The representativeness heuristic is used when making judgments about the
probability of an event under uncertainty. It is one of a group of
heuristics proposed by psychologists Amos Tversky and Daniel Kahneman in the
early 1970s. Heuristics are described as "judgmental shortcuts that generally get
us where we need to go – and quickly – but at the cost of occasionally sending
us off course." Heuristics are useful because they use effort-reduction and
simplification in decision-making. Tversky and Kahneman defined
representativeness as "the degree to which [an event] is similar in essential
characteristics to its parent population, and reflects the salient
features of the process by which it is generated". When people rely on
representativeness to make judgments, they are likely to judge wrongly because
the fact that something is more representative does not actually make it
more likely. The representativeness heuristic is simply described as
assessing similarity of objects and organizing them based around the
category prototype. This heuristic is used because it is an easy computation.
The problem is that people overestimate its ability to accurately predict the
likelihood of an event. Thus, it can result in neglect of relevant base rates
and other cognitive biases. Determinants of representativeness
Certain factors of the judgment or decision to be made make the use of the
representativeness heuristic more likely.
= Similarity= When judging the representativeness of a
new stimulus/event, people usually pay attention to the degree of similarity
between the stimulus/event and a standard/process. It is also important
that those features be salient. Nilsson, Juslin, and Olsson found this to be
influenced by the exemplar account of memory so that new instances were
classed as representative if highly similar to a category as well as if
frequently encountered. Several examples of similarity have been described in the
representativeness heuristic literature. Research has focused on medical beliefs.
People often believe that medical symptoms should resemble their causes or
treatments. For example, people have long believed that ulcers were caused by
stress, due to the representativeness heuristic, when in fact bacteria cause
ulcers. In a similar line of thinking, in some alternative medicine beliefs
patients have been encouraged to eat organ meat that corresponds to their
medical disorder. Use of the representativeness heuristic can be seen
in even simpler beliefs, such as the belief that eating fatty foods makes one
fat. Even physicians may be swayed by the representativeness heuristic when
judging similarity, in diagnoses, for example. The researcher found that
clinicians use the representativeness heuristic in making diagnoses by judging
how similar patients are to the stereotypical or prototypical patient
with that disorder. = Randomness=
Irregularity and local representativeness affect judgments of
randomness. Things that do not appear to have any logical sequence are regarded
as representative of randomness and thus more likely to occur. For example,
THTHTH as a series of coin tosses would not be considered representative of
randomly generated coin tosses as it is too well ordered.
Local representativeness is an assumption wherein people rely on the
law of small numbers, whereby small samples are perceived to represent their
population to the same extent as large samples. A small sample which appears
randomly distributed would reinforce the belief, under the assumption of local
representativeness, that the population is randomly distributed. Conversely, a
small sample with a skewed distribution would weaken this belief. If a coin toss
is repeated several times and the majority of the results consists of
"heads", the assumption of local representativeness will cause the
observer to believe the coin is biased toward "heads".
Tversky and Kahneman's classic studies = Tom W.=
In a study done in 1973, Kahneman and Tversky divided their participants into
three groups: "Base-rate group", which was given the
instructions: "Consider all the first-year graduate students in the U.S.
today. Please write down your best guesses about the percentage of students
who are now enrolled in the following nine fields of specialization." The nine
fields given were business administration, computer science,
engineering, humanities and education, law, library science, medicine, physical
and life sciences, and social science and social work.
"Similarity group", who were given a personality sketch. "Tom W. is of high
intelligence, although lacking in true creativity. He has a need for order and
clarity, and for neat and tidy systems in which every detail finds its
appropriate place. His writing is rather dull and mechanical, occasionally
enlivened by somewhat corny puns and by flashes of imagination of the sci-fi
type. He has a strong drive for competence. He seems to feel little
sympathy for other people and does not enjoy interacting with others.
Self-centered, he nonetheless has a deep moral sense." The participants in this
group were asked to rank the nine areas listed in part 1 in terms of how similar
Tom W. is to the prototypical graduate student of each area.
