Judgment under Uncertainty: Heuristics and Biases

Judgment under Uncertainty:
Heuristics and Biases
Biases in judgments reveal some heuristics of
thinking under uncertainty.
Amos Tversky and Daniel Kahneman
Many decisions are based on beliefs
concerning the likelihood of uncertain
events such as the outcome of an election, the guilt of a defendant, or the
future value of the dollar. These beliefs
are usually expressed in statements such
as “I think that . . . ,” “chances are
. . . ,” “it is unlikely that . . . ,” and
so forth. Occasionally, beliefs concerning uncertain events are expressed in
numerical form as odds or subjective
probabilities. What determines such beliefs? How do people assess the probability of an uncertain event or the
value of an uncertain quantity? This
article shows that people rely on a
limited number of heuristic principles
which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations.
In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
The subjective assessment of probability resembles the subjective assessment of physical quantities such as
distance or size. These judgments are
all based on data of limited validity,
which are processed according to heuristic rules. For example, the apparent
distance of an object is determined in
part by its clarity. The more sharply the
object is seen, the closer it appears to
be. This rule has some validity, -because
in any given scene the more distant
objects are seen less sharply than nearer
objects. However, the reliance on this
rule leads to systematic errors in the
estimation of distance. Specifically, distances are often overestimated when
visibility is poor because the contours
of objects are blurred. On the other
hand, distances are often underestiThe authors are members of the department of
psychology at the Hebrew University, Jerusalem,
mated when visibility is good because
the objects are seen sharply. Thus, the
reliance on clarity as an indication of
distance leads to common biases. Such
biases are also found in the intuitive
judgment of probability. This article
describes three heuristics that are employed to assess probabilities and to
predict values. Biases to which these
heuristics lead are enumerated, and the
applied and theoretical implications of
these observations are discussed.
Many of the probabilistic questions
with which people are concerned belong
to one of the following types: What is
the probability that object A belongs to
class B? What is the probability that
event A originates from process B?
What is the probability that process B
will generate event A? In answering
such questions, people typically rely on
the representativeness heuristic, in
which probabilities are evaluated by the
degree to which A is representative of
B, that is, by the degree to which A
resembles B. For example, when A is
highly representative of B, the probability that A originates from B is judged
to be high. On the other hand, if A is
not similar to B, the probability that A
originates from B is judged to be low.
For an illustration of judgment by
representativeness, consider an individual who has been described by a
former neighbor as follows: “Steve is
very shy and withdrawn, invariably
helpful, but with little interest in people, or in the world of reality. A meek
and tidy soul, he has a need for order
and structure, and a passion for detail.”
How do people assess the probability
that Steve is engaged in a particular
occupation from a list of possibilities
(for example, farmer, salesman, airline
pilot, librarian, or physician)? How do
people order these occupations from
most to least likely? In the representativeness heuristic, the probability that
Steve is a librarian, for example, is
assessed by the degree to which he is
representative of, or similar to, the
stereotype of a librarian. Indeed, research with problems of this type has
shown that people order the occupations by probability and by similarity
in exactly the same way (1). This approach to the judgment of probability
leads to serious errors, because similarity, or representativeness, is not influenced by several factors that should
affect judgments of probability.
Insensitivity to prior probability of
outcomes. One of the factors that have
no effect on representativeness but
should have a major effect on probability is the prior probability, or base-rate
frequency, of the outcomes. In the case
of Steve, for example, the fact that
there are many more farmers than librarians in the population should enter
into any reasonable estimate of the
probability that Steve is a librarian
rather than a farmer. Considerations of
base-rate frequency, however, do not
affect the similarity of Steve to the
stereotypes of librarians and farmers.
If people evaluate probability by representativeness, therefore, prior probabilities will be neglected. This hypothesis
was tested in an experiment where prior
probabilities were manipulated (1).
Subjects were shown brief personality
descriptions of several individuals, allegedly sampled at random from a
group of 100 professionals-engineers
and lawyers. The subjects were asked
to assess, for each description, the probability that it belonged to an engineer
rather than to a lawyer. In one exper

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