Introduction
Key references are
Barberis and Thaler (2003)
Gärling et al. (2009)
Kahneman (2011)
Li Calzi (2008)
Plous (1993)
Rabin (1996) (and later works by the same author)
Shefrin (2007)
Shleifer (2000)
Bibliography Lecture 2 Lecture 3
1.11
Friday, 11 March 2016
6:40 PM
Introduction
Whats in a name?
Much research that has appeared in the media as
research done by behavioral economists, in fact has
been done by psychologists.
For an interesting essay see “Why Behavioural
Economics Is Cool, and I'm Not” by Adam Grant who
is a professor at the Wharton School of the
University of Pennsylvania and the author of “Give
and Take: A Revolutionary Approach to Success”
(Viking Press, 2013).
1.22
Seeing Is Believing!
1.33
Introduction
Making decisions is both tough and risky (see, for
example, the reviews by Rapoport and Wallsten 1972
and Edwards and Fasolo 2001).
Bad decisions can cause damage to a business a
career or your finances, sometimes irreparably.
So where do bad decisions come from?
1.44
Introduction
In many cases, they can be traced back to the way
the decisions were made; the alternatives were not
clearly defined, the right information was not
collected, the costs and benefits were not accurately
weighed.
Sometimes the fault lies not in the decision-making
process but rather in the mind of the decision maker.
The way the human brain works can sabotage our
decisions. Researchers have identified a whole series
of such flaws in the way we think in making decisions.
1.55
Introduction
Shefrin’s (2010) insightful observation is of interest:
“Finance is in the midst of a paradigm shift, from
a neoclassical based framework to a
psychologically based framework. Behavioural
finance is the application of psychology to
financial decision making and financial markets.
Behaviouralising finance is the process of
replacing neoclassical assumptions with behavioural
counterparts. … the future of finance will combine
realistic assumptions from behavioural finance and
rigorous analysis from neoclassical finance.”
1.66
Introduction
If irrational traders cause deviations from a “true” value,
rational traders will often be powerless to do anything about it
(Barberis and Thaler 2003)?
Current examples are the oil price (which is too high) and the
gold or silver prices (where there has been artificial shorting
going on).
In view of this economists turn to the extensive experimental
evidence compiled by cognitive psychologists on the systematic
biases that arise when people form beliefs, and on people’s
preferences.
Fffffffffffff
(Shorting - The sale of a borrowed security, commodity or
fffff
currency with the expectation that the asset will fall in value.)
1.77
Introduction
For a useful link to topics in Daniel Kahneman’s text
see "Thinking, Fast and Slow" Shim Marom. This
provides an overview rather than a detailed analysis.
Also of interest is "Common Flaws With How We
Think" Forbes - 2/11/2014 - Ross Pomeroy. For a
concise overview see Homo economicus – or more like
Homer Simpson? - Schneider 2010 - Deutsche Bank
Research
These are intended as background reading.
1.88
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1.99
1.10
10
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Avoidance
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Avoidance
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1.11
11
Overconfidence
Extensive evidence shows that people are
overconfident in their judgments.
1
Confidence intervals people assign to their
estimates of quantities.
For example, estimating the level of the stock
market in a years time, are far too narrow. Their
98% confidence intervals, for example, include
the true quantity only about 60% of the time
(Alpert and Raifa, 1982).
1.12
12
Overconfidence
Extensive evidence shows that people are
overconfident in their judgments.
2 People are poorly calibrated when estimating
probabilities.
Events they think are certain to occur actually
occur only around 80% of the time, and events
they deem impossible occur approximately 20% of
the time (Fischhof et al. 1977).
1.13
13
Overconfidence
An example from David J. Spiegelhalter and coworkers.
The Great Ormond Street Hospital in London
(GOS) specialises in child diseases and acts as a
regional centre for South East of England.
Whenever a blue baby is born, the paediatrician
telephones GOS and a diagnosis is made. It is
then decided whether or not to send Child to
GOS for treatment.
A Bayesian model has been used for the diagnosis
of congenital heart disease and was derived by
experts.
1.14
14
Overconfidence
Evaluation Of A
Diagnostic Algorithm
For Heart-Disease In
Neonates
Franklin, R.C.G.,
Spiegelhalter, D.J.,
Macartney, F.J. and
Bull, K.
British Medical Journal,
302, 935-939, 1991.
1.15
15
Overconfidence
1.16
16
Overconfidence
On the first day a baby exhibited a mix of
symptoms that the experts said would never
arise together.
The impossible occurred!
1.17
17
Overconfidence
Overconfidence may in part stem from two other
biases (self-attribution and hindsight bias).
3 Self-attribution bias refers to people’s tendency
to ascribe any success they have in some activity
to their own talents, while blaming failure on bad
luck, rather than on their ineptitude.
Doing this repeatedly will lead people to the
pleasing but erroneous conclusion that they are
very talented.
1.18
18
Overconfidence
We like to exploit the luck of others (BPS)
Psychologists have documented the many irrational
ways we think about luck, from the fact we prefer to
make our own choice in gambling games (thus
increasing our sense of control) to our belief in lucky
runs or hot numbers (Wohl and Enzle, 2009).
1.19
19
Overconfidence
Bad luck really can be reversed by touching wood ritual, say
scientists (The Telegraph 2 Oct. 2013).
In five separate experiments, researchers had participants
either tempt fate or not and then engage in an action that
was either avoidant or not. The avoidant actions included
those that were superstitious – like knocking on wood – or
non-superstitious – like throwing a ball.
They found that those who knocked down (away from
themselves) or threw a ball believed that a jinxed negative
outcome was less likely than participants who knocked up
(toward themselves) or held a ball (Zhang et al. 2013).
1.20
20
Overconfidence
For example, investors might become overconfident
after several quarters of investing success (Gervais
and Odean, 2001).
In an experimental asset market where agents trade
one risky asset, Maciejovsky and Kirchler (2002) find
the largest overconfidence towards the end of the
experiment, when the participants gain more
experience and start to rely more heavily on their
(overestimated) knowledge. This finding indicates
that overconfidence may be subject to modifications,
which goes back to the crucial role of clear, rapid
feedback in shaping individual overconfidence levels
(Russo and Schoemaker 1992).
1.21
21
Overconfidence
Overconfidence may in part stem from two other
biases (self-attribution and hindsight bias).
1
Hindsight bias is the tendency of people to
believe, after an event has occurred, that they
predicted it before it happened.
2 If people think they predicted the past better
than they actually did, they may also believe that
they can predict the future better than they
actually can.
1.22
22
Overconfidence
Overconfidence is the tendency to be overly
optimistic, to overestimate one's own abilities, or to
believe their information is more precise than it
really is. In the strip, Dilbert's boss falls victim to
this bias when he assumes that all managers
(presumably including himself) are better than
average, all the while not recognizing Dilbert's
impolite jab at his poor math skills Cartoon (Kramer
2014). For a broad interdisciplinary review see Skala
(2008).
1.23
23
Beliefs – Overconfidence
Self-attribution or Self-Serving Bias
Along the same vein as overconfidence is the selfattribution or self-serving bias. This is when
investors are quick to take credit for portfolio gains,
but just as quick to blame losses on outside factors
like market forces or the Bank of China. Much like an
athlete blaming the referee for a loss, self-serving
bias helps investors avoid accountability. Although
you might feel better by following this bias, you will
be cheating yourself out of a valuable opportunity to
improve your investing intelligence. If you've never
made a mistake in the market, you'll have no reason
to develop better investing skills and your returns
will reflect it.
1.24
24
Certainty
Gigerenzer et al. (2008) report on the illusion of
certainty. Shown are results from face-to-face
interviews conducted in 2006, in which a
representative sample of 1,016 German citizens was
asked: “Which of the following tests are absolutely
certain?”
1. DNA test
vote now!
2. Fingerprint test
higher or lower than those above?
3. HIV test
higher or lower than those above?
4. Mammogram
higher or lower than those above?
5. Horoscope
higher or lower than those above?
1.25
25
Certainty
A large proportion of
the general public
have illusory certainty
about the perfection
of tests, including
HIV
testing
and
mammography.
This
illusion is not simply a
product
of
the
individual mind but
has its historical origins in deterministic medical science.
Today, it is fueled by health messages that claim or
suggest certainty.
1.26
26
1.27
27
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1.28
28
Overconfidence - Avoidance
To reduce the effects of overconfidence in
making estimates, always start by considering the
extremes, the low and high ends of the possible
range of values. This will help you avoid being
anchored by an initial estimate.
Then challenge your estimates of the extremes.
Try to imagine circumstances where the actual
figure would fall below your low or above your
high, and adjust your range accordingly.
Challenge the estimates of your subordinates and
advisers in a similar fashion. They're also
susceptible to overconfidence (Hammond et al.,
1.29
29
1998/2006).
1.30
30
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Overconfidence
Avoidance
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1.31
31
Prudence
Another problem takes the form of overcautiousness, or prudence.
When faced with high-stakes decisions, we tend
to adjust our estimates or forecasts “just to be
on the safe side.”
1.32
32
Prudence
Many years ago, for example, one of the Big
Three U.S. automakers was deciding how many of
a new-model car to produce in anticipation of its
busiest sales season.
The market-planning department, responsible for
the decision, asked other departments to supply
forecasts of key variables such as anticipated
sales, dealer inventories, competitor actions, and
costs.
