The main argument and aim of this study is that given the different views about the nature of KE and competitiveness, the abundance of indicators and the con- fusion they create to the decision makers, a more intelligent, flexible and univer- sally acceptable measure of the constituent elements contributing to KBE com- petitiveness can be better selected, weighted, aggregated and forecasted through the adoption of computationally intelligent approaches. The present research therefore will employ and assess such approaches to discover the utilisation of Computational Intelligence (CI) methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering an intelligent qualitative taxonomy as a theoretical framework for making a Unified Macro-Knowledge Competitive- ness Indicator (UKCI ) to enable consistent and transparent assessments and fore- casting of the progress and competitiveness of KBE. In this thesis, the focus is on whether different CI methodologies to build SCIs, would lead to different results. The use of CI techniques to build the quantitative side of a new SCI includes the use of Fuzzy Proximity Knowledge Mining (FPKM) methodology for the pur- pose of devising a non-biased, novel and intelligent method to create a new SCI taxonomy. This research also aims to fill the gap where existing KE indicators have failed. A contemporary and unified macro-knowledge epistemology is pro- posed, where many new factors such as intellectual capital and competitiveness would constitute a major ingredient for a reliable KBE measurement. Such new view would give credit to the efforts made by emerging, competitive and vibrant nations, which existing KBE indicators discounts.
The proposed methods to construct the UKCI will be applied to fifty-seven countries initially, then expanded to include the MENA region countries as a special case study. In total seventy three countries will be included, that are rep- resentative of developed, developing and underdeveloped economies. The UKCI will be evaluated on two levels: from a quantitative point of view and from real case study application in order to show the value added by the new development techniques and measure. The validity and robustness of all techniques are eval- uated using Monte Carlo simulation. Finally, the UKCI will be subjected to a number of uncertainty and sensitivity analyses. It is envisaged that the KMS
1. Introduction
thus developed is capable of evaluating, measuring, describing, forecasting and analysing the main issues that affect knowledge economies on a macro level.
To accomplish the aim of this research, the following objectives are identified:
- To develop understanding and to critically evaluate the current and existing position of the KBEs by studying the available measurements and tools of utilisation issued by the global indicators.
- To propose an alternative method to the measurement of KBE and com- petitiveness, to integrate the strengths and resolve the shortcomings of the assessed techniques.
- To coin a novel CI technique as a a non-biased way to create the qualitative taxonomy of future SCIs. Fuzzy Proximity Knowledge Mining (FPKM) technique is proposed for this purpose. The suggested FPKM consists of two major steps: Focused web mining using Soft Focused Crawler (SFC), and fuzzy text matching using Wagner-Fischer dynamic programming al- gorithm for computing the Levenshtein or ‘edit distance’. The suggested taxonomy will serve as a non-biased, novel and intelligent method for inclu- sion/exclusion and unifications of empirical variables to establish significant, consistent and sound SCI theoretical framework.
- To establish an intelligent and universally acceptable KBE measurement indicator; a number of analysis methods will be used including Principal Component Analysis (PCA), Factor Analysis (FA), Geometric Mean Ag- gregation and CI techniques such as, Fuzzy c-Means (FCM) and Vector Quantization (VQ). These methods will be contrasted and compared to introduce a valuable tool for weighting and aggregating the quantitative elements of future SCIs, and it would change the norm when ranking and classifying nations.
- To compare and contrast the performance of different missing data impu- tation methods including two special FCM techniques that is, the Opti- mal Completion Strategy (OCS), the Nearest Prototype Strategy (NPS). The results are compared against statistical imputation techniques namely;
1. Introduction
the Expectation Maximisation (EM), Multiple Imputation (MI), Nearest Neighbour (NN) and Multiple Regression (MR).
- To investigate the performance of different prediction and forecasting meth- ods to assess the most appropriate technique for forecasting KBE compet- itiveness performance given the limited data sets available. Time Series, Cross Sectional (TSCS) Panel Data, ANN and SOM will be investigated to create a Unified Knowledge Economy Forecast Map (UKFM).
- To introduce a novel macro knowledge capacity building and competitive- ness framework by constructing an Intelligent Syntactic Composite Indica- tors (iSCI) for any nation to share their knowledge, monitor their progress, track their KBE competitiveness to improve their overall welfare.
- To simplify and calibrate the final developed model, a robustness analysis will be performed using Monte Carlo simulation, as an appropriate and justifiable model robustness technique.
- To validate the effectiveness of the introduced iSCI and UKCI frameworks and to evaluate its strengths and weaknesses. Economies in the Middle East and North Africa (MENA) region are used as case study.
In attaining the above goals the current research study makes a contribu- tion to producing a novel and intelligent indicator, would be suitable for any country with different cultural, socio-economic and technical conditions. It is envisaged to assist such economies in establishing and monitoring a suitable and competitive knowledge based economy. This research uses real data sets to illus- trate constructing the major components of the proposed index, which includes the qualitative taxonomy, missing values imputations, the weighting, aggregation and forecasting of the suggested UKCI variables.