Transcript Judgment and Decisions

Judgment and Decisions
Outline
• Heuristics
– Representativeness
– Availability
– Anchoring
• Errors & biases
–
–
–
–
–
Base rate neglect
Gambler’s fallacy
Conjunction fallacy
Illusory correlations
Confirmation bias
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 …”
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.
50%
50%
as
si
cs
fa
sc
ho
la
r
rm
er
1. farmer
2. Classics scholar
Why?
similarity: he sounds like a classics scholar
Michael has been randomly selected for an interview.
Do you suppose that Michael is:
1. employed
2. unemployed
50%
50%
ed
em
pl
oy
un
em
pl
o
ye
d
Why?
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
- Gambler’s Fallacy
- stereotypes are sometimes incorrect
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.
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: People’s tendency to neglect the overall
frequency of an event when predicting its likelihood.
More likely to be a green car. Do
you agree?
o
50%
N
s
50%
Ye
1. Yes
2. No
Suppose the witness were to identify all the cabs in the city...
1000 blue cabs
What the witness
would report
900 “blue”
100 “green”
50 green cabs
5 “blue”
45 “green”
“green” answers
are more often
wrong than right!
(100/145 are wrong)
In this case, the base rate information overwhelms the
diagnostic information.
Base rate neglect has real world consequences...
Suppose mammograms are 85% likely to detect breast
cancer, if it’s 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?
What’s really
there
cancer present
cancer absent
Mammogram Indicates
Cancer
No Cancer
Total
850
9,900
150
89,100
1,000
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 wise…they 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.
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
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
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 person’s 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%.
The Gambler’s 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
The Gambler’s 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’
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?
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:
tiv
.
..
lle
r
ac
nd
lle
ra
te
an
k
a bank teller and active in
the feminist movement
50%
te
2.
50%
ba
nk
a bank teller
a
1.
Conjunction fallacy
bank teller
feminist
bank teller
feminist
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.
Hooray for psychology!!!
College Helps...
Failure to understand regression to
the mean
• Israeli flight instructors
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
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_’
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?”)
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?
Availability heuristic:
one last example
• Write:
– 2 things that are bad about Diego’s class
– 15 things that are bad about Diego’s class
• Evaluate Diego as an instructor
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.
Anchoring: Example
“10”
“65”
“What is the proportion of
African nations in the UN?
Answer: ‘25%’
“What is the proportion of
African nations in the UN?
Answer: ‘45%’
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 --> Illusory Correlations
When subjects observed data without preconceptions...
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
Illusory correlation: Possible Mechanisms
Confirmation bias. Tendency to notice and remember
evidence that confirms our preconceptions.
Data consistent with one’s theories are more easily retrieved.
This increased availability biases our judgment.
Outline
• Heuristics
– Representativeness
– Availability
– Anchoring
• Errors & biases
–
–
–
–
–
Base rate neglect
Gambler’s fallacy
Conjunction fallacy
Illusory correlations
Confirmation bias