1.33
33
Prudence
Knowing the purpose of the estimates, each
department slanted its forecast to favour
building more cars; “just to be safe.”
But the market planners took the numbers at face
value and then made their own “just to be safe”
adjustments. Not surprisingly, the number of cars
produced far exceeded demand, and the company
took six months to sell off the surplus, resorting
in the end to promotional pricing (Hammond et al.
1998/2006).
1.34
34
Prudence
Policy makers have gone so far as to codify overcautiousness in formal decision procedures.
An extreme example is the methodology of
“worst-case analysis,” which was once popular in
the design of weapons systems and is still used in
certain engineering and regulatory settings.
Using this approach, engineers designed weapons
to operate under the worst possible combination
of circumstances, even though the odds of those
circumstances actually coming to pass were
infinitesimal.
1.35
35
Prudence
Worst-case analysis added enormous costs with
no practical benefit (in fact, it often backfired by
touching off an arms race), proving that too much
prudence can sometimes be as dangerous as too
little.
1.36
36
Prudence
However, maybe we can be more careful, consider the list of
bridge failures, famously the Tacoma Narrows Bridge
(Galloping Gertie - 7 November 1940).
The Tay Bridge disaster occurred during a violent storm on
28 December 1879 when the first Tay Rail Bridge collapsed
while a train was passing over it from Wormit to Dundee,
killing all aboard. For William McGonagall's poem on this
subject, see The Tay Bridge Disaster.
1.37
37
1.38
38
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1.39
39
Prudence - Avoidance
To avoid the prudence trap, always state your
estimates honestly and explain to anyone who will
be using them that they have not been adjusted.
Emphasize the need for honest input to anyone
who will be supplying you with estimates.
Test estimates over a reasonable range to assess
their impact.
Take a second look at the more sensitive
estimates (Hammond et al. 1998/2006).
1.40
40
1.41
41
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Avoidance
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1.42
42
Recallability
Even if we are neither overly confident nor unduly
prudent, we can still fall into a trap when making
estimates or forecasts.
Because we frequently base our predictions about
future events on our memory of past events, we
can be overly influenced by dramatic events;
those that leave a strong impression on our
memory.
1.43
43
Recallability
We all, for example, exaggerate the probability
of rare but catastrophic occurrences such as
plane crashes because they get disproportionate
attention in the media.
A dramatic or traumatic event in your own life can
also distort your thinking. You will assign a higher
probability to traffic accidents if you have
passed one on the way to work. You will assign a
higher chance of someday dying of cancer
yourself if a close friend has died of the disease.
1.44
44
Recallability
In fact, anything that distorts your ability to
recall events in a balanced way will distort your
probability assessments.
In one experiment, lists of well-known men and
women were read to different groups of people
(Hammond et al. 1998/2006).
1.45
45
Recallability
Unbeknownst to the subjects, each list had an
equal number of men and women, but on some lists
the men were more famous than the women while
on others the women were more famous.
Afterward, the participants were asked to
estimate the percentages of men and women on
each list.
Those who had heard the list with the more
famous men thought there were more men on the
list, while those who had heard the one with the
more famous women thought there were more
women.
1.46
46
Recallability
Corporate lawyers often get caught in the
recallability trap when defending liability suits.
Their decisions about whether to settle a claim or
take it to court usually hinge on their assessments
of the possible outcomes of a trial.
Because the media tend to aggressively publicise
massive damage awards (while ignoring other, far
more common trial outcomes), lawyers can
overestimate the probability of a large award for
the plaintiff. As a result, they offer larger
settlements than are actually warranted
(Hammond et al. 1998/2006).
1.47
47
1.48
48
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1.49
49
Recallability - Avoidance
To minimize the distortion caused by variations in
recallability, carefully examine all your
assumptions to ensure they're not unduly
influenced by your memory.
Get actual statistics whenever possible. Try not
to be guided by impressions (Hammond et al.
1998/2006).
1.50
50
1.51
51
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1.52
52
Optimism And Wishful
Thinking
Most people display unrealistically rosy views of their
abilities and prospects.
They also display a systematic planning fallacy: they
predict that tasks (such as writing survey papers)
will be completed much sooner than they actually are
(Buehler et al. 1994).
Typically, over 90% of those surveyed think they are
above average in such domains as driving skill, ability
to get along with people and sense of humour
(Weinstein 1980).
1.53
53
Optimism And Wishful
Thinking
But maybe they
were right!
Link
The average rate
was 18 per 100,000
people.
1.54
54
Optimism And Wishful
Thinking
Activity to have been won
Men by women,
Women
Driving's battle of the sexes appears
according
Appropriate speed approaching hazards
55%
75%
to a survey.
Stopping safely at amber traffic lights
44%
85%
Negative impact on other drivers
73%
54%
Driving too close to the vehicle in front
27%
4%
Cutting corners when turning
68%
43%
Female drivers outscored males not only in in-car tests but also when
Adequate indication
82%
96%
observed anonymously using one of the UK's busiest junctions - Hyde Park
Adequate use of mirrors
46%
79%
Corner.
Effective observation (e.g. checking blind spot)
82%
71%
But another part of the
survey
- from
- found
only 28%
Staying
within the speed
limit Privilege Insurance
86%
89%
of women reckoned they
were
drivers than men,
13% of men
Appropriate
speedbetter
for the situation
64% with only
64%
thinking women were Steering
superior
behind
the wheel.
/ Control
of the vehicle
100%
96%
Talking
or texting
on the phone
while driving
16% watched at
A sample of 50 drivers
faced
in-car
assessment
while24%200 were
Cutting dangerously in to traffic
Hyde Park Corner. Marked
on 14 different aspects of14%driving, 1%women scored
Causing an obstruction on the road
25%
16%
23.6 points out of a possible
30, while men managed to
chalk up
only 19.8
Total co-efficient (max 30)
19.8
23.6
points.
Women are, after all, better drivers than men - Telegraph - 15 May 2015
1.55
55
Optimism And Wishful
Thinking
The study tested a theoretical model of the
relationship between the Big Five Personality Factors,
aggressive driving and ‘risky driving outcomes’
(accidents, traffic tickets, and license suspension). It
also tested the mediation effect of aggressive driving
in the relationship between the five factor personality
model and risky driving outcomes.
The link between personality, aggressive driving, and
risky driving outcomes – testing a theoretical model
Chraif et al. 2015
1.56
56
Optimism And Wishful
Thinking
The experimental results of Camerer and Lovallo
(1999) confirm the better-than average effect in
the behaviour of most business owners, who forecast
negative returns for an average market participant,
with themselves being an exception to the rule.
1.57
57
1.58
58
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1.59
59
Representativeness
Also known as the Conjunction Fallacy.
Erceg and Galić (2014) in their study explored the
occurrence of the overconfidence bias and the
conjunction fallacy in betting behaviour among
frequent and sporadic bettors and to test whether it
was influenced by the task format (probability vs.
frequencies). Frequent bettors (N = 67) and sporadic
bettors (N = 63) estimated whether the bets on
football games presented to them via an on-line
questionnaire would be successful.
1.60
60
Representativeness
The bets consisted of singles (one match outcomes)
and conjunctions (two matches outcomes), and were
presented either in probability or frequency terms.
Both frequent and sporadic bettors showed similar
levels of the overconfidence bias. However, the
frequent bettors made the conjunction fallacy more
often than the sporadic bettors. The presentation of
the task in the frequency terms significantly
reduced the overconfidence bias in comparison to
the evaluations in probability terms, but left the
conjunction fallacy unaffected.
1.61
61
Representativeness
Kahneman and Tversky (1974) show that when people
try to determine the probability that a data set A
was generated by a model B, or that an object A
belongs to a class B, they often use the
representativeness heuristic.
This means that they evaluate the probability by the
degree to which A reflects the essential
characteristics of B.
1.62
62
Representativeness
Much of the time, representativeness is a helpful
heuristic, but it can generate some severe biases.
The first is base rate neglect.
To illustrate, Kahneman and Tversky present this
description of a person named Linda:
1.63
63
Representativeness
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 anti-nuclear demonstrations.
1.64
64
Representativeness
Asked which of “Linda is a bank teller” (statement A)
and “Linda is a bank teller and is active in the
feminist movement” (statement B) is more likely.
What do you think?
Subjects typically assign a greater probability to B.
This is, of course, impossible.
1.65
65
Representativeness
This is, of course, impossible.
Representativeness provides a simple explanation.
The description of Linda sounds like the description
of a feminist – it is representative of a feminist –
leading subjects to pick B.
1.66
66
Representativeness
Put differently, while Bayes Law says that
Prob(statement B| description) =
Prob(description | statement B) Prob(statement B)
Prob(description)
People apply the law incorrectly, putting too much
weight on Prob(description | statement B), which
captures representativeness, and too little weight on
the base rate, Prob(statement B).
1.67
67
Representativeness
Put differently, a Venn diagram makes the position
clear.
1.68
68
Representativeness
Another explanation of the conjunction fallacy is the
“configural weighted average (CWA) hypothesis”
(Nilsson, 2008; Nilsson, Winman, Juslin and Hansson,
2009). According to the CWA hypothesis,
participants will assess the probability of the
conjunction first by assessing the probability of each
of the components in the conjunction, assigning them
a weight and finally adding them. In other words,
instead of multiplying the probabilities of
components, participants are averaging them,
inevitably making the conjunction fallacy.
