Judgment and Decisions

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Judgment and Decisions
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Outline

• Heuristics
• Representativeness
• Availability
• Anchoring

• Errors biases
• Base rate neglect
• Gamblers fallacy
• Conjunction fallacy
• Illusory correlations
• Confirmation bias

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Heuristic - a rule of thumb for judgment and
decision-making - it takes into account only a
portion of the available evidence - it allows
for fast and efficient decision-making, but - it
is vulnerable to error. Algorithm -
guarantees the correct answer - inefficient
(computationally expensive) Judgment how
likely is that ? Decision-Making (Choice)
should you take a coupon for 200 or 100 in
cash, given that
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William has been randomly selected for an
interview. From the interview, the following
personal info was revealed William is a short,
shy man. He has a passion for poetry, and loves
strolling through art museums. As a child, he was
often bullied by his classmates.

1. farmer
2. Classics scholar

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Why?

• similarity he sounds like a classics scholar

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Michael has been randomly selected for an
interview.Do you suppose that Michael is

1. employed
2. unemployed

Why?
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The Representativeness Heuristic
The tendency to judge an event as likely if it
represents the typical features of its
category. (individual is similar to the
prototype) Why is it useful? - Typical features
often are the most frequent ones Why is it
sometimes misleading? - It fails to account
for - prior odds - Base Rate Neglect -
Conjunction Fallacy - random process -
Gamblers Fallacy - stereotypes are sometimes
incorrect
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Base Rate Some things are very frequent (flu),
others are quite infrequent (mad cow
disease) Base Rate Neglect tendency to neglect
the overall frequency of an event when predicting
its likelihood.
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Base Rate Neglect Example
A single witness is found for a hit and run
accident involving a taxi cab. There are 2
cab companies in this town.  A huge blue cab
company (with 1000 cars active at a time) and,
A small green cab company (with 50 cars active at
a time). The witness believes the cab was
green. Subsequent experiments show that this
person is 90 accurate in determining the color
of cabs. Is it more likely that the cab was
blue or green? Base Rate Neglect Peoples
tendency to neglect the overall frequency of an
event when predicting its likelihood.
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More likely to be a green car. Do you agree?

1. Yes
2. No

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Suppose the witness were to identify all the cabs
in the city...
What the witness would report
1000 blue cabs
900 blue
100 green
green answers are more often wrong than
right! (100/145 are wrong)
50 green cabs
5 blue
45 green
In this case, the base rate information
overwhelms the diagnostic information.
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Base rate neglect has real world consequences...
Suppose mammograms are 85 likely to detect
breast cancer, if its really there (hit rate),
and 90 likely to return a negative result if
there is no breast cancer (correct rejection
rate). Suppose we are testing a patient
population with an overall likelihood of cancer
of 1. If the mammogram detects cancer, what are
the odds that the patient really has cancer?
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Mammogram Indicates Cancer No Cancer
Total cancer present 850 150
1,000 cancer absent 9,900 89,100
99,000 In this case, when the mammogram
indicates the presence of cancer, there is an
850/10,750 likelihood that the patient actually
has cancer (only about an 8 chance). While
positive results on a mammogram surely indicate
that more tests would be wisethey should be
viewed in the context of the overall probability
of the disease they are testing for. Studies
have shown that doctors have the same base rate
neglect tendencies as the rest of the population.

Whats really there
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Base Rate Neglect Another Example

• From a sample of 30 engineers and 70 lawyers, you
  randomly draw Jack(Base Rate Information)
• Jack is 45 yrs old... He shows no interest in
  political or social issues and spends most of his
  free time on his many hobbies which include...
  mathematical puzzles. (Diagnostic Information)
• How likely is it that Jack is an engineer?
• - Diagnostic and Base Rate information are
  important
• - however, when both are provided, subjects
  ignore the Base rate information and make their
  judgment based exclusively on the Diagnostic
  infromation

