ATTRIBUTE SEARCH ITERATIONS. Each attribute selection path, from start to end nodes, is
regarded as a complete iteration cycle, regardless of the selection method used. This could be the
forward, backward, or combined approach. How many search iterations would then be required
until the selection goals are met? Searching for attributes can be an easy task if the number of
attribution model nodes is not too great. More comprehensive implementations, however, would
require looking through a significant number of attribute permutations and pursuing various search
paths until the attribution analysis process yields satisfactory results.
One of the major determining factors for pursuing multiple iterations and scenarios is core
attribute collection size, which is located in level 1 of the attribution model. This is where the
84 Ch. 4 Attribution Analysis
top-down collection process begins. For example, an attribution model that is made up of four
attributes??”such as return, time, liquidity, and risk??”results in 11 node variations. This model is
regarded as a small-sized network. In large-scale projects, however, the node count can grow exponentially,
potentially expressing hundreds of attribute permutations. Thus, in such scenarios it would
be worth pursuing multiple search iterations before concluding a successful attribution process.
Additional attribute inspection and search iterations are also necessary when business
requirements change.
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