1.69
69
Representativeness
For example, the probability of the conjunction “Linda is
a bank teller and is active in the feminist movement” will
be assessed first by assigning greater weight to the less
probable assertion (Linda is a bank teller) and lower
weight to the more probable claim (Linda is active in the
feminist movement), and then by aggregating these
weighted claims. Thus, if a person estimates the
probability of Linda being a bank teller to be 0.2, and
the probability that she is active in the feminist
movement to be 0.8, and then assigns weights of 0.7 to
the less likely and 0.3 to the more likely claim, their
integration of these information would proceed as
1.70
70
follows: Prob = 0.7 × 0.2 + 0.3 × 0.8 = 0.38 Wrong!!
Representativeness
We think a person is more likely to be a member of
some group if that person is similar to a typical
member of that group.
If a man behaves more like a criminal (shifty eyes,
etc.), then we think it is more likely he is a criminal.
Bayes Law of course, captures this simple intuition.
1.71
71
Representativeness
Who
was
fatally
shot?
is the
the
policeman?
ddddddddddddddddddddd
Who
is
criminal?
The Daily Mail
17-9-2013
An unarmed man
Dddddddddddddddd
D(left) seeking
Dhelp after a car
Dcrash was shot
D10 times by the
DCharlotte police
Dofficer who's
Dnow charged in
his death. 1.72
72
Jonathan Ferrell: North Carolina cop Randall Kerrick charged with manslaughter in death of unarmed man
Representativeness
In case you were curious:•Who
Second
North
Carolina
was
fatally
shot?grand jury indicts police officer Randall
is the
the
policeman?
ddddddddddddddddddddd
Who
is
criminal?
Kerrick, 28, who fatally shot unarmed Jonathan Ferrell, 24, ten
times last September
• First jury declined to indict Kerrick on involuntary
manslaughter last week
Dddddddddddddddd
• Investigators say Kerrick shot Ferrell last September 14 as he
D
looked for help after a car crash
• Ferrell's mother says: “I just feel like God's D
will, will be done”
• But Kerrick's attorneys say there was “nothing
D irregular or
improper” about the decision of the first grand
D jury
• Voluntary manslaughter charge carries a prison sentence of up
D
to 11 years
D man as he looked
Second grand jury indicts police officer who fatally shot unarmed
for help after car crash - Daily Mail - 28 January 2014
D
2nd grand jury indicts officer in shooting of ex-FAMU football player - CNN - 28
January 2014
1.73
73
Beliefs – Representativeness
Getting to grips with implicit bias
Implicit attitudes are one of the hottest topics in social
psychology. Now a massive new study directly compares
methods for changing them. The results are both good and
bad for those who believe that some part of prejudice is
our automatic, uncontrollable, reactions to different social
groups.
1.74
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Beliefs – Representativeness
Getting to grips with implicit bias
The implicit association test (IAT) is a simple task you can
complete online at Project Implicit which records the
speed of your responses when sorting targets, such as
white and black faces, into different categories, such as
good and bad. Even people who disavow any prejudiced
beliefs or feelings can have IAT scores which show they
find it easier, for example, to associate white faces with
goodness and black faces with badness – a so called
“implicit bias” (Lai et al. 2014).
1.75
75
Beliefs – Representativeness
Getting to grips with implicit bias
Lai et al. 2014 state
“Many methods for reducing implicit prejudice have been identified, but little
is known about their relative effectiveness. We held a research contest to
experimentally compare interventions for reducing the expression of implicit
racial prejudice. Teams submitted 17 interventions that were tested an
average of 3.70 times each in 4 studies (total N = 17,021), with rules for
revising interventions between studies. Eight of 17 interventions were
effective at reducing implicit preferences for Whites compared with Blacks,
particularly ones that provided experience with counter stereotypical
exemplars, used evaluative conditioning methods, and provided strategies to
override biases. The other 9 interventions were ineffective, particularly ones
that engaged participants with others' perspectives, asked participants to
consider egalitarian values, or induced a positive emotion. The most potent
interventions were ones that invoked high self-involvement or linked Black
people with positivity and White people with negativity. No intervention
consistently reduced explicit racial preferences. Furthermore, intervention
effectiveness only weakly extended to implicit preferences for Asians and
Hispanics.”
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Avoidance
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Representativeness
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Avoidance
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Heuristics
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Sample Size Neglect
Representativeness also leads to another bias,
sample size neglect.
When judging the likelihood that a data set was
generated by a particular model, people often fail to
take the size of the sample into account: after all, a
small sample can be just as representative as a large
one.
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Sample Size Neglect
Six tosses of a coin resulting in three heads and
three tails are as representative of a fair coin as
500 heads and 500 tails are in a total of 1000 tosses.
Representativeness implies that people will find the
two sets of tosses equally informative about the
fairness of the coin, even though the second set is
much more so.
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Sample Size Neglect
Sample size neglect means that in cases where
people do not initially know the data-generating
process, they will tend to infer it too quickly on the
basis of too few data points.
For instance, they will come to believe that a
financial analyst with four good stock picks is
talented
because
four
successes
are
not
representative of a bad or mediocre analyst.
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Sample Size Neglect
It also generates a “hot hand” phenomenon, whereby
sports fans become convinced that a basketball
player who has made three shots in a row is on a hot
streak and will score again, even though there is no
evidence of a hot hand in the data (Gilovich, Valone
and Tversky 1985).
This belief that even small samples will reflect the
properties of the parent population is sometimes
known as the “law of small numbers” (Rabin 2002).
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Sample Size Neglect
Surprised by the Gambler's and Hot Hand Fallacies? A Truth in the Law of
Small Numbers Miller and Sanjurjo 2015
We find a subtle but substantial bias in a standard measure of the
conditional dependence of present outcomes on streaks of past outcomes in
sequential data. The mechanism is driven by a form of selection bias, which
leads to an underestimate of the true conditional probability of a given
outcome when conditioning on prior outcomes of the same kind. The biased
measure has been used prominently in the literature that investigates
incorrect beliefs in sequential decision making - most notably the Gambler's
Fallacy and the Hot Hand Fallacy. Upon correcting for the bias, the
conclusions of some prominent studies in the literature are reversed. The
bias also provides a structural explanation of why the belief in the law of
small numbers persists, as repeated experience with finite sequences can
only reinforce these beliefs, on average.
With discussion and critique.
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Sample Size Neglect
In situations where people do know the datagenerating process in advance, the law of small
numbers leads to a gambler’s fallacy effect.
If a fair coin generates five heads in a row, people
will say that “tails are due”. Since they believe that
even a short sample should be representative of the
fair coin, there have to be more tails to balance out
the large number of heads.
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Sample Size Neglect
The study (Xu and Harvey 2014) is a great example
of how a simple phenomenon – the gambler's fallacy –
can have unpredicted outcomes when studied in a
complex real-world environment. Don't get too
carried away by the rewards of online gambling
however, the paper contains this telling detail: of all
the bets analysed in the study, 178,947 were won and
192,359 were lost - giving overall odds of winning at
0.48. Enough to ensure the betting site's profit
margin, and to suggest that on average you're going
to lose more than you stake. Unless you're lucky.
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Overconfidence
Avoidance
Prudence
Avoidance
Recallability
Avoidance
Optimism And Wishful Thinking
Representativeness
Sample Size Neglect
The Law Of Small Numbers
Conservatism
Belief Perseverance
Anchoring
Avoidance
Confirmatory Bias
Avoidance
Availability Bias
Internalisation
Heuristics
Menu
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The Law Of Small
Numbers
A phenomenon related to the under-use of base
rates is “the law of small numbers” (Tversky and
Kahneman 1971):
People exaggerate how often a small group will
closely resemble the parent population or underlying
probability distribution that generates the group.
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The Law Of Small
Numbers
We expect even small classes of students to contain
very close to the typical distribution of smart ones
and personable ones.
Likewise, we underestimate how often a good
financial analyst will be wrong a few times in a row,
and how often a clueless analyst will be right a few
times in a row.
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The Law Of Small
Numbers
For example, Kahneman and Tversky (1982, p. 44)
asked undergraduates the following question:
A certain town is served by two hospitals. In the
larger hospital about 45 babies are born each day,
and in the smaller hospital about 15 babies are born
each day.
As you know, about 50 percent of all babies are boys.
However, the exact percentage varies from day to
day. Sometimes it may be higher than 50 percent,
sometimes lower.
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The Law Of Small
Numbers
For a period of 1 year, each hospital recorded the
days on which more than 60 percent of the babies
born were boys. Which hospital do you think
recorded more such days?
Twenty-two percent of the subjects said that they
thought that it was more likely that the larger
hospital recorded more such days, and 56% said that
they thought the number of days would be about the
same.
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The Law Of Small
Numbers
Only 22% of subjects answered correctly that the
smaller hospital would report more such days.
This is the same fraction as guessed exactly wrong.
Apparently, the subjects simply did not see the
relevance of the number of childbirths per day.
A large sample is less likely to stray from the 50%, it
will provide the best estimate.
Recall the t test confidence interval.
As n increases, tν and 1/n decrease.
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The Law Of Small
Numbers
But this would not really happen?
The Gates Foundation has spent about $2 billion on
its goal of having 80% of minority and low-income
students graduate from high school college-ready.
The Foundation has mainly supported this through a
“small schools” initiative that breaks existing lowperforming schools into 400-student blocks. The
theory is that these small schools will reduce dropout rates - even in the absence of other major
improvements.