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What can help improve the quality of these
kinds of decisions?
--Overt cues increase the likelihood that people
will use probability information. 70 are
lawyers
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Question If a test to detect a disease whose
prevalence is 1/1000 has a false positive rate of
5 percent, what is the chance that a person found
to have a positive result actually has the
disease, assuming that you know nothing about the
persons symptoms or signs? Participants
Students at the Harvard Medical School - 1000
people tested, one has the disease (1/1000). This
should lead to - 50 false positives (5) and 1
hit (assuming perfect sensitivity) - The chance
of having the disease if the result comes
positive is 1/51 (1.96) - This is due to the
very low base rate (1/1000). - Almost half of
the participants responded 95. - The average
answer was 56.
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The Gamblers Fallacy Example Which sequence of
coin tosses is more likely? 1. H T T H H H
T 2. H H H H H H H
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The Gamblers Fallacy the misconception that
prior outcomes can influence the outcome of an
independent probabilistic event. But
why?! Because in the long run heads tails
alternate, so a short run in which heads tails
alternate seems more typical (similar) member of
the category. We wrongly conclude that if
someone got - 10 H in a row, she is cheating - 4
baskets in a row, the player has hot hands
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Streak Shooting

• Hot hand basketball players get hot (91 of
  76ers fans)
• Analysis of 48 76ers home games during 1980-81
  season revealed no basis in fact.
• Measured probability of making shot after
• making 1, 2, or 3 shots.
• missing 1, 2 or 3 shots.
• Found no difference.
• How might the representativeness heuristic
  explain belief in streak shooting?

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Linda is 31 years old, single, outspoken, and
very bright. She majored in philosophy. As a
student, she was deeply concerned with issues of
discrimination and social justice, and also
participated in antinuclear rallies. Which
alternative is more probable. Linda is

1. a bank teller
2. a bank teller and active in the feminist movement

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Conjunction fallacy

• bank teller feminist feminist
• bank teller

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What can help improve the quality of these
kinds of decisions?
-- use Venn diagrams to represent categories.
This significantly reduced cases of conjunctive
fallacy in this group.
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Hooray for psychology!!!
College Helps...
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Failure to understand regression to the mean

• Israeli flight instructors

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The Availability Heuristic Examples
Which household chores do you do more frequently
than your partner? (e.g. washing dishes, taking
out the trash, etc.) - wives report 16/20
chores - husbands report 16/20 chores Ross and
Sicoly (1979) Why? Availability! - I remember
lots of instances of taking out the trash,
washing dishes, but I do not remember lots of
instance of my wife doing it
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The Availability Heuristic Examples
Which is more frequent? Words that begin with
R, or words with R as their third
letter? Why? Availability! - I can come up with
many examples of R_ _ _, but few of _ _ R_
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The Availability Heuristic Tendency to form a
judgment on the basis of information is readily
brought to mind. Why is it useful? - Frequent
events are easily brought to mind (words that
start with X) Why is it sometimes misleading? -
Factors other than frequency can affect ease of
remembering --Ease of Retrieval (the r
example) --Recency of the example
(advertisement, news) -- Familiarity (what of
people go to college?)
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Testing the Availability Heuristic
- Keep frequency invariant - Experimentally
manipulate availability - Measure estimated
frequency (dependent variable) Subjects read a
list of names - 50 of names are male names, the
rest are female - Group A Some male names
famous (Bill Clinton) - Group B Some female
names famous Test Where there more men or
women in the list?
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Availability heuristic one last example

• Write
• 2 things that are bad about Diegos class
• 15 things that are bad about Diegos class
• Evaluate Diego as an instructor

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Anchoring

• Tendency to reach an estimate by beginning with
  an initial guess and altering it based on new
  information.
• In general
• People rely too heavily on the anchor (initial
  value)
• Adjustments are too small
• even when the anchor (reference point) is known
  to be uninformative.

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Anchoring Example
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Illusory Correlations --Does a college education
lead to a higher paying job? -- Are flaws in the
personal arena --sexual escapades, DUI--
correlated with flaws in governing the
country? -- Do small dogs bite more often than
big dogs? The perceived correlation between two
variables is influenced - by the data we observe
- by our personal theories --gt Illusory
Correlations
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When subjects observed data without
preconceptions...
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When subjects had theories about what they would
see.
- The estimates did not show as orderly a
relationship with the data. - The correlation
values were over-estimated!
Scientists are similarly affected by their
theoretical biases
Jennings, Amabile, Ross, 1982
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Illusory correlation Possible Mechanisms
Confirmation bias. Tendency to notice and
remember evidence that confirms our
preconceptions. Data consistent with ones
theories are more easily retrieved. This
increased availability biases our judgment.
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Outline

• Heuristics
• Representativeness
• Availability
• Anchoring

• Errors biases
• Base rate neglect
• Gamblers fallacy
• Conjunction fallacy
• Illusory correlations
• Confirmation bias