The power of the Analytic Network Process (ANP) lies
in its use of ratio scales to capture all kinds of interactions and make
accurate predictions, and, even further, to make better decisions. So far, it has proven itself to be a success
when expert knowledge was used with it to predict sports outcomes, economic
turns, business, social and political events.
The ANP is the first mathematical theory that makes it
possible for us to deal systematically with all kinds of dependence and
feedback. The reason for its success is
the way it elicits judgments and uses measurement to derive ratio scales. Priorities as ratio scales are a fundamental
kind of number amenable to performing the basic arithmetic operations of adding
within the same scale and multiplying different scales meaningfully as required
by the ANP.
The Analytic Network Process (ANP) is a new theory
that extends the AHP to cases of dependence and feedback and generalizes on the
supermatrix approach introduced in Thomas Saaty’s 1980 book on the Analytic
Hierarchy Process. It allows interactions and feedback within clusters (inner
dependence) and between clusters (outer dependence). Feedback can better
capture the complex effects of interplay in human society. The ANP provides a
thorough framework to include clusters of elements connected in any desired way
to investigate the process of deriving ratio scales priorities from the distribution
of influence among elements and among clusters. The AHP becomes a special case
of the ANP. Although many decision problems are best studied through the ANP,
it is not true that forcing an ANP model always yields better results than
using the hierarchies of the AHP. There are examples to justify the use of
both. We have yet to learn when the shortcut of the hierarchy is justified, not
simply on grounds of expediency and efficiency, but also for reasons of
validity of the outcome.