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The Law Of Small
Numbers
Yet, according to Wharton School statistician
Howard Wainer, the foundation may have misread
the numbers when it arrived at its first prescription
for American education. Wainer, who used the
foundation as a case study in his 2009 book,
“Picturing the Uncertain World”, says it seized on
data showing small schools are overrepresented
among the country's highest achievers and started
pouring money into creating small high schools and
subdividing big ones.
Wainer, H.: Picturing the Uncertain World: How to Understand, Communicate,
and Control Uncertainty through Graphical Display. (Paperback)
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The Law Of Small
Numbers
But according to Wainer, adherents overlooked a
troublesome fact: Small schools are overrepresented
among the lowest as well as highest achievers. Why?
Because the smaller a school, the more likely its
overall performance can be skewed by a few good or
bad students.
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The Law Of Small
Numbers
Forum on Education in America - Bill & Melinda Gates
Foundation November 11, 2008
In the first four years of our work with new, small schools,
most of the schools had achievement scores below district
averages on reading and math assessments. In one set of
schools we supported, graduation rates were no better
than the state wide average, and reading and math scores
were consistently below the average. The percentage of
students attending college the year after graduating high
school was up only 2.5 percentage points after five years.
Simply breaking up existing schools into smaller units often
did not generate the gains we were hoping for.
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Overconfidence
Avoidance
Prudence
Avoidance
Recallability
Avoidance
Optimism And Wishful Thinking
Representativeness
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The Law Of Small Numbers
Conservatism
Belief Perseverance
Anchoring
Avoidance
Confirmatory Bias
Avoidance
Availability Bias
Internalisation
Heuristics
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Conservatism
While representativeness leads to an under
weighting of base rates, there are situations where
base rates are over-emphasized relative to sample
evidence.
In an experiment run by Edwards (1968), there are
two urns, one containing 3 blue balls and 7 red ones,
and the other containing 7 blue balls and 3 red ones.
A random draw of 12 balls, with replacement, from
one of the urns yields 4 blues and 8 reds.
What is the probability the draw was made from the
first urn? You guess!
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Conservatism
While the correct answer is 0.97
(0.78×0.34/(0.78×0.34+0.38×0.74)), most people
estimate a number around 0.7, apparently over
weighting the base rate of 0.5.
At first sight, the evidence of conservatism appears
at odds with representativeness. However, there may
be a natural way in which they fit together.
It appears that if a data sample is representative of
an underlying model, then people over weight the
data.
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Conservatism
However, if the data is not representative of any
salient model, people react too little to the data and
rely too much on their priors.
In Edwards’ experiment, the draw of 4 blue and 8
red balls is not particularly representative of either
urn, possibly leading to an over reliance on prior
information.
Mulainathan (2001) presents a formal model that
neatly reconciles the evidence on under weighting
sample information with the evidence on over
weighting sample information.
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Conservatism
Mulainathan (2001) presents a model of human
inference in which people use coarse categories to
make inferences. Coarseness means that rather than
updating continuously as suggested by the Bayesian
ideal, people update change categories only when they
see enough data to suggest that an alternative
category better fits the data. This simple model of
inference generates a set of predictions about
behaviour. The author applies this framework to
produce a simple model of financial markets, where it
produces straight forward and testable predictions
about predictability of returns, co-movement and
volume.
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Overconfidence
Avoidance
Prudence
Avoidance
Recallability
Avoidance
Optimism And Wishful Thinking
Representativeness
Sample Size Neglect
The Law Of Small Numbers
Conservatism
Belief Perseverance
Anchoring
Avoidance
Confirmatory Bias
Avoidance
Availability Bias
Internalisation
Heuristics
End
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Belief Perseverance
There is much evidence that once people have
formed an opinion, they cling to it too tightly and for
too long (Lord, Ross and Lepper 1979).
At least two effects appear to be at work.
First, people are reluctant to search for evidence
that contradicts their beliefs.
Second, even if they find such evidence, they treat
it with excessive scepticism.
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Belief Perseverance
Some studies have found an even stronger effect,
known as confirmation bias, whereby people
misinterpret evidence that goes against their
hypothesis as actually being in their favour.
In the context of academic finance, belief
perseverance predicts that if people start out
believing in the Efficient Markets Hypothesis (see
the technical definitions), they may continue to
believe in it long after compelling evidence to the
contrary has emerged.
Definitions
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Belief Perseverance
As suggested by Soufian et al. (2014) it is possible to
replace the concept of the Efficient Markets
Hypothesis, that financial markets always act to set
prices “rationally”. By an understanding that prices
change as investors’ constantly adapt their
behaviour, allowing markets to evolve their own
internal order. The latter process is known as the
Adaptive Markets Hypothesis and was initially
proposed by Lo (2004, 2005).
Definitions
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Efficient Market
An efficient market incorporates news into prices immediately and
fully. Tests for efficiency in financial markets have been undermined
by information leakage. The authors (Croxson and Reade 2013) test
for efficiency in sports betting markets – real-world markets where
news breaks are remarkably clean. The data deployed in the article
comprise second-by-second prices and volumes from Football Betting
Markets & Odds at Betfair Sportsbook markets for 1,206
professional football games. Applying a novel identification to highfrequency data, they investigate the reaction of prices to goals
scored on the “cusp” of half-time. This strategy allows them to
separate the market's response to major news (a goal), from its
reaction to the continual flow of minor game-time news. The Betfair
markets behave largely “as if” they are updating efficiently to the
ticking down of the clock. On the author’s evidence, prices update
swiftly and fully.
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Efficient Market
A stock market is said to be efficient if it accurately
reflects all relevant information in determining security
prices. Critics have asserted that share prices are far too
volatile to be explained by changes in objective economic
events - the October 1987 crash (Black Monday) being a
case in point. Although the evidence is not unambiguous,
reports of the death of the efficient market hypothesis
appear premature (Malkiel 1989).
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Stock Performance in Popular Quartiles
There are fashions for everything: clothes, hairstyles, video games and
hashtags on Twitter. And that applies to stock markets as well. Who can
forget the enthusiasm for technology, media and telecom shares in the
late 1990s?
A paper (Ibbotson and Idzorek 2014) suggests that this tendency may
provide a strategy for outperforming the stock market, based on the
popularity of individual stocks. The authors defined the most popular
stocks as those that saw the most trading in their shares as a proportion
of their market value. These are most likely to be the companies that
are in the news, perhaps because they have a hot new product or
because many analysts are recommending them.
For a time these stocks may benefit from the so-called momentum
effect — a phenomenon whereby stocks that have recently risen in price
continue to perform well in the short term (usually a matter of months).
However, this popularity may drive such stocks up to excessive
valuations from which future returns are bound to be disappointing (in
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other words, they inevitably lose momentum sooner or later).
Stock Performance in Popular Quartiles
Standard
deviation!!
The table shows the annualised return of American stocks, based on
their popularity over the preceding year. Over a period of more than
40 years, the paper finds, stocks in the least popular quartile
outperformed those in the most popular segment by seven percentage
points a year.
The finding is significant. Academics have explained the long-term
outperformance of small companies (the size effect) or those with
below-average valuations (the value effect) in terms of compensation
for extra risk. Small firms are more likely to go bust than large ones;
cheap-looking stocks are usually cheap for a reason.
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Stock Performance in Popular Quartiles
The effect is theoretically compatible with the efficient-market
hypothesis. But it is very hard to see how the momentum or popularity
effects can be squared with the hypothesis, which supposes that all
public information is already reflected in share prices and thus should
be no help in determining future price movements.
The psychological reasons for the popularity effect are not hard to
discern. Financial assets are not like other goods; when they rise in
price, demand has a tendency to increase, not decrease. An investor
who hears that a friend or neighbour had made money out of a
particular stock will want to jump on the bandwagon. The authors of
the paper quote Ben Graham, the doyen of share analysts, as saying
the market is not a weighing machine but a “voting machine whereon
countless individuals register choices which are partly the product of
reason and partly the product of emotion.”
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Stock Performance in Popular Quartiles
Even professional fund managers may have good reasons for following
a fad. They may want to show, in their reports to clients, that they
have been smart enough to buy the hottest stocks of the year. In
addition, clients have a natural tendency to fire managers who have
performed badly, and transfer their assets to managers who have
recently beaten the market. When that happens the new managers
get cash, and they are likely to use it to buy their favourite shares —
by definition, those that have recently performed well. This may
exacerbate the momentum effect.
In turn, this may explain why the average manager does not
outperform the market, even though apparently exploitable anomalies
exist. Professional fund managers have their favourites; they just
hang on to them for too long.
Dimensions of Popularity by R.G. Ibbotson and T.M. Idzorek
Drop of the pops - The Economist - 17 Jan 2015
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Passive Investing
The process of bringing diversified, affordable investment products to the
masses started with investment trusts, which first appeared in the UK in the
1860s and afforded “the investor of moderate means the same advantages as
large capitalists”. Open-ended mutual funds followed in the 1920s, and were
boosted in the 1990s by fund supermarkets which made them more popular by
removing the initial charges for investing.
By contrast, passive investing is a fairly recent arrival. It did not start until
the 1970s, when academic research started to highlight the fact that most
active fund managers do not achieve better returns after costs than the
broader market.
As a proportion of the UK fund management industry, passive investing is still
fairly small. The Investment Association says index-tracking open-ended funds
account for about 10 per cent of overall retail funds under management.
