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Techniques
Decision Analysis
.2 Decision Matrices
Table 10.16.1: Simple Decision Matrix
The tables below provide examples of a a simple decision matrix and a weighted
decision matrix.
A simple decision matrix checks whether or not each alternate meets each
criterion being evaluated, and then totals the number of criteria matched for each
alternate. In this example, Alternate 1 would likely be selected because it matches
the most criteria.
Alternate 1 Alternate 2 Alternate 3
Criterion 1 Meets criterion n/a n/a
Criterion 2 Meets criterion Meets criterion Meets criterion
Criterion 3 n/a Meets criterion Meets criterion
Criterion 4 Meets criterion n/a n/a
Score 3 2 2
Table 10.16.2: Weighted Decision Matrix
Criterion
Weighting
A weighted decision matrix assesses options in which each criterion is weighted
based on importance. The higher the weighting, the more important the
criterion. In this example, the criteria are weighted on a scale of 1-5, where 5
indicates the most important. The alternates are ranked per criterion on a scale of
1-5, where 5 indicates the best match. In this example, Alternate 3 would likely
be selected due to its high weighted score.
Alternate 1 Alt 1
Value
Alternate 2 Alt 2
Value
Alternate 3 Alt 3
Value
Criterion 1 1 Rank = 1*3 3 Rank = 1*5 5 Rank = 1*2 2
Criterion 2 1 Rank = 1*5 5 Rank = 1*4 4 Rank = 1*3 8
Criterion 3 3 Rank = 3*5 15 Rank = 3*1 3 Rank = 3*5 15
Criterion 4 5 Rank = 5*1 5 Rank = 5*5 25 Rank = 5*3 15
Weighted
Score
28 37 40
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.3 Decision Trees
For more
information on
decision trees, see
Decision
Modelling
(p. 265).
A decision tree is a method of assessing the preferred outcome where multiple
sources of uncertainty may exist. A decision tree allows for assessment of
responses to uncertainty to be factored across multiple strategies.
Decision trees include:
• Decision Nodes: that include different strategies.
• Chance Nodes: that define uncertain outcomes.
• Terminator or End Nodes: that identify a final outcome of the tree.
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