The basic problem of decision making is to choose the best one in a set of competing alternatives that are evaluated under conflicting criteria. The Analytic Hierarchy Process (AHP) provides us with a comprehensive framework for solving such problems. It enables us to cope with the intuitive, the rational, and the irrational, all at the same time, when we make multi-criteria and multi-actor decisions. We can use the AHP to integrate our perceptions and purposes into an overall synthesis. The AHP does not require that judgments be consistent. The degree of consistency (or inconsistency) of the judgments is revealed at the end of the AHP process. Most of us have difficulty examining even a few ideas at a time. We need instead to organize our problems in complex structures which allow us to think about them one or two at a time. We need both simplicity and complexity. We need an approach that is conceptually simple so that we can use it easily. And at the same time…
We need an approach that is robust enough to handle real world decisions and complexities.
The Analytic Hierarchy Process is such a problem-solving framework. It is a systematic procedure for representing the elements of any problem. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then calls for only simple pairwise comparison judgments to develop priorities in each hierarchy.
The AHP can be characterized as a multi-criteria decision technique in which qualitative factors are of prime of importance. A model of the problem (team contribution) is developed using a hierarchical representation. At the top of the hierarchy is the overall goal or prime objective one is seeking to fulfill.
We are all fundamentally decision makers. Everything we do consciously or unconsciously is the result of some decision. The information we gather is to help us understand occurrences in order to develop good judgments to make decisions about these occurrences. Not all information is useful for improving our understanding and judgments. If we only make decisions intuitively, we are inclined to believe that all kinds of information are useful and the larger the quantity the better. But that is not true. There are numerous examples which show that too much information is as bad as little information. Knowing more does not guarantee that we understand better as illustrated by some author’s writing “Expert after expert missed the revolutionary significance of what Darwin had collected. Darwin, who knew less, somehow understood more.” To make a decision we need to know the problem, the need and purpose of the decision, the criteria of the decision, their sub- criteria, stakeholders and groups affected and the alternative actions to take. We then try to determine the best alternative, or in the case of resource allocation we need priorities for the alternatives to allocate their appropriate share of the resources.
Alphabetical list of AHP reference material.
It measures intangibles along side tangibles and produces valid results close to the answer in measuring tangibles...
This is a full methodological briefing with all of the math and background from the founders of AHP at Decision Lens Inc.
Nearly all of us have been brought up to believe that clear-headed logical thinking is our only sure way to face and solve problems. But experience suggests that logical thinking is not natural to us. Indeed, we have to practice, and for a long time, before we can do it well. Since complex problems usually have many related factors, traditional logical thinking leads to sequences of ideas so tangled that the best solution cannot be easily discerned.
Lecture 2 – Ratings and Introduction to Analytic Network Process
Decision making depends on identifying a structure of criteria and alternatives of a decision. It also depends on experience and judgments to select the best alternative. From the judgments, priorities are derived in the form of eigenvectors. An eigenvector is a technical mathematical idea that would benefit from simplifying explanation. That is what this note does in two ways.
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The data that an enterprise has around strategy is like no other data in the organization.
It is a combination of qualitative assessments with quantitative factors. Data, in and of itself, has no intrinsic meaning unless it is put into context, and this is especially true of the data related to strategy.
By way of example, what does 40 degrees Fahrenheit mean to a Floridian vs. an Alaskan? Two very different things. Cold is a relative concept. Data, like temperature readings, needs to be evaluated as to how it applies to the decision at hand.
Data needs to be evaluated as to how it applies to the decision at hand.
The ratings process not only values data in this way by using ratings scales with tiered increments that apply value to data or judgments based on what the impact is on a particular evaluation factor, but it also enables a group to see where there are areas of agreement and disagreement in the evaluation.
Oftentimes we will see groups that violently disagree on the rating of particular projects only to find that their understanding of the criterion on which they are rating is vastly different. For example, in our work with the NFL, we found conflicting ratings for the same players under the term “agility”. In working with the scouts, some of them believed that agility was a measure of speed, while others considered it to be a measure of maneuverability on the field of play.
Many organizations move through planning processes with fundamental misunderstandings or misalignment on priorities, and it is only through an analytical, repeatable, and transparent process that full alignment and better outcomes can be realized.
In this paper it is shown that the principal eigenvector is a necessary representation of the priorities derived from a positive reciprocal pairwise comparison judgment matrix A = (aij) when A is a small perturbation of a consistent matrix. When providing numerical judgments, an individual attempts to estimate sequentially an underlying ratio scale and its equivalent consistent matrix of ratios. Near consistent matrices are essential because when dealing with intangibles, human judgment is of necessity inconsistent, and if with new information one is able to improve inconsistency to near consistency, then that could improve the validity of the priorities of a decision. In addition, judgment is much more sensitive and responsive to large rather than to small perturbations, and hence once near consistency is attained, it becomes uncertain which coefficients should be perturbed by small amounts to transform a near consistent matrix to a consistent one. If such perturbations were forced, they could be arbitrary and thus distort the validity of the derived priority vector in representing the underlying decision.
The rank of a given set of independent alternatives with respect to several criteria must stay the same if new alternatives are added or old ones deleted unless adding or deleting alternatives introduces or deletes criteria and changes judgments.