Background
Ordinary aggregated indices method allows comprehensive estimation of complex (multi-attribute) objects’ quality. Examples of such complex objects (decision alternatives, variants of a choice, etc.) may be found in diverse areas of business, industry, science, etc. (e.g., large-scale technical systems, long-time projects, alternatives of a crucial financial/managerial decision, consumer goods/services, and so on). There is a wide diversity of qualities under evaluation too: efficiency, performance, productivity, safety, reliability, utility, etc.
The essence of the aggregated indices method consists in an aggregation (convolution, synthesizing, etc.) of some single indices (criteria) q(1),…,q(m), each single index being an estimation of a fixed quality of multiattribute objects under investigation, into one aggregated index (criterion) Q=Q(q(1),…,q(m)).
In other words, in the aggregated indices method single estimations of an object, each of them being made from a single (specific) “point of view” (single criterion), is synthesized by aggregative function Q=Q(q(1),…,q(m)) in one aggregated (general) object’s estimation Q, which is made from the general “point of view” (general criterion).
Aggregated index Q value is determined not only by single indices’ values but varies depending on non-negative weight-coefficients w(1),…,w(m). Weight-coefficient (“weight”) w(i) is treated as a measure of relative significance of the corresponding single index q(i) for general estimation Q of the quality level.
Read more about this topic: Aggregated Indices Randomization Method
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