Page 292 - Mechanical Engineers' Handbook (Volume 2)

4 Systems Engineering Methodology and Methods 283

the consequences, and aggregating or summarizing these values for all consequences
of each action. In doing this, we obtain an expected utility evaluation of each alter-
native act. The one with the highest value is the most preferred action or option.
Figure 7 presents some of the salient features involved in the decision analysis of a
simplified problem.
• Multiattribute utility theory (MAUT) has been designed to facilitate comparison and
ranking of alternatives with many attributes or characteristics. The relevant attributes
are identified and structured and a weight or relative utility is assigned by the decision-
maker to each basic attribute. The attribute measurements for each alternative are used
to compute an overall worth or utility for each attribute. Multiattribute utility theory
allows for explicit recognition and incorporation of the decision-maker’s attitude to-
ward risk in the utility computations. There are a number of variants of MAUT; many
of them are simpler, more straightforward processes in which risk and uncertainty
considerations are not taken into account. The method is very helpful to the decision-
maker in making values and preferences explicit and in making decisions consistent
with those values. The tree structure of Fig. 6 also indicates some salient features of
the MAUT approach for the particular case where there are no risks or uncertainties
involved in the decision situation. We simply need to associate importance weights
with the attributes and then provide scores for each alternative on each of the lowest
level attributes.
• Policy capture (or social judgment theory) has also been designed to assist decision-
makers in making their values explicit and their decisions consistent with their values.
In policy capture, the decision-maker is asked to rank order a set of alternatives in a
gestalt or holistic fashion. Alternative attributes and associated attribute measures are
then determined by elicitation from the decision-maker. A mathematical procedure
involving regression analysis is used to determine that relative importance weight of
each attribute that will lead to a ranking as specified by the decision-maker. The result
is fed back to the decision-maker, who, typically, will express the view that his or her
values are different. In an iterative learning process, preference weights and/or overall
rankings are modified until the decision-maker is satisfied with both the weights and
the overall alternative ranking.
There are many advantages to formal interpretation efforts in systems engineering, in-
cluding the following:

1. Developing decision situation models to aid in making the choice-making effort
explicit helps one both to identify and to overcome the inadequacies of implicit
mental models.
2. The decision situation model elements, especially the attributes of the outcomes of
alternative actions, remind us of information we need to obtain about alternatives
and their outcomes.
3. We avoid such poor information-processing heuristics as evaluating one alternative
on attribute A and another on attribute B and then comparing them without any basis
for compensatory trade-offs across the different attributes.
4. We improve our ability to process information and, consequently, reduce the possi-
bilities for cognitive bias.
5. We can aggregate facts and values in a prescribed systemic fashion rather than by
adopting an agenda-dependent or intellect-limited approach.
6. We enhance brokerage, facilitation, and communication abilities among stakeholders
to complex technological and social issues.