280 Unit 7 Critical reasoning: Advanced Level
(You will sometimes see the word ‘utility’ used
instead of ‘value’. Note also that the value, or
utility, of something can be zero, or even
negative: a liability rather than an asset.)
How do these two concepts figure in
rational decision making? The answer, if not
already obvious, is as follows.
Firstly (1): since we cannot always be sure
what the outcomes of a particular decision will
be, the best we can usually do is to estimate
how likely they are. In the case of the two cars,
we would naturally like to know what the
chances are of its developing a serious fault in
the foreseeable future. Taking the older car
first, the kind of factors we would consider
would be not just its age but the mileage it had
done, the number of owners it had had, its
service record, and so on. We might also want
to look at information in an auto magazine, or
ask someone with expertise how reliable such
makes and models are above a certain age and
mileage. We may want to consider the
reputation of the seller. If the answers to these
questions are all, or mostly, positive, this raises
the likelihood of getting – say – three good
years of use from the car. (Fewer than that
would mean the car had been poor value;
more would be a bonus.) If the answers are
mostly negative, the chances of this positive
outcome would be lowered.
‘Raised’, ‘lowered’ and ‘likely’ are still rather
vague notions. Ideally we would want a more
precise, quantifiable measure of the
probabilities. Statistically such figures will
exist, and can be found if you are prepared to
go to the trouble. Suppose a representative
sample of cars of a certain make, age and
mileage have been assessed for their reliability,
and it turns out that around 60% of them gave
their owners three years of trouble-free use,
whilst 40% developed one or more serious
problems, some irreparable. Now let’s suppose
that the statistics for the other, newer car in
our scenario, were 90% : 10% using the same
criteria. Which car would you buy?Consequences are what follow from a
decision: the outcomes of actions. In practical
terms consequences are what determine
whether a decision is a good one or not. This,
too, is a very obvious point; but again it is easy
to ignore or play down the importance and/or
likelihood of some potential outcome, especially
when trying to justify a decision already partly
made, or favoured more than others.
A familiar example is the scenario of
deciding which of various products to buy,
particularly when it is a major item like a car, a
new bicycle, or computer. It is very easy to let
ourselves be persuaded by advertising, or by
pre-existing preferences, rather than by
predictable consequences. Take the choice
between buying a comparatively new and
therefore quite expensive car, or an older but
much cheaper one. If as a result of buying the
newer car you find you have taken on a debt
that you can’t meet, you may regret the
decision. On the other hand, if the older car
promptly breaks down and lands you with a
massive repair bill, or worse still has to be
scrapped, you may wish you had chosen the
more expensive but more reliable model.
Expressed in these general terms it seems
like a lottery. How can we know in advance
which of these possibilities will be the actual
outcome? We don’t. No one can pretend that
decision making, or prediction, is an exact
science. But that does not mean it is not a
rational activity, nor that it cannot be made
more reliable by approaching it in a
methodical rather than a random way. A
sound decision – as opposed to a random
choice – can only be made if it is informed; and
to be informed it must be based on some kind
of factual or statistical or quantifiable data.
Assessing consequences
This brings us to two key criteria by which
consequences can be critically assessed. The
criteria are:
1 probability (likelihood, chance)
2 value (importance, seriousness, cost).