalised to build the suggested UKCI which ultimately is equipped with short-term forecasting capabilities. This achieved by a hybridised model consisting of Artificial Neural Networks and Panel Data: Time Series Cross Sectional to predict and forecast the competitiveness of KBE. The proposed model has the capability of forecasting and aggregating seven major KBE indicators into a unified meaningful map that places any KBE in its league even with limited data points. The Unified Knowledge Economy Forecast Map reflects the overall position of homogeneous knowledge economies, and it can be used to visualise, identify or evaluate stable, progressing or accelerating KBEs. In order to show the value added by the new development techniques, the UKCI is applied to fifty-seven countries initially, then expanded to include the Middle East and North Africa (MENA) region as a special case study. In total seventy-three countries were included, that are representative of developed, developing and underdeveloped economies. The final and overall results obtained, suggest novel, intelligent and unbiased results compared to traditional or statistical methods when building, not only the UKCI, but for any future composite indicator for many other fields.
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Nottingham Trent University
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