Thinking and Deciding

MAUT AS A TYPE OF DECISION ANALYSIS 349
MAUT as a type of decision analysis
Conjoint measurement is one of the more amazing “rabbit out of the hat” tricks that
mathematicians have been able to show us in utility theory. It provides the theoretical
basis for MAUT. Like other forms of decision analysis, MAUT is based on the idea
expressed in the motto “Divide and conquer.” In this case, the division is into psychologically
independent attributes: that is, attributes that the decision maker views
as being separate and independent. If the dimensions are indeed independent, proper
application of MAUT ensures that the decision made will maximize the achievement
of our goals. The attribute on each dimension determines the extent to which we
achieve the goal corresponding to that dimension, so we can measure the utility of
each option on each dimension and add up the utilities on the various dimensions in
order to determine the total utility of the option.
If the attributes in the particular decision do not seem to be psychologically independent
from the outset, MAUT, in its basic form, has no normative status and
should not be attempted. For example, suppose you were using MAUT to decide
which computer to buy, and the three dimensions were price, memory size, and harddisk
size. If you regarded these three dimensions as independent, you could use a
MAUT analysis. You might think that a large memory makes disk size less important,
however, so that you would be willing to pay less extra money for a large disk
if the computer had a large memory than if it had a small memory. In this case, the
size of the memory would affect the tradeoff between price and disk size. If you tried
to use disk size to measure the utility units for price, you would get different results
for different memory sizes. You need not go through the whole MAUT analysis to
determine whether this is the case. You can just ask yourself directly whether this
would occur.
It is also important to make sure that the dimensions really refer to different
things. For example, it would not be helpful to add a fourth dimension, “ease of
use,” to your analysis, because ease of use is affected by memory and disk size. If
you did this, you would be counting the effects of memory and disk size more than
once. A suitable new dimension would be “keyboard layout.”
Although conjoint measurement provides the theoretical basis for MAUT, you do
not have to use conjoint measurement to estimate the utilities for a MAUT analysis.
Once you have established that the dimensions are independent, you can use any of
the measurement techniques described in the last section: difference measurement,
direct rating, or any other method, on one attribute at a time.
Suppose you have found that the three dimensions of the computer really are
independent for you. You assign a utility of 100 to the most desirable end of each
dimension, and 0 to the least desirable end. Suppose that you gather data on four
different models, organized as in Table 14.1, with the utilities on each dimension
shown in parentheses. You cannot simply add up the utilities for each model and
compare them to decide among them. If you do, model B will win, but perhaps you
do not care very much about memory size. If the price differences matter more to
you, you will want model A; if disk size is more important, you will want model C.