"Prediction group", who were given the personality sketch described in 2, but
were also given the information "The preceding personality sketch of Tom W.
was written during Tom's senior year in high school by a psychologist, on the
basis of projective tests. Tom W. is currently a graduate student. Please
rank the following nine fields of graduate specialization in order of the
likelihood that Tom W. is now a graduate student in each of these fields."
The judgments of how likelihood were much closer with the judgments of
similarity than with the estimated base rates. The findings supported the
authors' predictions that people make predictions based on how representative
something is, than based on relative base rate information. For example, more
than 95% of the participants said that Tom would be more likely to study
computer science than education or humanities, when there were much higher
base rate estimates for education and humanities than computer science.
= The taxicab problem= In another study done by Tversky and
Kahneman, subjects were given the following problem:
A cab was involved in a hit and run accident at night. Two cab companies,
the Green and the Blue, operate in the city. 85% of the cabs in the city are
Green and 15% are Blue. A witness identified the cab as Blue.
The court tested the reliability of the witness under the same circumstances
that existed on the night of the accident and concluded that the witness
correctly identified each one of the two colors 80% of the time and failed 20% of
the time. What is the probability that the cab
involved in the accident was Blue rather than Green knowing that this witness
identified it as Blue? Most subjects gave probabilities over
50%, and some gave answers over 80%. The correct answer, found using Bayes'
theorem, is lower than these estimates: There is a 12% chance of the witness
correctly identifying a blue cab. There is a 17% chance of the witness
incorrectly identifying a green cab as blue.
There is therefore a 29% chance the witness will identify the cab as blue.
This results in a 41% chance that the cab identified as blue is actually blue.
Representativeness is cited in the similar effect of the gambler's fallacy,
the regression fallacy and the conjunction fallacy.
Biases attributed to the representativeness heuristic
= Base rate neglect and base rate fallacy=
The use of the representativeness heuristic will likely lead to violations
of Bayes' Theorem. Bayes' Theorem states:
However, judgments by representativeness only look at the resemblance between the
hypothesis and the data, thus inverse probabilities are equated:
As can be seen, the base rate P(H) is ignored in this equation, leading to the
base rate fallacy. A base rate is a phenomenon’s basic rate of incidence.
The base rate fallacy describes how people do not take the base rate of an
event into account when solving probability problems. This was
explicitly tested by Dawes, Mirels, Gold and Donahue who had people judge both
the base rate of people who had a particular personality trait and the
probability that a person who had a given personality trait had another one.
For example, participants were asked how many people out of 100 answered true to
the question "I am a conscientious person" and also, given that a person
answered true to this question, how many would answer true to a different
personality question. They found that participants equated inverse
probabilities even when it was obvious that they were not the same.
A medical example is described by Axelsson. Say a doctor performs a test
that is 99% accurate, and you test positive for the disease. However, the
incidence of the disease is 1/10,000. Your actual chance of having the disease
is 1%, because the population of healthy people is so much larger than the
disease. This statistic often surprises people, due to the base rate fallacy, as
many people do not take the basic incidence into account when judging
probability. Research by Bar-Hillel suggests that perceived relevancy of
information is vital to base-rate neglect: base rates are only included in
judgments if they seem equally relevant to the other information.
Some research has explored base rate neglect in children, as there was a lack
of understanding about how these judgment heuristics develop. The authors
of one such study wanted to understand the development of the heuristic, if it
differs between social judgments and other judgments, and whether children
use base rates when they are not using the representativeness heuristic. The
authors found that the use of the representativeness heuristic as a
strategy begins early on and is consistent. The authors also found that
children use idiosyncratic strategies to make social judgments initially, and use
base rates more as they get older, but the use of the representativeness
heuristic in the social arena also increase as they get older. The authors
found that, among the children surveyed, base rates were more readily used in
judgments about objects than in social judgments. After that research was
conducted, Davidson was interested in exploring how the representativeness
heuristic and conjunction fallacy in children related to children’s
stereotyping. Consistent with previous research, children based their responses
to problems off of base rates when the problems contained nonstereotypic
information or when the children were older. There was also evidence that
children commit the conjunction fallacy. Finally, as students get older, they
used the representativeness heuristic on stereotyped problems, and so made
judgments consistent with stereotypes. There is evidence that even children use
the representativeness heuristic, commit the conjunction fallacy, and disregard
base rates. Research suggests that use or neglect of
base rates can be influenced by how the problem is presented, which reminds us
that the representativeness heuristic is not a "general, all purpose heuristic",
but may have many contributing factors. Base rates may be neglected more often
when the information presented is not causal. Base rates are used less if
there is relevant individuating information. Groups have been found to
neglect base rate more than do individuals. Use of base rates differs
based on context. Research on use of base rates have been inconsistent, with
some authors suggesting a new model is necessary.