According to ETFGI, a consultancy, exchange traded funds — which also track
indices, but are traded on a regulated market just like equities — account for
just 4.4 per cent of mutual fund assets in Europe despite explosive growth in
recent years.
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Democratising finance: How passive funds changed investing - FT - 30 Jan
15
Cycles
Numerous research works indicate that the cycle of boom
and crisis can be regarded as a natural element in
financial market history. On the other hand, there is a
rich discussion among practitioners and academics on the
origins of the recent global economic and financial crisis,
which led the world into the deepest and most severe
downturn since the Great Depression in the 1930s. An
explanation solely based on the collapse of the U.S.
housing bubble and its effects seems far too shortsighted. In addition to economic elucidations and
rationalizations, there are also behavioural and socioeconomic explanations, which take into account the
powerful social and psychological forces at work in
financial markets (Fenzl et al. 2013) .
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Cycles
Fenzl et al. (2013) approaches the discussion from a mass
psychological perspective. Starting from the shortcomings
of mainstream economic approaches in predicting market
trends and their underlying trading behaviour realistically,
their paper elucidates postulated mechanisms behind mass
phenomena and provides a concise review of literature on
collective dynamics in financial markets. They then
delineate previous research on the distinction between
mass phenomena and attempt to transfer this theoretical
framework to financial markets.
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Sociability
Interestingly Heimer (2014) found empirical evidence
that social interaction is more prevalent among active
rather than passive investors. It is found that active
investors tend to be male, urban, and educated, they are
technologically savvy and risk-seeking. Sociability is less
strongly associated with ownership of savings bonds –
considered to be an extremely passive form of investing.
Active investing is driven by overconfidence, and since
men are more overconfident than women, men are more
likely to be active traders. Specifically, using data from a
discount brokerage in the U.S., Barber and Odean (2001)
document that men trade 45 percent more than women.
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Age – Gender – Ethnicity - Religion
Yuce and Yap (2006) examined the investment behaviour
of male and female Canadian students who participated in
an investment game and invested $1,000,000 and formed
portfolios of different financial assets. Risk aversion
levels showed that female students are statistically more
risk averse than the male students. All female students
avoided futures and options investments, the risky
derivative instruments. Their results also showed that
female groups did not get the top 5 returns or the bottom
5 returns; instead they obtained middle range returns,
because they invested in safer instruments.
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Age – Gender – Ethnicity - Religion
In southern Brasil male individuals showed a higher level
of financial literacy on average compared to females
(Potrich et al. 2015).
White males perceive negative outcomes to be less
probable than white females or people from ethnic
minorities (Olofsson and Rashid 2011).
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Age – Gender – Ethnicity - Religion
Hong et al. (2004) used a representative sample of
elder households, to show that social individuals –
those who claim to “know their neighbours,” “visit
their neighbours,” or “attend church” – are more
likely to be stock market participants.
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Age – Gender – Ethnicity - Religion
Kanagaretnam et al. (2015) examined religiosity and
risk-taking in international banking. Individuals who
are more religious have greater risk aversion. There
is a positive relation between religiosity and both
financial accounting transparency and timely
recognition of bad news. Banks located in more
religious countries exhibit lower levels of risk in
their decision-making. And were less likely to
encounter financial difficulty or fail during the
2007–2009 financial crisis.
Also affected by technology?
Aversion to risk hampers
growth of German fintech sector - FT - 8 October 2015 fintech –
financial technology
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Emotion
There is considerable evidence that stock prices are
not driven by fundamentals and that emotions play a
major role. Shiller (1981) highlighted emotionallydriven excess market volatility, which has been hotly
debated ever since. But after 30 years of empirical
efforts to explain excess volatility and prove the
efficiency of markets, Shiller (2003) stood by his
initial assertion:
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Emotion
“After all the efforts to defend the efficient markets
theory there is still every reason to think that, while
markets are not totally crazy, they contain quite
substantial noise, so substantial that it dominates the
movements in the aggregate market. The efficient
markets model, for the aggregate stock market, has still
never been supported by any study effectively linking
stock
market
fluctuations
with
subsequent
fundamentals.”
The fact that noise, rather than fundamentals,
dominates market price movements is clear evidence
that crowds dominate stock pricing (Howard 2013). 1.123
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Emotion
Benartzi and Thaler (1993), however, provide an emotional
explanation.
“The equity premium puzzle refers to the empirical fact that
stocks have outperformed bonds over the last century by a
surprisingly large margin. We offer a new explanation based on
two behavioural concepts. First, investors are assumed to be
“loss averse,” meaning that they are distinctly more sensitive to
losses than to gains. Second, even long-term investors are
assumed to evaluate their portfolios frequently. We dub this
combination “myopic loss aversion.” Using simulations, we find
that the size of the equity premium is consistent with the
previously estimated parameters of prospect theory if
investors evaluate their portfolios annually.”
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Emotion
The observed 7% equity premium is thus the result
of short-term loss aversion and the investor ritual of
evaluating portfolio performance annually, rather
than the result of fundamental risk. Putting Shiller’s
research together with Benartzi and Thaler’s
analysis, it is reasonable to conclude that both stock
market volatility and long-term returns are largely
determined by investor emotions (Howard 2013).
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Avoidance
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Heuristics
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Anchoring
Kahneman and Tversky (1974) argue that when
forming estimates, people often start with some
initial, possibly arbitrary value, and then adjust away
from it.
Experimental evidence shows that the adjustment is
often insufficient. Put differently, people “anchor”
too much on the initial value.
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Anchoring
In one experiment, subjects were asked to estimate
the percentage of United Nations’ countries that are
African.
More specifically, before giving a percentage, they
were asked whether their guess was higher or lower
than a randomly generated number between 0 and
100.
The initial random number significantly affected
their subsequent estimates.
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Anchoring
Those who were asked to compare their estimate to
10% subsequently estimated 25%, while those who
compared to 60%, estimated 45%.
Recall “Lets ask the audience” in Who Wants To Be A
Millionaire, where the contestants discussion
influences the audience. Also in auctions, where the
auctioneer starts at a high value, prior to descending.
In anchoring, arbitrary and irrelevant numbers bias
people's judgments (Tversky and Kahneman, 1974)
and decisions (Ariely et al. 2003), even when
participants know that anchors are random or
implausible (Chapman and Johnson, 1994).
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Anchoring
Meaningful anchors also bias judgments (e.g., Mussweiler
and Strack, 2000). If decisions about credit-card
repayments are anchored upon minimum-payment
information, then people will repay less than they
otherwise would and incur greater interest charges
(Thaler and Sunstein, 2008 and Stewart, 2009). Stewart
(2009) found a strong correlation between minimum
payment size and actual repayment size in a survey of
credit-card payments.
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Anchoring
A number of studies have pointed out experts’
susceptibility to anchoring, e.g. for car mechanics
(Mussweiler et al., 2000), real estate agents (Northcraft
and Neale, 1987) and legal experts (Englich and
Mussweiler, 2001 and Englich et al., 2005; 2006). As
Furnham and Boo (2011) summarize, expertise fails to
prevent anchoring.
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Anchoring
However, task specific knowledge has been shown to reduce anchoring
by Wilson et al. (1996), as well as by Wright and Anderson (1989).
The divergent results on task familiarity point to different processes
that elicit anchoring effects (see Crusius et al., 2012). Thus, expert
statements may be biased as anchor-consistent knowledge is activated
in a cognitively effortful process, whereas in more simple tasks,
anchors are used intuitively as a cue to the right answer (Wegener et
al., 2001; 2010). Given that the decision situations investigated in
empirical anchoring studies can be expected to feature non-intuitive
settings, respective experimental studies need to implement
cognitively effortful tasks to uphold external validity. Connected to
this is the effect of cognitive load on subject’s decision quality.
Blankenship et al. (2008) show that a mental overload through time
pressure and task complexity increases anchoring (Meub and Proeger
2015).
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Beliefs – Anchoring
Numbers-Only Investing
Markets become volatile when investors pour in money
based purely on a few figures from the financials and
the analysts' predictions without knowing about the
companies those numbers represent. This is called
anchoring and it refers to focusing on one detail at the
expense of all the others. Imagine betting on a boxing
match and choosing the fighter purely by who has
thrown the most punches in their last five fights. You
may come out all right by picking the statistically busier
fighter, but the fighter with the least punches may
have won five by first-round knockouts. Clearly, any
metric can become meaningless when it is taken out of
context.
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Beliefs – Anchoring
Numbers-Only Investing
If you believe that it is all in the numbers, then you have
to react quickly to any change in the numbers to protect
your profits. Numbers-only investors are the most prone
to panic selling. They tend to hedge their buys with
stop-loss orders that other traders will try to trigger in
order to profit from shorting a stock. This strategy,
called gathering in the stops, can increase market
volatility for a short period of time and give the traders
who short the stock a profit. What this doesn't change
is the actual company beneath the stock. Short-term
volatility in the stock market shouldn't affect a
corporation's business operations. For example, Nike
doesn't stop making shoes when its stock dips.
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Beliefs – Anchoring
Anchoring Index (Kahneman 2011)
To illustrate anchoring bias in action, psychologists
Daniel Kahneman and Amos Tversky developed the
anchoring index (Jacowitz and Kahneman 1995). Here is
how it works. The researchers asked test subjects the
following questions (Tversky and Kahneman, 1974):
Is the height of the tallest redwood more or less than
1,200 feet?
