Heuristics & Biases
MAR 3053
February 28, 2012
The use and
misuse of affect,
availability,
representativeness, and anchors
PART 1: HEURISTICS & INTUITIVE
JUDGMENT
Two systems of reasoning
System 1
System 2
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“Intuitive”
Automatic
Effortless
Rapid & parallel
Concrete
Associative
“Reflective”
Controlled
Effortful
Slow & often serial
May be abstract
Rule-based
Which bet would you choose?
1 in 10
9 in 100
Who chooses the large box?
Percentage of participants choosing the box with greater # of total balls
(odds with small box = 10%; odds with large box = value shown on x-axis)
What is a heuristic?
• “Mental shortcut” used in judgment and
decision making
– Essential for living in an uncertain world
– But they can lead to faulty beliefs and suboptimal
decisions
– By looking at errors and biases, we can learn how
people are reasoning under uncertainty
Two types of heuristics
• Special purpose heuristics – use restricted to
specific domains
– Height as a guide for ability as basketball player
– # of publications as guide for quality as an
academic
• General use heuristics
– Affect
– Availability
– Representativeness (similarity)
The affect heuristic
• ## migrating birds die each year by drowning
in uncovered oil ponds, which the birds
mistake for bodies of water. Covering the
ponds with nets could prevent these deaths.
How much money would you be willing to pay
to provide the needed nets?
• 2,000 birds -- $80
• 20,000 birds -- $78
• 200,000 birds -- $88
The identifiable victim effect
• “A death of a single Russian solder is a tragedy.
A million deaths is a statistic.” – Joseph Stalin
Affect
• Judgments of life happiness:
• People asked 2 questions:
– 1) How satisfied are you with your life these days?
– 2) How many dates have you had in the last month?
• Correlation = -.12
• Another group asked in opposite order – 2), then
1)
• Correlation = .66
Strack et al., 1993
The availability heuristic
• Making judgments
about the frequency or
likelihood of an event
based on the ease with
which evidence or
examples come to mind
– Example: Category size
Kansas?
Nebraska?
Availability
• Egocentric allocations of responsibility:
“Overclaiming”
• People claim more responsibility for collective
endeavors than is logically possible
• Self-allocations sum to more than 100%
• Why? Because one’s own contributions are
more available than those of others
Availability
• Experimental evidence
• Married couples asked to allocate responsibility
for:
– Positive events: Making breakfast, planning activities,
shopping for family, making important decisions
– Negative events: Causing arguments, causing messes,
irritating spouse
• Results:
– Overclaiming occurred for 16 of 20 activities
– Equivalent overclaiming for positive and negative
events
Ross & Sicoly, 1979; Kruger & Gilovich, 1999
Availability
• What is availability? Two possibilities:
– 1. Number – amount of information generated
– 2. Ease – the ease with which information can be
generated
• Iconic study teased them apart:
– Participants were asked to evaluate their own
assertiveness…
– By generating either 6 (easy) or 12 (hard)
examples of assertiveness or unassertiveness
Availability: number versus ease
Moral: Ease influences judgments sometimes in spite of number
Schwarz et al., 1991
Representativeness
• Determining class inclusion or likelihood by
similarity:
– A member ought to resemble the overall category
– An effect ought to resemble or be similar to the cause
– An outcome ought to resemble the process that
produced it
• Like goes with like
• Often easier to assess similarity than probability
– Does he look like an engineer?
– Does it look like it could cause a clogged artery?
– Does it look like a random sequence?
Representativeness
• Leads to several classic judgment errors
– Conjunction fallacy
– Misperceiving randomness
– Regression fallacy
The Linda problem
• 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 criminal justice, and also participated in antinuclear demonstrations.
• Rank likelihood that Linda is:
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A teacher in elementary school
Active in the feminist movement
A member of the League of Women Voters
A bank teller
An insurance salesperson
A bank teller and active in the feminist movement
The Linda problem
• Class data (rankings—lower numbers mean
more likely):
• Active in the feminist movement:
• A bank teller:
• Active in feminist movement a bank teller:
Representativeness: Conjunction
fallacy
• Judging the conjunction of two events to be
more probable than one of the constituent
elements
Bank tellers
Feminists
P(A & B) >/ P(A) or P(B)
Conjunction fallacy
• How much would you be willing to pay for a
new insurance policy that would cover
hospitalization for:
• 1. Any disease or accident
– Mean = $89.10
• 2. Any reason
– Mean = $41.53
Johnson et al., 1993
Conjunction fallacy
• How much would you be willing to pay for
flight insurance (1 flight to London) that
covers death due to:
• 1. Any act of terrorism
– Mean = $14.12
• 2. Any reason
– Mean = $12.03
Johnson et al., 1993
Representativeness: Randomness
• Effects should resemble the process that
produced them
• If something is random, it should look random
• What does random look like?
