C-FAR m
Decision support system “ A ggregation and  R anking   m ethod ”  (C-FAR m ),  use an innovative approach to create compos...
Why Composite indicators?   Composite indicators are used to summarise complex information and constitute a precious instr...
The demand in composite indicators is rapidly growing for at least two main reasons:  <ul><ul><li>Complexity of modern eco...
Robust and Objective Decision Maker <ul><li>Objectivity  : No empirical manipulation of weights </li></ul><ul><li>Specific...
Three steps  to aggregate complex information  First,   C-FAR m  realizes self-organization of items into homogeneous subs...
Breakthrough C-FARm  solves a major concern of aggregation problems whereby the question of the importance of each variabl...
C-FARm references and Success The model has attracted interest from several organizations: <ul><li>World Bank:  </li></ul>...
Target market <ul><ul><li>Banks and  Insurance   Companies   </li></ul></ul><ul><ul><li>Rating  Agencies   </li></ul></ul>...
<ul><ul><li>Thank You </li></ul></ul>
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CFAR-m

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Advanced Aggregation & Ranking Method

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CFAR-m

  1. 1. C-FAR m
  2. 2. Decision support system “ A ggregation and R anking m ethod ” (C-FAR m ), use an innovative approach to create composite indicators allowing ranking various items (countries, firms, consumers, etc.). C-FAR m is a helpful tool to aggregate multi-dimensional information and extract knowledge for decision-making in many areas.
  3. 3. Why Composite indicators? Composite indicators are used to summarise complex information and constitute a precious instrument of action and decision. <ul><li>These indicators facilitate the task of ranking on complex issues in benchmarking exercise in different areas such : </li></ul><ul><ul><li>Economy and Finance: rank countries, regions, banks, businesses, academic institutions, etc. </li></ul></ul><ul><ul><li>Marketing: rank products, strategies, customers, etc. </li></ul></ul><ul><ul><li>Medicine: rank diseases, hospitals, patients, etc. </li></ul></ul>
  4. 4. The demand in composite indicators is rapidly growing for at least two main reasons: <ul><ul><li>Complexity of modern economics. </li></ul></ul><ul><ul><li>Enormous amount of information has to be processed. </li></ul></ul>Growth in use of Composite indicators Google searches on composite indicators Oct:2005 june:2006 june:2007 Google scholar Google
  5. 5. Robust and Objective Decision Maker <ul><li>Objectivity : No empirical manipulation of weights </li></ul><ul><li>Specificity : A specific equation for each item </li></ul><ul><li>Decision support : Ability to perform simulations and propose action plans and optimal sequence of reforms to decision-makers. </li></ul>C-FAR m is based on an new approach using neural networks insuring a high level of robustness – Its main features are:
  6. 6. Three steps to aggregate complex information First, C-FAR m realizes self-organization of items into homogeneous subsets (clustering), through a learning process that takes into account the positive and negative interactions. Second , an appropriate weights vector is determined for each item. Finally , the weighting vectors are applied to original data to calculate the composite indicator and make the overall ranking.
  7. 7. Breakthrough C-FARm solves a major concern of aggregation problems whereby the question of the importance of each variable is still valid. The weighting system can be characterized as objective since it emanates from the informational content of the variables themselves and their internal dynamics. This last feature of C-FARm represents a valuable step forward and a going-beyond what is currently practiced in terms of classification / aggregation. These advances are based on benefits of Artificial Neural Networks model.
  8. 8. C-FARm references and Success The model has attracted interest from several organizations: <ul><li>World Bank: </li></ul><ul><ul><li>Construction of an indicator of governance and country rankings. </li></ul></ul><ul><ul><li>Developing a plan of reforms to improve standards of governance in Algeria. </li></ul></ul>Other international organizations have also expressed interest in it. <ul><ul><li>International Labor Organisation (OIT) </li></ul></ul><ul><ul><li>African Development Bank (ADB) </li></ul></ul><ul><ul><li>Ministry of Economy, Finance and Industry in France (MINEFI) </li></ul></ul><ul><ul><li>African Union (AU) </li></ul></ul>
  9. 9. Target market <ul><ul><li>Banks and Insurance Companies </li></ul></ul><ul><ul><li>Rating Agencies </li></ul></ul><ul><ul><li>The Office of Research and Investment advice </li></ul></ul><ul><ul><li>Large Enterprises </li></ul></ul><ul><ul><li>Research Institutions </li></ul></ul><ul><ul><li>International Organizations </li></ul></ul><ul><ul><li>Etc. </li></ul></ul>
  10. 10. <ul><ul><li>Thank You </li></ul></ul>

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