IQ & EQ
Today the Wise Investor Must Balance IQ with EQ
Increasingly the value of the corporate human capital is measured by creating an intelligence and environmental (IQ/EQ) risk matrix. While traditional risk assessment can be applied to IQ, the EQ component demands a different approach, which increasingly involves calculations based upon Neural programming principles. Although Probus has achieved the identification of quantitative-based predictive risk in Environmental, Social and Governance, (ESG) based investments, the nature of all environmentally and socially based “conscience” investments requires a way of quantifying the necessarily subjectively based features of these type of investments.
To address these issues, Probus has entered into several collaborative research agreements with University Business Schools and in some cases the commercial arms of these Schools, in particular CFAR-m. The originators of CFAR-m, Professors Nouri Chtourou and Rochdi Feki, have both academic and commercial application experience. This research led to the development of an algorithm called “CFAR-m” a global leader in the field of Competitive Intelligence (CI). Professor Chtouri is currently Professor at the Faculty of Economics at Sfax (Tunisia) and visiting Professor at the University of Sophia-Antipolis (France). Professor Chtouri has served as Consultant to (amongst others) The World Bank and The African Union. Professor Feki has also served as Consultant to The World Bank and African Union, and is currently based at the University Business School of Sfax (Tunisia). Professor Feki’s research focuses on functional forms and their uses in the approximation of production technologies, neural networks and their applications in economics and finance, and construction of composite indicators. There have been many academic white papers and journals published highlighting this work.
The CFAR-m research programmes focus on new institutional economics and governance, public economics and the normative and positive models using artificial neural networks in the problem of construction of composite indicators, classification/clustering and ranking/rating.
CFAR-m stems from collaborative research and development between, economists, and professors of University Business Schools specialising in political & environmental risk, corporate governance, and natural disaster prediction.
CFAR-m processes raw data and extrapolates the knowledge from the following additional qualitative frameworks:
1. General Morphological Analysis (GMA)
2. Analytic Hierarchy Process (AHP)
3. Bayesian Belief Networks (BNNs)
4. Monte Carlo Method
5. Systems Dynamics
6. Artificial Neural Networks (ANNs)
Large scale environmentally based alternate energy projects and similar infrastructure projects, which are often based within emerging economies, are extremely complex challenges involving several interactive phenomena emanating from different fields. This results in the collection of variables that have different units of measure.
To obtain an aggregate score requires defining, for each variable a measure which represent its relative value in comparison to all the others variables (which do not have the same unit of measure). To get this overall score, one must provide a coecient which will represent the contribution of each variable to the phenomena, known as weighting. When the problem is well known and experience with it has an accumulated history, one can provide this weighting. However, when the problem is completely new, it is very dicult to provide such weights requiring substantial time and cost to obtain reliable results and models.
CFAR-m overcomes this problem as:
- It is an original method of aggregating disparate variables based on neural networks which can aggregate with great objectivity the information comprised of a large number of variables originating from diverse different fields
- Its contribution lies in determining, from the data itself, a weighting scheme of variables specific to each particular variable. CFAR-m resolves the major problem of assigning the ‘subjective’ importance of each variable in the overall aggregated score. It avoids the adoption of an equal weighting or a weighting based on exogenous criteria
- The weightings for CFAR-m emanate only from the information content of variables themselves and their own internal dynamics. It has been used and has been validated in many fields such as economics, social phenomena, finance, insurance, ecology, sociology….
The ranking provided by CFAR-m has the following unique features:
- Objectivity -no manipulation of weightings: The weightings are resolutely objective and only originate from the informational content of the variables themselves and their internal dynamics.
- Specificity: A specific equation for each variable is given and its relation to the overall score.
- Decision support: ability to run simulations and propose to the decision makers plans of action and optima sequences of alternate courses of action
CFAR-M METHODOLOGY has been tested within several commercial and civil environments, including:
- The World Bank
- Construction of an indicator of governance and ranking for countries
- Construction of a plan of reforms to increase the level of governance in Algeria.
- International Labour Organization (ILO)
- African Development Bank
- French Finance and Economy Ministry (MINEFI -political risk model)
- African Union
- Construction of an Emerging market risk Index for major Bank
- Construction of a composite index to predict natural disasters (Global change in the occurrence of natural disasters)
CFAR-m has partnerships (amongst others) with Nice University, IBM, HP, CHEOPS TECHNOGY, INFOBRIGHT and particularly with Strategy Foresight LLP, who address very complex organisation al problems often known as ‘wicked problems, social messes and VUCAs’.
Strategy Foresight Partnership LLP (SFP) forms the third arm with Probus and CFARm in the overall solution offering to provide predictive IQ/EQ risk metrics SFP particularly works with “wicked problems”, ‘which are complex, ever changing commercial, societal and organisational planning problems that are:
- Not easily quantifiable – fragmentary or uncertain data
- Continually developing and mutating
- Full of ambiguities and contradictions
- Strongly stakeholder orientated with strong political, moral and professional issues
- Reactive – the problem complex fights back
Public, private and corporate organisations are often called upon to make decisions under conditions of great complexity, high uncertainty and high decision. SFP brings clarity to these complex project and organisational problems, with a rigorously tested set of methodologies and decision support processes, which have been used in over 100 projects worldwide. Organisations and large scale projects are complex ecosystems – they need to collate, compare, shape, formulate, test and manage hundreds of differing standards, social issues, and their thoughts and their relationships – internally and externally.
The financial crash of recent years and its on-going economic, political and social ramifications has opened decision, policy and strategy makers eyes in seeking new ways to address the realities of the highly complex and uncertain world in which we live.
SFP’s specialist methods include:
General Morphological Analysis (GMA) which is a method for structuring and investigating the total set of relationsips contained in a multidimensional, non-quantifiable problem complex. GMA delivers an inference model representing the total problem space, and as many of the potential solutions to the problem complex as possible. GMA relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. Designed for decision-making for exceedingly complex situations, GMA is essentially agnostic. By combining the collective intelligence of key experts, it creates an environment for continuous organizational improvement. Since the mid-90s, GMA has been used in over 100 client-based projects both in the private and public sector including.
- Futures projections and strategic alternatives for insurance
- Transaction Scenarios and Future Strategies for Municipal Housing Companies
- Risk Assessment for New Start-up Investment Strategies
- Energy Diversity Strategies
- Social Exclusion: causes, variations, effects and preventive measures