What is your best guess about the height of the tallest
redwood?
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Beliefs – Anchoring
Anchoring Index (Kahneman 2011)
The group of participants had a mean estimate of 844
feet for the question, which is about three times the
actual height of a very tall redwood. A different group
was given the same question, but the height value in first
question was changed from 1,200 feet to 180 feet.
The results from this second group illustrate the
powerful effects of anchoring bias, as the mean
estimate fell to 282 feet. Rather than try to reason
that a 1,200-foot tree would approximate a 120-story
building, people assume that there must be some factual
basis to the hypothetical height value, so they adjust
their estimates accordingly.
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Beliefs – Anchoring
Anchoring Index (Kahneman 2011)
The anchoring index is the ratio of the differences
expressed as a percentage. In the example, the
difference between the two estimates (562 feet) is
divided by the difference between the two anchors
(1,020 feet) to arrive at 55%. According to Kahneman,
the 55% anchoring index measure is fairly typical of
similar experiments.
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Beliefs – Anchoring
Listing Prices Affect Estimates of Home Values
Stocks are not the only aspect of personal finance that
anchoring bias affects. A separate study (Northcraft
and Neale 1987) asked real estate agents to estimate
the value of homes on the market. The agents visited
the homes and heard comprehensive information about
them, including an asking price. Half the agents got an
asking price that was significantly higher than the actual
listing price, and half received a price significantly lower
than the listing price.
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Beliefs – Anchoring
Listing Prices Affect Estimates of Home Values
Each agent then had to suggest a reasonable buying
price, and the lowest price at which the agent would sell
the home if it were their own. The agents also had to list
the factors that affected their estimates. None of the
agents cited asking prices as a factor in their price
estimates. Indeed, the agents touted their ability to
ignore asking prices when estimating home values.
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Beliefs – Anchoring
Listing Prices Affect Estimates of Home Values
The results, however, tell a very different story.
The effect of anchoring bias, as measured by the
anchoring index, was 41% for the agents in the
experiment. In other words, 41% of them were very
close to the asking price they had heard. A control group
of business school students with no real estate
expertise performed the same experiment, and fared
only slightly worse with an anchoring index of 48%. The
main difference is that the students admitted that their
estimates were affected by listing price.
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Beliefs – Anchoring
The house doesn’t always win: Evidence of
anchoring among Australian bookies
McAlvanah and Moul (2013) examine Australian horseracing
bookmakers’ responses to late scratches, instances in which a horse
is abruptly withdrawn after betting has commenced. They observed
bookies exhibit anchoring on the original odds and fail to re-adjust
odds fully on the remaining horses after a scratch, thereby earning
lower profit margins and occasionally creating nominal arbitrage
opportunities for bettors. They also examined which horses’ odds
bookies adjust after a scratch and demonstrate diminished profit
margins even after controlling for these endogenous adjustments.
Their results indicate that bookies’ adjustments recover
approximately 80% of lost profit margin but that bookies forgo the
remaining 20% due to systematic under-adjustments.
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Beliefs – Anchoring
Closely related is the recognition heuristic which could be a first
step in consideration set formation (Marewski et al. 2010), as it
allows the choice set to be quickly reduced. This idea is
consistent with research that suggests that priming a familiar
brand increases the probability that it will be considered for
purchase (e.g., Coates et al. 2004). Brand recognition can be even
more important than attributes that are a more direct reflection
of quality. For instance, in a blind test, most people preferred a
jar of high-quality peanut butter to two alternative jars of lowquality peanut butter. Yet when a familiar brand label was
attached to one of the low-quality jars, the preferences changed.
Most (73%) now preferred the jar with the label they
recognized, and only 20% preferred the unlabelled jar with the
high-quality peanut butter (Hoyer and Brown 1990). Brand
recognition may well dominate the taste cues, or the taste cues
themselves might even be changed by brand recognition — people
“taste” the brand name.
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Beliefs – Anchoring
The anchoring(-and-adjustment) heuristic has been
studied in numerous experimental settings Anchors
result from publicly observable and aggregated
decisions of other market participants. Studies have
neglected this social dimension. An experimental
design with a socially derived anchor, to more
accurately implement market conditions was employed.
Robust effects for the socially derived anchor exhibit
an increased bias for higher cognitive load, and only
weak learning effects. Comparison to a neutral anchor
shows that the social context increases biased
behaviour. Anchoring remains a valid explanation for
systematically biased decisions within market
contexts (Meub and Proeger 2015).
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Beliefs – Anchoring
Furnham and Boo (2011) review the literature on
anchoring including various different models,
explanations and underlying mechanisms used to
explain the effects. The anchoring effect is both
robust and has many implications in all decision making
processes. The paper documents the many different
domains and tasks in which the effect has been shown.
It also considers mood and individual difference
(ability, personality, information styles) correlates of
anchoring as well as the effect of motivation and
knowledge on decisions affected by anchoring. Finally
it looks at the applications of anchoring effects in
everyday life.
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Anchoring
Anchoring is closely related to confirmatory bias
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Beliefs – Anchoring - Avoidance
Hammond et al. 1998/2006
1. Always view a problem from different
perspectives. Try using alternative starting points
and approaches rather than sticking with the
first line of thought that occurs to you.
2. Think about the problem on your own before
consulting others to avoid becoming anchored by
their ideas.
3. Be open-minded. Seek information and opinions
from a variety of people to widen your frame of
reference and to push your mind in fresh
directions.
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Beliefs – Anchoring - Avoidance
4. Be careful to avoid anchoring your advisers,
consultants, and others from whom you solicit
information and counsel. Tell them as little as
possible about your own ideas, estimates, and
tentative decisions. If you reveal too much, your
own preconceptions may simply come back to you.
5. Be particularly wary of anchors in negotiations.
Think through your position before any
negotiation begins in order to avoid being
anchored by the other party's initial proposal. At
the same time, look for opportunities to use
anchors to your own advantage - if you're the
seller, for example, suggest a high, but
defensible, price as an opening gambit.
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Confirmatory Bias
The most striking evidence for the confirmatory bias
is a series of experiments demonstrating how
providing the same ambiguous information to people
who differ in their initial beliefs on some topic can
move their beliefs further apart.
To illustrate such polarization, Lord, Ross, and
Lepper (1979) asked 151 undergraduates to complete
a questionnaire that included three questions on
capital punishment.
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Confirmatory Bias
Later, 48 of these students were recruited to
participate in another experiment.
Twenty-four of them were selected because their
answers to the earlier questionnaire indicated that
they were “proponents” who favoured capital
punishment, believed it to have a deterrent effect,
and thought most of the relevant research supported
their own beliefs.
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Confirmatory Bias
Twenty-four were opponents who opposed capital
punishment, doubted its deterrent effect and
thought that the relevant research supported their
views.
These subjects were then asked to judge the merits
of randomly selected studies on the deterrent
efficacy of the death penalty, and to state whether
a given study (along with criticisms of that study)
provided evidence for or against the deterrence
hypothesis.
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Confirmatory Bias
Subjects were then asked to rate, on 17 point scales
ranging from -8 to +8, how the studies they had read
moved their attitudes towards the death penalty,
and how they had changed their beliefs regarding its
deterrent efficacy.
Lord, Ross and Lepper (1979, pp. 2102-4) summarize
the basic results (all of which hold with confidence
p < 0.01) as follows:
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Confirmatory Bias
The relevant data provide strong support for the
polarization hypothesis.
Asked for their final attitudes relative to the
experiment’s start, proponents reported that they
were more in favour of capital punishment, whereas
opponents reported that they were less in favour of
capital punishment.
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Confirmatory Bias
Similar results characterized subjects’ beliefs about
deterrent efficacy.
Proponents reported greater belief in the deterrent
effect of capital punishment, whereas opponents
reported less belief in this deterrent effect.
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Confirmatory Bias
This bias leads us to seek out information that
supports our existing instinct or point of view while
avoiding information that contradicts it.
The confirmatory bias not only affects where we go
to collect evidence but also how we interpret the
evidence we do receive, leading us to give too much
weight to supporting information and too little to
conflicting information.
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Confirmatory Bias
Research (Young et al. 2009 and 2011) suggests that
the antidote for confirmation bias could be, oddly,
anger. Researchers asked 97 undergraduates to
participate in what they thought were two separate
experiments. The first involved either recalling and
writing about a time they'd been exceptionally angry
(this was just a prop designed to make them angry),
or a time they'd been sad, or about something
mundane.
Next, all the participants read an introduction to the
debate about whether hands-free devices make
speaking on a mobile phone while driving any safer.
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Confirmatory Bias
(Important to note: all of the participants had been
chosen because a pre-study showed they believed
that the devices do make it safer.)
Finally, the participants were presented with onesentence summaries of eight articles, either in
favour, or against, the idea that hands-free devices
make driving safer. The participants had to choose
five of these articles to read in full.
The results: Participants who'd earlier been made to
feel angry read more articles critical of hands-free
devices, contrary to their own position.
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Confirmatory Bias
And when the participants' attitudes were retested
at the end of the study, it was the angry participants
who'd shifted more from their original position in
the debate.
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Confirmatory Bias
Confirmation bias is the tendency to seek evidence
consistent with a prior belief. In the strip, Dilbert's
boss demonstrates this bias to a tee when he
assumes his astute managerial skills are what caused
a minuscule (and clearly unrelated) improvement in
the company's stock price. Cartoon (Kramer 2014)
A Quick Puzzle to Test - NY Times - 2 July 2015
A short game sheds light on government policy,
corporate America and why no one likes to be wrong.
