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usage of subjective metrics although possible must
be limited, and whenever possible is better to choose
a more objective metric. At the end of this step, the
responsible for the analysis, must be in possession of
each scenario’s scores.
2.7 Weight Criteria
Most of the choice problems analysed in real life do
not have a single selection criterion, but multiple
criteria as presented in multi-criteria analysis. But
since not all criteria are equally important, some sort
of compensation, must be applied so that a more
important criteria, contributes more to the overall
score than less important criteria.
To do this compensation there are several
weighting methods available. In the next sections we
describe several weighting methods that can be
integrated in a multi-criteria analysis (Dodgson et
al., 2009).
2.7.1 Trade-off
This method can reveal the indecisions faced by
stakeholders, comparing pairs of criteria. The
process is the following: for each pair of criteria,
two hypothetical alternatives are constructed, one of
them has the best score on criterion A and the worst
on B, the other alternative is the reverse of the first
one. We start by asking the stakeholders which is the
preferred scenario, and after they made their choice,
we ask how much they were willing to sacrifice the
best performing criterion, in order to maximize the
worst. The answer to these questions reveals the
Trade-Off between the two criteria, or on other
words, the weight associated with which criterion
(Daniels et al., 2001).
2.7.2 SWING
The SWING method also requires generation of
hypothetical alternatives, in this case only two, a
Worst alternative (W), where all criteria have the
lowest possible score and a Best alternative (B),
where all criteria have the best possible score
(Mustajoki et al., 2005).
This method starts with the scenario W, and the
stakeholders are asked which criterion they want to
move first from W to B, and a value of 100 points is
attributed to this criterion. Next they are asked
which criterion they wish to move next from W to B
and how much they value this transition comparing
to the 100 points of the first choice. This last step is
repeated for every criterion, and at the end we will
have all the criteria weighted relatively to the most
preferred criterion, in a normalized scale, since all
weights are contained in the [0;100] interval.
2.7.3 Change Resistance
In this approach each criterion is given two different
performance poles, best and worst, assuming that all
criteria are desirable in the final solution. By putting
all criteria in the best performance, and asking to the
stakeholders to compare all the criteria pairwise, and
choose one to be moved from best to worst state,
repeatedly, until all criteria have been compared
with the rest. The number of times a criterion
maintains its best performance, or in other words,
resists change, is the weight of that criterion.
2.7.4 Macbeth
The Macbeth method regards not only the weighting
step of the analysis, but it integrates weighting
criteria as an essential part. It has some swing and
trade-off, elements, like generating hypothetical
scores (good and neutral), for each criterion. The
objective of this method is to build a cardinal scale
of value, regarding the stakeholder’s preferences, or
alternatives attractiveness, like described in (Bana e
Costa et al., 1997).
2.7.5 Holistic
The holistic approach, as the name suggests, takes in
account the complete set of criteria and the
stakeholders are asked to rank the alternatives
regarding the overall score. In order to extract the
individual criterion weights, is necessary to apply
regression statistical methods. This process although
simple for the stakeholders, since they don’t have to
worry about the individual weights, causes other
problems like judgement inconsistencies, because
stakeholders are unaware of certain factors when
thinking over the full criteria set instead of each
criterion at a time. The need for statistical regression
operations, also adds complexity to the work of the
analyst realizing the analysis (Dodgson et al., 2009).
2.7.6 Selected Weighting Method
In our analysis we need each criterion individual
weight, relatively to the rest of the set, in order to
compute a global score combining the determined
weights with the scenarios score obtained in the
previous step, section 2.7.2. Any of the suggested
weighting methods could be used but in our proposal
we will use SWING, due to its simplicity, the
capacity to deal with large criteria number without
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