Thinking and Deciding

258 DESCRIPTIVE THEORY OF CHOICE UNDER UNCERTAINTY
utility for gains — but psychological research has found determinants of risk aversion
that cannot be explained this way. We are therefore biased in making decisions
when outcomes are uncertain, just as we are biased in the various ways we examined
in Part II.
Some writers make a distinction between risk and uncertainty. They use the term
“risk” when the probabilities of outcomes are known. A Bayesian view of probability,
however, tends to downplay this distinction, and I shall give other arguments
against its importance later in this chapter. For the purpose of talking about experimental
results, though, we can take “risk” to refer to experiments in which subjects
are given numerical probabilities and “uncertainty” to refer to experiments in which
they are not. Most of the experiments are about risk.
Experienced, predicted, and decision utility
An important distinction among three ways of talking about utility is that between
experienced, predicted, and decision utility (Kahneman, 1994; Kahneman, Frederickson,
Schreiber, and Redelmeier, 1993; Kahneman and Snell, 1992; Varey and
Kahneman, 1992). In essence, experienced utility is what really matters. (The term
makes an assumption that experiences are all that matter, an assumption I have rejected
here, but we could expand its meaning to include goal achievement of all sorts
without losing the point of the main distinctions at issue.) If you try two different
kinds of beer, then the experience of drinking each beer is its true (experienced) utility.
Predicted utility is the judgment you would make about each experience, how
good it would be, possibly on the basis of memory of previous experience. To assess
your decision utility, I would ask you to rate each beer. Decision utility is inferred
from your choice. I would observe which one you choose.
The three types of utility could conflict. Beer A might taste better (provide more
experienced utility) than beer B, but you might predict the opposite. You might, for
example, had a naive theory that a beer tastes better when you haven’t had it for a
while, and you might base your prediction on the fact that you haven’t had B for a
long time. Or, you might even predict that A would taste better, but you might choose
B anyway because you follow a general heuristic of seeking variety, a rule that here
could let you down, in terms of experienced utility.
In a dramatic example of the conflict between predicted and experienced utility,
Sieff, Dawes, and Loewenstein (1999) asked people who were tested for HIV virus
to predict how they would feel five weeks after getting the test result, if the test
were positive, and if it were negative. Those who received positive results thought
they would feel happier than they felt, and those who received negative results also
underpredicted their unhappiness. People adapt more quickly than they think they
will.
In a less dramatic example, Kahneman and Snell (1992) explored some prediction
failures in cases in which the main goals were purely hedonic: experiencing
pleasure and avoiding pain. In one study, subjects were asked to predict how much