The Expected Utility Model: Its Variants, Purposes, Evidence and ...

Schoemaker: The Expected Utility Model 551
consumer products across stores. The
price variance increased markedly with
price level, but was fairly constant when
expressed as a percentage of price.
Often reference point effects underlie
sunk-cost fallacies and failures to treat opportunity
costs as being real. As an illustration,
consider the following actual example
offered by Richard Thaler:
Mr. R bought a case of good wine in the late
50's for about $5 a bottle. A few years later
his wine merchant offered to buy the wine back
for $100 a bottle. He refused, although he
never paid more than $35 for a bottle of wine
[1980, p. 431.
Although such behavior could be rationalized
through income effects and transaction
costs, it probably involves a psychological
difference between opportunity
costs and actual out-of-pocket costs.
Finally, there is a fifth major factor that
underlies EU violations, namely the psychology
of probability judgments (Hogarth,
1975a). We shall briefly touch on
three aspects: (1)the impact of objective
(or stated) probabilities on choice, (2)
quantitative expressions of degrees of beliefs
through subjective probabilities, and
(3)probabilistic reasoning, particularly inference.
A general finding of subjective expected
utility research is that subjective probabilities
relate non-linearly to objective ones
(e.g., Edwards, 1953; 1954a). Typically,
the subjective probability curves overweigh
low probabilities and underweigh
high ones (Wayne Lee, 1971, p. 61). For
example, Menahem Yaari (1965) explained
the acceptance of actuarially unfair
gambles as resulting from optimism
regarding low-probability events, as opposed
to convexities in the utility function.
Although the evidence is inconclusive as
to the general nature of probability transformations,
particularly with respect to its
stability across tasks and its dependence
on outcomes (Richard Rosett, 1971), R. W.
Marks, 1951, Francis Irwin, 1953, and Slo-
vic, 1966 found that subjective probabilities
tend to be higher as outcomes become
more desirable (i.e., a type of wishful
thinking). Furthermore, subjective "probabilities"
often violate mathematical properties
of probability (Kahneman and Tversky,
1979). In such cases we refer to them
as decision weights w(p),as noted earlier.
In expressing degrees of confidence
probabilistically, other biases are encountered.
A robust result is that people are
generally overconfident. For instance, if
one were to check out all instances where
a person claimed 90 percent confidence
about the occurrence of events, typically
that person would be correct only about
80 percent of the time (Lichtenstein, et
al., 1981). This same bias manifests itself
in subjective confidence intervals, which
are typically too tight (M. Alpert and
RaifFa, 1981). Many biases in subjective
probability judgments stem from the type
of heuristics (i.e., simplifying rules) people
use to estimate relative likelihoods. For
instance, a doctor may judge the likelihood
of a patient having disease A versus
B solely on the basis of the similarity of
the symptoms to textbook stereotypes of
these diseases. This so-called representativeness
heuristic, however, ignores possible
differences in the a priori probabilities
of these diseases (Kahneman and Tversky,
1972). Another common heuristic is estimation
on the basis of availability (Tversky
and Kahneman, 1973). In judging the
chances of dying from a car accident versus
lung cancer, people may base their
estimates solely on the frequencies with
which they hear of them. Due to unrepresentative
news coverage in favor of car
accidents, these estimates will be systematically
biased (Lichtenstein et al., 1978).
Finally, people suffer from a serious hindsight
bias resulting in suboptimal learning.
Events that did (not) happen appear in
retrospect more (less) likely than they did
before the outcome was known. Baruch
Fischhoff (1975) explains this phenome-