Hierarchical decision process
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The Hierarchical Decision Process (HDP) refines the classical AHP Analytic Hierarchy Process a step further in eliciting and evaluating subjective judgements. These improvements, proposed initially by Dr. Jang Ra (a student of Dr. Saaty Thomas L. Saaty who developed and refined AHP) include the constant-sum measurement scale (1-99 scale) for comparing two elements, the logarithmic least squares method (LLSM) for computing normalized values, the sum of inverse column sums (SICS) for measuring the degree of (in)consistency, and sensitivity analysis of pairwise comparisons matrices. These subtle modifications address issues concerning normal AHP consistency and applicability in the process of constructing hierarchies: generating criteria, classifying/selecting criteria, and screening/selecting decision alternatives. IEEE has a link to the five page pdf in Technology Management: the New International Language, 27-31 Oct 1991, pp. 595–599, ISBN 0-7803-0161-7 [1], although it costs about $29.95. New posts to this article will include excerpts and notes from the University of Alaska's Engineering Science / Project Management Risk Management course 624 in which Dr. Ra discussed the applicability of both AHP and DHP in context of actual case studies in both chain and pairwise comparison techniques linked into Decision Tree Analysis (bayesian networks).