The local priorities, shown in gray, represent the relative weights of the nodes within a group of siblings with respect to their parent. You can easily see that the local priorities of each group of Criteria and their sibling Subcriteria add up to 1.000. The global priorities, shown in black, are obtained by multiplying the local priorities of the siblings by their parent's global priority. The global priorities for all the subcriteria in the level add up to 1.000.
The rule is this: Within a hierarchy, the global priorities of child nodes always add up to the global priority of their parent. Within a group of children, the local priorities add up to 1.000.
So far, we have looked only at default priorities. As the Analytical Hierarchy Process moves forward, the priorities will change from their default values as the decision makers input information about the importance of the various nodes. They do this by making a series of pairwise comparisons.
Practical examples[edit]
Experienced practitioners know that the best way to understand the AHP is to work through cases and examples. Two detailed case studies, specifically designed as in-depth teaching examples, are provided as appendices to this article:
Simple step-by-step example with four Criteria and three Alternatives: Choosing a leader for an organization.
More complex step-by-step example with ten Criteria/Subcriteria and six Alternatives: Buying a family car and Machinery Selection Example.[26]
Some of the books on AHP contain practical examples of its use, though they are not typically intended to be step-by-step learning aids.[23][27] One of them contains a handful of expanded examples, plus about 400 AHP hierarchies briefly described and illustrated with figures.[25] Many examples are discussed, mostly for professional audiences, in papers published by the International Symposium on the Analytic Hierarchy Process.[28][29][30][31][32]
Criticisms[edit]
The AHP is included in most operations research and management science textbooks, and is taught in numerous universities; it is used extensively in organizations that have carefully investigated its theoretical underpinnings.[5] While the general consensus is that it is both technically valid and practically useful, the method does have its critics.[8] Most of the criticisms involve a phenomenon called rank reversal, discussed in the following section.
Rank reversal
Decision making involves ranking alternatives in terms of criteria or attributes of those alternatives. It is an axiom of some decision theories that when new alternatives are added to a decision problem, the ranking of the old alternatives must not change — that "rank reversal" must not occur.
There are two schools of thought about rank reversal. One maintains that new alternatives that introduce no additional attributes should not cause rank reversal under any circumstances. The other maintains that there are some situations in which rank reversal can reasonably be expected. The original formulation of AHP allowed rank reversals. In 1993, Forman[33] introduced a second AHP synthesis mode, called the ideal synthesis mode, to address choice situations in which the addition or removal of an 'irrelevant' alternative should not and will not cause a change in the ranks of existing alternatives. The current version of the AHP can accommodate both these schools—its ideal mode preserves rank, while its distributive mode allows the ranks to change. Either mode is selected according to the problem at hand.
Rank reversal and the AHP are extensively discussed in a 2001 paper in Operations Research,[5] as well as a chapter entitled Rank Preservation and Reversal, in the current basic book on AHP.[27] The latter presents published examples of rank reversal due to adding copies and near copies of an alternative, due to intransitivity of decision rules, due to adding phantom and decoy alternatives, and due to the switching phenomenon in utility functions. It also discusses the Distributive and Ideal Modes of the AHP.
There are different types of rank reversals. Also, other methods besides the AHP may exhibit such rank reversals. More discussion on rank reversals with the AHP and other MCDM methods is provided in the rank reversals in decision-making page.
Non-Monotony of some weight extraction methods
Within a comparison matrix one may replace a judgement with a less favourable judgement and then check to see if the indication of the new priority becomes less favourable then the original priority. In the context of tournament matrices, it has been proven by Oskar Perron in,[34] that the principal right eigenvector method is not monotonic. This behaviour can also be demonstrated for reciprocal n x n matrices, where n > 3. Alternative approaches are discussed in.[35][36][37]