I don't want to play; just tell me the answer.
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Confirmatory Bias - Avoidance
Hammond et al. 1998/2006 :
1. Always check to see whether you are examining all
the evidence with equal rigour. Avoid the tendency
to accept confirming evidence without question.
2. Get someone you respect to play devil's advocate,
to argue against the decision you're contemplating.
Better yet, build the counter arguments yourself.
What's the strongest reason to do something else?
The second strongest reason? The third? Consider
the position with an open mind.
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Confirmatory Bias - Avoidance
3. Be honest with yourself about your motives. Are
you really gathering information to help you make
a smart choice, or are you just looking for
evidence confirming what you think you'd like to
do?
4. In seeking the advice of others, don't ask leading
questions that invite confirming evidence. And if
you find that an adviser always seems to support
your point of view, find a new adviser. Don't
surround yourself with yes-men.
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Use Experts Wisely
Expert advice can often be compromised by human
frailties - like their current mood or what their
values are - and should be treated accordingly,
experts say (Sutherland and Burgman 2015).
Eight ways to improve expert advice
1. Use groups. Their estimates consistently
outperform those of individuals‘.
2. Choose members carefully. Expertise declines
dramatically outside an individual's specialisation.
3. Don't be star struck. A person's age, number of
publications or reputation is not a measure of an
168
expert's ability to estimate or predict events. 1.168
Use Experts Wisely
4. Avoid homogeneity. Diverse groups tend to
generate more accurate judgements.
5. Don't be bullied. Less-assured and assertive
people tend to make better judgements.
6. Weight opinions. Calibrate an expert's
performance with test questions.
7. Train experts. Training can improve an expert's
ability.
8. Give feedback. Rapid feedback tends to improve
expert judgements.
Sutherland and Burgman 2015
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Availability Bias
When judging the probability of an event – the
likelihood of getting mugged in Chicago, say – people
often search their memories for relevant
information.
While this is a perfectly sensible procedure, it can
produce biased estimates because not all memories
are equally retrievable or “available”, in the language
of Kahneman and Tversky (1974). More recent
events and more salient events – the mugging of a
close friend, say – will weigh more heavily and
distort the estimate.
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Availability Bias
Economists are some times wary of this body of
experimental evidence because they believe
1. That people, through repetition, will learn
their way out of biases;
2. That experts in a field, such as traders in an
investment bank, will make fewer errors;
3. That with more powerful incentives, the
effects will disappear.
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Availability Bias
While all these factors can attenuate biases to some
extent, there is little evidence that they wipe them
out altogether.
The effect of learning is often muted by errors of
application: when the bias is explained, people often
understand it, but then immediately proceed to
violate it again in specific applications.
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Availability Bias
Expertise, too, is often a hindrance rather than a
help: experts, armed with their sophisticated models,
have been found to exhibit more overconfidence than
laymen, particularly when they receive only limited
feedback about their predictions.
Finally, in a review of dozens of studies on the topic,
Camerer and Hogarth (1999, p. 7) conclude that while
incentives can sometimes reduce the biases people
display, “no replicated study has made rationality
violations disappear purely by raising incentives”.
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Availability Bias
Bodnaruk and Simonov (2014) provide direct evidence
on the effect of financial expertise on investment
outcomes by analysing private portfolios of mutual
fund managers. They find no evidence that financial
experts make better investment decisions than
peers: they do not outperform, do not diversify their
risks better, and do not exhibit lower behavioural
biases.
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Availability Bias
Managers do much better in stocks for which they
have an information advantage over other investors,
i.e., stocks that are also held by their mutual funds.
More experienced managers seem to be aware of the
limitations to their investment skills as they increase
their holdings of mutual fund-related stocks
following poor performance of their portfolios. Their
results suggest that there are limits to the value
added by financial expertise.
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Availability Bias
News media highlights memorable occurrences which
gives an event the inaccurate appearance of frequency.
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Availability Bias
Child theft is a rare occurrence, but availability bias
suggests a high probability of abduction.
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Availability Bias
Belief in the likelihood of a plane crash is caused by
availability bias. More traffic deaths in wake of 9/11
- ScienceDaily - 11 Sept 2012 also Gaissmaier and
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Gigerenzer 2012.
Availability Bias
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Availability Bias
Risk Savvy: How To Make Good Decisions By Gerd Gigerenzer
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Debias
Of course debiasing (Soll et al. 2013) is a good idea.
There are two general approaches available for
debiasing decisions:
(1) debiasing by modifying the decision maker (e.g.,
through education and the provision of tools)
(2) debiasing by modifying the environment (e.g., by
creating optimal conditions to support wise
judgment).
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Internalisation
Are investors in stock markets influenced by their
current mood?
Some studies have empirically found that factors
known or assumed to affect current mood (e.g.,
temperature, sunny or cloudy weather, changes of
season, time of day) correlate with stock returns in
the expected direction (Dowling and Lucey, 2005;
Nofsinger, 2005).
For instance, in one study (Hirshleifer and Shumway,
2003) replicating some previous research, a negative
relationship between cloudy weather and stock returns
was observed in a majority of 26 international stock
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markets.
Internalisation
Also the hungrier an animal becomes, the more risks
it's prepared to take in the search for food. Now,
for the first time, Symmonds et al. (2010) have
shown that our animal instinct to maintain a balanced
metabolic state influences our decision-making in
other contexts, including finance. In the context of
their study, biology would seem to inform economic
theory, not only in providing explanations of
psychological phenomena such as loss aversion, but
also in highlighting substantive effects of state
changes on economic decisions, perhaps reflecting
shared evolutionarily conserved neurobiological
mechanisms.
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Internalisation
The immediate effect of a meal is to neutralise risk
aversion. For the men with more adipose tissue and
higher baseline levels of leptin (a hormone that
suppresses appetite), who are generally more risk
averse, this meant they became less risk averse when
performing the task right after eating. By contrast,
for men with less adipose tissue and lower leptin
levels, who are generally low risk averse, their risk
aversion was increased immediately after eating, just
as you'd expect based on the behaviour of hungry
animals.
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Internalisation
Going to the casino? Don't eat! Gambling on an empty
stomach leads to better decisions, study claims Daily Mail - 29 Oct 2014
It might seem like common sense that it’s better to
make important decisions after you’ve eaten.
But a study has claimed the exact opposite - that we
actually make better decisions on an empty stomach.
Researchers found that people who were hungry
made better snap decisions and also could also
appreciate future big rewards than those who were
fully fed.
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Internalisation
The research was carried out by scientists at
Utrecht University in The Netherlands.
In the study participants were asked to fast for a
night, and when they arrived at the laboratory the
next day some were given food and some were not.
They were then given a variety of tasks to simulate
decision making.
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Internalisation
Three experimental studies examined the counter
intuitive hypothesis that hunger improves strategic
decision making, arguing that people in a hot state
(like emotions or visceral drives - relating to deep
inward feelings rather than to the intellect) are
better able to make favourable decisions involving
uncertain outcomes. Studies 1 and 2 demonstrated
that participants with more hunger or greater
appetite made more advantageous choices in the
Iowa Gambling Task compared to sated participants
or participants with a smaller appetite.
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Internalisation
Study 3 revealed that hungry participants were
better able to appreciate future big rewards in a
delay discounting task; and that, in spite of their
perception of increased rewarding value of both food
and monetary objects, hungry participants were not
more inclined to take risks to get the object of their
desire. Together, these studies for the first time
provide evidence that hot states improve decision
making under uncertain conditions, challenging the
conventional conception of the detrimental role of
impulsivity in decision making (de Ridder et al. 2014).
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Internalisation
Although several cognitive models have been proposed to
disentangle the psychological processes underlying performance on
the Iowa Gambling Task (IGT), the Expectancy Valence Model (EVM)
has been the most widely implemented.
It is shown that the EVM does not provide clear information about
decision making processes at the individual level by fitting the EVM,
with individual random effects, to a sample of participants from
various drug using populations using Bayesian techniques and to a
sample of participants who complete the IGT multiple times. In
particular, they show that the individual-level parameter estimates
from the model may be bi-modally distributed and hence are
inherently ambiguous and have little psychological significance
(Humphries et al. 2015).
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Internalisation
Numerous stock market pricing distortions have been
uncovered. Many of these have been linked to the
cognitive errors documented in the behavioural
science literature. Hirshleifer (2008) provided three
organizing principles to place price distortions into a
systematic framework.
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Internalisation
People rely on heuristics (i.e. short-cut decision rules)
because people face cognitive limitations. Because of a
shared evolutionary history, people might be predisposed
to rely on the same heuristics, and therefore be subject
to the same biases.
People inadvertently signal their inner states to others.
For this reason, nature might have selected for traits
such as overconfidence, in order that people signal strong
confidence to others.
People’s judgments and decisions are subject to their own
emotions as well as to their reason (Howard 2013).
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Internalisation
Duclos et al. (2013) examined the effects of social
exclusion on a critical aspect of consumer behaviour,
financial decision-making. Specifically, four lab
experiments and one field survey uncover how feeling
isolated or ostracized causes consumers to pursue
riskier but potentially more profitable financial
opportunities. These daring proclivities do not appear
driven by impaired affect or self-esteem. Rather,
interpersonal rejection exacerbates financial risktaking by heightening the instrumentality of money
(as a substitute for popularity) to obtain benefits in
life. Invariably, the quest for wealth that ensues
tends to adopt a riskier but potentially more
lucrative road.