– HTHHHTTTTHTHHTTTHHHTH
– HTHTHTTTHHTHTHTTHHHTH
The hot hand
• “If I’m on, I find that confidence just
builds…you feel nobody can stop you. It’s
important to hit that first one, especially if it’s
a swish. Then you hit another, and…you feel
like you can do anything.”
– --Lloyd Free (a.k.a. World B. Free)
The hot hand
• The belief that success breeds success, and
failure breeds failure
• 100 basketball fans…
– 91% thought player has a better chance of making a
shot after having just made his last two or three shots
than he does after having just missed his last two or
three shots
– Given a player who makes 50% of his shots, subjects
thought that shooting percentage would be…
• 61% after having just made a shot
• 42% after having just missed a shot
– 84% thought that it’s important to pass the ball to
someone who has just made several shots in a row
Gilovich, Vallone, & Tversky, 1985
The hot hand
• Calculate probability of making a shot after
missing previous 1, 2, or 3 shots and after
making previous 1, 2, or 3 shots
Gilovich, Vallone, & Tversky, 1985
What the hot hand results mean
• “The independence between successive shots, of
course, does not mean that basketball is a game
of chance rather than skill, nor should it render
the game less exciting to play, watch, or analyze.
It merely indicates that the probability of a hit is
largely independent of the outcome of previous
shots, although it surely depends on other
parameters such as skill, distance to the basket,
and defensive pressure…The availability of
plausible explanations may contribute to the
erroneous belief that the probability of a hit is
greater following a hit than following a miss.”
– –Gilovich et al., 1985, pp.312-313
Regression to the mean
The SI jinx
The SI jinx
• In sports (the SI jinx, the sophomore slump,
rehiring the interim manager, etc.)
• In education (the illusory superiority of
punishment over reward)
• In medicine (why it’s so easy to believe that a
worthless “remedy” really works)
• In politics (be careful about taking office
during an economic boom or a drop in crime)
Overconfidence
and its causes
PART 2: BIASES
Overconfidence in social predictions
• Would the target person…
– Prefer to subscribe to Playboy or the New York Review of
Books?
– Describe his/her lecture notes as neat or messy?
– Say s/he would pocket or turn in $5 found on the ground?
– Object when the experimenter referred to him/her by the
wrong name?
– Comb his/her hair before posing for a photograph in the
lab?
• How confident are you in your answer (50-100%)?
• Mean confidence: 75.7%
• Mean accuracy: 60.8%
– When 100% confident, accuracy = 78.5%!
Dunning et al., 1990
Overconfidence in self predictions
• Will you…
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Visit San Francisco more than 3 times this year?
Participate in the dorm play?
Drop a course?
Question your decision to attend Stanford?
Become best friends with your roommate?
Visit a friend more than 100 miles away?
Get a new boy/girlfriend?
• Overall confidence: 82.3%
• Overall accuracy: 68.2%
– When participants were 100% confident, they were
correct only 77.4% of the time!
Vallone et al., 1990
Causes of overconfidence
• Hindsight bias
• Motivated and non-motivated confirmatory
thinking
– Confirmation bias
– Wishful thinking
• Naïve realism
Naïve realism
• You drive up to San Francisco with friends to
celebrate the end of the quarter. The plans
include dinner and then some entertainment
afterward.
– How much money will you personally spend on the
dinner?
• You receive a telephone call from a survey firm.
You initially agree to answer some questions.
There is a long series of questions
– How many minutes will you spend answering
questions before you end the call?
Griffin, Dunning, & Ross, 1990
Naïve realism
• Three conditions:
– Control condition: Confidence intervals simply
given a second time
– “Assumers” condition: Asked to assume that their
image of the situation was, in fact, correct in all
details
– Multiple construal condition: Asked to describe
several alternative ways the situation they would
be in could turn out
Griffin, Dunning, & Ross, 1990
Naïve realism
Griffin, Dunning, & Ross, 1990
Summary
• Engage “System 2”
– Learn the common errors that people make in our
uncertain world
• They rely too much on affect, availability and representativeness
• They’re overconfident in their decisions
– Take a skeptical mindset even when you like an initial
judgment
• Don’t be an “assumer”
– Invoke an audience to which you need to justify your
thinking
– Next time: What is construal?