C-FARm




         1
Decision support system


“Aggregation and Ranking method” (C-FARm), use an innovative
approach to create composite indica...
Why Composite indicators?


Composite indicators are used to summarise complex information and
constitute a precious instr...
Growth in use of Composite indicators

The demand in composite indicators is rapidly growing for at least two
main reasons...
Robust and Objective Decision Maker


C-FARm is based on an new approach using neural networks insuring a
high level of ro...
Three steps to aggregate complex information


First, C-FARm realizes self-organization of items into homogeneous
subsets ...
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:
        World Bank:
          ...
Target market



  • Banks and Insurance Companies
  • Rating Agencies
  • The Office of Research and Investment advice
  ...
Thank You


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CFAR-

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CFAR-

  1. 1. C-FARm 1
  2. 2. Decision support system “Aggregation and Ranking method” (C-FARm), use an innovative approach to create composite indicators allowing ranking various items (countries, firms, consumers, etc.). C-FARm is a helpful tool to aggregate multi-dimensional information and extract knowledge for decision-making in many areas. 2
  3. 3. Why Composite indicators? Composite indicators are used to summarise complex information and constitute a precious instrument of action and decision. These indicators facilitate the task of ranking on complex issues in benchmarking exercise in different areas such :  Economy and Finance: rank countries, regions, banks, businesses, academic institutions, etc.  Marketing: rank products, strategies, customers, etc.  Medicine: rank diseases, hospitals, patients, etc. 3
  4. 4. Growth in use of Composite indicators The demand in composite indicators is rapidly growing for at least two main reasons: • Complexity of modern economics. • Enormous amount of information has to be processed. Google searches on composite indicators Google ; juin-07; 1240000 Google académique Google scholar Google Google Google académique; Google ; Oct :2005; Google ; juin-06; juin-07; 174000 Google académique; 35000 80000 Google académique; Oct :2005; 992 juin-06; 1440 Oct:2005 june:2006 june:2007 4
  5. 5. Robust and Objective Decision Maker C-FARm is based on an new approach using neural networks insuring a high level of robustness – Its main features are: • Objectivity : No empirical manipulation of weights • Specificity : A specific equation for each item • Decision support : Ability to perform simulations and propose action plans and optimal sequence of reforms to decision-makers. 5
  6. 6. Three steps to aggregate complex information First, C-FARm 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. 6
  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. 7
  8. 8. C-FARm references and Success The model has attracted interest from several organizations: World Bank: • Construction of an indicator of governance and country rankings. • Developing a plan of reforms to improve standards of governance in Algeria. Other international organizations have also expressed interest in it. • International Labor Organisation (OIT) • African Development Bank (ADB) • Ministry of Economy, Finance and Industry in France (MINEFI) • African Union (AU) 8
  9. 9. Target market • Banks and Insurance Companies • Rating Agencies • The Office of Research and Investment advice • Large Enterprises • Research Institutions • International Organizations •Etc. 9
  10. 10. Thank You 10

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