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Aggregation is a way to combine several single indicators representing different components (dimensions)
of the same concept to form a single aggregate. The result leads to a single score, called a composite
indicator, which has the ability to summarize a large amount of information in a comprehensible form .
Aggregation requires the determination of a weighting scheme of the different components. This task is
extremely difficult and is one of the central problems in the construction of composite indicators.
Weights must take into account all existing forms of interaction between the components aggregated and
have a significant effect on the result. However, there is no universally agreed methodology and the
arbitrary nature of the weighting process by which components are combined constitutes the main
weakness of composite indicators which CFAR-m overcomes.
CFAR-m OVERCOMES THIS PROBLEM:
CFAR-m is an original method of aggregation based on neural networks which can summarize
with great objectivity the information contained in a large number of variables emanating from
many different fields.
Its contribution lies in determining, from the database itself, a wei ghting scheme of variables
specific to each individual. CFAR-m solves the major problem of fixing the subjective importance
of each variable in the aggregation.
It avoids the adoption of an equal weighting or a weighting based on exogenous criteria. Th e
weightings for CFAR-m emanate only from the information content of variables themselves and
their own internal dynamics.
THE RANKING PROVIDED BY CFAR-m HAS THE FOLLOWING ENABLES THE FOLLOWING
Objectivity: No handling of weightings - the weighting is resolutely objective and it emanates from
the informational content of the variables themselves of their research and internal dynamics.
Specificity: a specific equation for each individual piece of data to is used calculate the indicator
Decision support: ability to run simulations and propose to the decision makers plans of action
and optimal sequences of reforms.
It can provides the contribution of the variables to the ranking
It keeps all the variables during the calculus and so it is helpful for extracting what is happening
within the noise. This is very interesting for predicitve models.