= Conjunction fallacy= A group of undergraduates were provided
with a description of Linda, modeled to be representative of an active feminist.
Then participants were then asked to evaluate the probability of her being a
feminist, the probability of her being a bank teller, or the probability of being
both a bank teller and feminist. Probability theory dictates that the
probability of being both a bank teller and feminist must be less than or equal
to the probability of being either a feminist or a bank teller. A conjunction
cannot be more probable than one of its constituents. However, participants
judged the conjunction as being more probable than being a bank teller alone.
Some research suggests that the conjunction error may partially be due
to subtle linguistic factors, such as inexplicit wording or semantic
interpretation of "probability". The authors argue that both logic and
language use may relate to the error, and it should be more fully
investigated. = Disjunction fallacy=
From probability theory the disjunction of two events is at least as likely as
either of the events individually. For example, the probability of being either
a physics or biology major is at least as likely as being a physics major, if
not more likely. However, when a personality description seems to be very
representative of a physics major over a biology major, people judge that it is
more likely for this person to be a physics major than a natural sciences
major. Evidence that the representativeness
heuristic may cause the disjunction fallacy comes from Bar-Hillel and Neter.
They found that people judge a person who is highly representative of being a
statistics major as being more likely to be a statistics major than a social
sciences major, but they do not think that he is more likely to be a Hebrew
language major than a humanities major. Thus, only when the person seems highly
representative of a category is that category judged as more probable than
its superordinate category. These incorrect appraisals remained even in
the face of losing real money in bets on probabilities.
= Insensitivity to sample size= = Misconceptions of chance and gambler's
fallacy= = Regression fallacy=
See also Affect heuristic
Attribute substitution Availability heuristic
List of biases in judgment and decision-making
Extension neglect References
= Works by Kahneman and Tversky= Tversky, Amos; Kahneman, Daniel. "Belief
in the law of small numbers". Psychological Bulletin 76: 105–110.
doi:10.1037/h0031322. Kahneman, Daniel; Tversky, Amos.
"Subjective probability: A judgment of representativeness". Cognitive
Psychology 3: 430–454. doi:10.1016/0010-0285(72)90016-3.
Kahneman, Daniel; Tversky, Amos. "On the psychology of prediction". Psychological
Review 80: 237–251. doi:10.1037/h0034747.
Tversky, Amos; Kahneman, Daniel. "Judgment under Uncertainty: Heuristics
and Biases". Science 185: 1124–1131. doi:10.1126/science.185.4157.1124. PMID
17835457. Tversky, Amos; Kahneman, Daniel.
"Evidential Impact of Base Rates". In Kahneman, Daniel; Slovic, Paul; Tversky,
Amos. Judgment Under Uncertainty: Heuristics and Biases. Cambridge
University Press. pp. 153–163. ISBN 978-0-521-28414-1.
Tversky, Amos; Kahneman, Daniel. "Extensional versus intuitive reasoning:
The conjunction fallacy in probability judgment.". Psychological Review 90:
293–315. doi:10.1037/0033-295X.90.4.293. = General references=
Baron, Jonathan. Thinking and Deciding. Cambridge University Press. ISBN
978-0-521-65972-7. Plous, Scott. The Psychology of Judgment
and Decision Making. McGraw-Hill Education. ISBN 978-0-07-050477-6.
External links Powerpoint presentation on the
representativeness heuristic