Thinking and Deciding

MAUT AS A TYPE OF DECISION ANALYSIS 353
how such an analysis would work. You might try a holistic estimate first, in which
you simply give your opinion. Then use your own best guesses for the effect of
different speed limits on the various dimensions, and give your own utilities for the
dimensions.
MAUT can be modified considerably for the analysis of real decisions. For example,
suppose you have one usable eye, and it has a cataract that your doctor must
eventually remove. You must decide whether to have the cataract removed now or
wait a month. Your vision is getting slowly worse. A successful operation will restore
your vision fully, but a failure will leave you blind. The probability of failure
is something your doctor can tell you. What you need is an estimate of the relative
utility — for the next month — of the three possible outcomes: restored vision,
blindness, and your present vision (assuming that it does not change over the course
of the month). How can you think about this? One way is to think of the various activities
that you can do given some outcome but not another. The activities should be
separate and independent. For example, you cannot count both “reading” and “doing
your job,” if your job involves reading. (You can count “reading for pleasure.”) You
can assign utilities to these activities by finding groups of them that are equivalent.
Suppose that there are ten activities, all with the same utility. Nine are activities
you can do now but would not be able to do if you were blind, but there is one
you cannot do now but would be able to do if your vision were restored. If we
assign a utility of 100 to restored vision and 0 to blindness, the utility of your present
condition is 90. You should have the operation now if the probability of success is
greater than .90, since its expected utility would then be at least .90 · 100 + .10 · 0,or
90. Of course, after doing an analysis like this, you could decide that the activities
were not independent after all, because the loss of the ability to engage in one of them
would be much worse if it were the only one left than if there were other alternatives.
Nonetheless, an analysis of this sort could help you to think about your decision in
a new way. If you repeated this process month after month, eventually you would
reach a point at which the activities you would gain back from success would be
worth the risk of failure.
Table 14.2 shows another example of a multiattribute analysis. The question is
to choose a birth control method. This too is a repeated decision. Let us assume it is
made once a year, so the decision concerns the effect of using one method or another
for a year. The particular analysis into attributes is one of many ways to do this. A
unique feature is the separation of AIDS risk from risk of other sexually transmitted
diseases. Some attributes are omitted, such as cost, religious issues, reversibility,
and health effects other than sexually transmitted diseases. Many methods are not
included, such as sterilization, implants, the “ryhthm method” and combinations of
methods.
In order to assign weights to the attributes, we need to know their ranges. Here
is how they are defined, in order from left to right:
HIV prevention. Range is 0 to 1 in 1,000 chance of infection per year. (These figures
are hypothetical.)
STD prevention. Sexually transmitted diseases other than HIV infection include