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Duclos et al. 2013
Internalisation
Wang and Xiao (2009) examined college students’
credit card indebtedness and found that their buying
patterns and social networks affected indebtedness.
Students with a tendency toward compulsive buying that is, chronic and repetitive purchasing that
becomes a primary response to negative events or
feelings (O’Guinn and Faber, 1989) - were more likely,
and those with greater social support less likely, to
have high debts.
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Internalisation
According to lay opinion about financial debts,
individual characteristics and irresponsible purchases
are the major reasons for indebtedness. Being in
debt is often attributed to personal fault of the
indebted people themselves rather than to
situational circumstances (e.g., Roland-Lévy and
Walker, 1994; Walker, 1996), or to easy access to
credit due to lenders’ misjudgments of borrowers’
financial standing.
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Internalisation
Subjective well-being and hedonic (relating to, or
marked by pleasure) editing, that is how happy people
maximize joint outcomes of loss and gain was
examined by Sul et al. (2013). Hedonic editing refers
to the decision strategy of arranging multiple events
in time to maximize hedonic outcomes (Thaler 1985).
The research investigated the relationship between
subjective well-being and hedonic editing.
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Internalisation
In Study 1, they gave participants pairs of social or
financial events and asked them to indicate their
preferences regarding the sequence and interval
length between the two events. Compared to
participants with lower subjective well-being, those
with higher subjective well-being preferred to
experience a social gain (e.g., chatting with a close
friend) temporally closer to a financial loss,
suggesting that happy individuals are more inclined
than less happy individuals to use positive social
events as buffers against loss.
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Internalisation
In Study 2, participants were asked to select the
type of positive event they would want to experience
after a negative event. Happy individuals displayed a
stronger preference for social events.
Their findings (Sul et al. 2013) suggest that happy
and less happy individuals employ different hedonic
editing strategies for mixed events. The hedonic
editing strategies preferred by happy individuals are
as follows. Happy individuals use the loss-buffering
strategy of arranging a positive social event closer to
a negative event (e.g., receiving a nice letter from a
friend and paying a fine for speeding on the same
day).
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Internalisation
They use the benefits of positive social events to
decrease the impact of negative experiences (e.g., an
unsuccessful job interview), by voluntarily choosing
to experience a social gain (e.g., hanging out with
friends) over other events (e.g., finding a $10 bill on
the street) as a cross-domain buffer. Given the
importance of social resources and effective coping
strategies for one’s subjective wellbeing, it is
possible that happy individuals are better than less
happy individuals at making themselves happier.
However, their data should be interpreted with
caution because it does not provide direct evidence
regarding whether happy individuals’ hedonic editing
strategies actually improve hedonic outcomes.
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Heuristics
Heuristics are strategies that guide information
search and modify problem representations to
facilitate solutions.
A heuristic is a strategy that ignores part of the
information, with the goal of making decisions more
quickly, frugally, and/or accurately than more
complex methods (Gigerenzer and Gaissmaier, 2011).
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Heuristics
When heuristics were formalized, a surprising
discovery was made. In a number of large worlds,
simple heuristics were more accurate than standard
statistical methods that have the same or more
information. These results became known as less-ismore effects: There is an inverse-U-shaped relation
between level of accuracy and amount of information,
computation, or time. In other words, there is a point
where more is not better, but harmful (Gigerenzer
and Gaissmaier, 2011).
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Heuristics
Ten well-studied heuristics for which there is
evidence that they are in the adaptive toolbox of
humans. Each heuristic can be used to solve problems
in social and non-social environments. See the
references given for more information regarding
their ecological rationality, and the surprising
predictions they entail (Gigerenzer and Brighton,
2009).
A summary table follows.
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Heuristics
Heuristic
Definition
Recognition heuristic (Goldstein and Gigerenzer,
If one of two alternatives is recognized, infer that it has the
2002) also Schooler and Hertwig, 2005
higher value on the criterion.
Fluency heuristic (Jacoby and Dallas, 1981) also
If both alternatives are recognized but one is recognised
Schooler and Hertwig, 2005
faster, infer that it has the higher value on the criterion.
Take-the-best (Gigerenzer and Goldstein, 1996) also
To infer which of two alternatives has the higher value:
Gigerenzer and Brighton 2009, Czerlinski et al., 1999
(a) search through cues in order of validity,
and Brighton, 2006
(b) stop search as soon as a cue discriminates, and
(c) choose the alternative this cue favours.
Tallying (unit-weight linear model, Dawes, 1979) also To estimate a criterion, do not estimate weights but simply
Hogarth and Karelaia, 2005, 2006 and Czerlinski et al., count the number of positive cues.
1999
Satisficing (Simon, 1955; Todd and Miller, 1999 also
Search through alternatives and choose the first one that
Dudey and Todd, 2002, Gilbert and Mosteller, 1966
exceeds your aspiration level.
and Bruss, 2000
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Heuristics
Heuristic
Definition
1⁄N; equality heuristic (DeMiguel et al.,
Allocate resources equally to each of N alternatives.
2009)
Default heuristic (Johnson and Goldstein,
If there is a default, do nothing.
2003; Pichert and Katsikopoulos, 2008)
Tit-for-tat (Axelrod, 1984)
Cooperate first and then imitate your partner’s last behaviour
Imitate the majority (Boyd and Richerson,
Consider the majority of people in your peer group and imitate their
2005)
behaviour
Imitate the successful (Boyd and Richerson,
Consider the most successful person and imitate his or her behaviour
2005)
Now in detail
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Recognition Heuristic
If one of two objects is recognized and the other is not, then infer that the
recognized object has the higher value with respect to the criterion (Goldstein
and Gigerenzer, 2002).
The recognition heuristic relies on ignorance that is partial and systematic. It
works because lack of recognition knowledge about objects such as cities,
colleges, sports teams, and companies traded on a stock market is often not
random (Schooler and Hertwig, 2005).
The recognition heuristic cannot be applied when both objects are either
recognized or unrecognized (Schooler and Hertwig, 2005).
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Recognition Heuristic
Common sense suggests that ignorance stands in the way of good decision making. The
recognition heuristic belies this intuition. To see how the heuristic turns ignorance to
its advantage, consider the simple situation in which one must select whichever of two
objects is higher than the other with respect to some criterion (e.g., size or price). A
contestant on a game show, for example, may have to make such decisions when faced
with the question, “Which city has more inhabitants, San Diego or San Antonio?”
San
1.356 million
(2013) depends on the information available to her. If the
How Diego
she makes
this decision
San Antonio 1.409 million (2013)
only information on hand is whether she recognizes one of the cities and there is
So
San to
Antonio
reason
suspect that recognition is positively correlated with city population, then
she can do little better than rely on her (partial) ignorance. This kind of ignorancebased inference is embodied in the recognition heuristic, which for a two
alternative choice can be stated as follows: If one of two objects is recognized and
the other is not, then infer that the recognized object has the higher value with
respect to the criterion (Schooler and Hertwig, 2005).
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Fluency Heuristic
The fluency heuristic assumes that if one object is processed more fluently, faster, or
more smoothly than another, it is inferred that this object has the higher value with
respect to the question being considered (Jacoby and Lee 1984).
The fluency heuristic, in contrast to the recognition heuristic, does not exploit partial
ignorance but rather graded recognition (Schooler and Hertwig, 2005).
Masson et al. (1995) examined the influence of task demands on the use of the fluency
heuristic using a version of the fame judgment task. Subjects initially read a list of
famous and non-famous names, and later were asked to classify a set of names as
famous or non-famous. Some of the test names had been read in the first part of the
experiment, and consequently were expected to be more fluently identified by all the
subjects. They attempted to verify this expectation by testing a subset of the names
in a visual identification task. Subsequent use of the fluency heuristic in the
classification task was expected to be revealed by a higher probability of classifying
the previously read names as famous.
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Fluency Heuristic
The "fluency heuristic," which tells us that if something is easy to process, then we
tend to prefer it over more complicated options. Gut instincts allow people to make
routine decisions without thinking too hard - Washington Post - 1 Nov 2010
In a University of Michigan study (Song and Schwarz 2008, 2010 or 2010), people
were more open to the idea of working out and more likely to do it when the directions
for an exercise routine were written in a basic typeface as opposed to a more
convoluted script. "Apparently the [subjects'] brains mistook the ease of simply
reading about exercise for ease of actually doing the sit-ups and bench presses,"
writes Herbert (On Second Thought: Outsmarting Your Mind's Hard-Wired Habits),
who surmises that for the group with a more confusing font, "the reading alone tired
them out."
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Fluency Heuristic
What that means, says Herbert, director of science communication for the
Association for Psychological Science, a national organization based in Washington, is
that "people on the front lines of getting us through this national health and obesity
crisis . . . have to overcome some really, really deeply wired habits of mind." On Second
Thought: Outsmarting Your Mind's Hard-Wired Habits - Herbert 2010
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Take-The-First Heuristic
Choose the first alternative that comes to mind (Gigerenzer and Gaissmaier, 2011).
Can taking the first option in decision-making lead to the best decisions in sports
contexts? And, is one's decision-making self-efficacy in that context linked to take
the first decisions? The purpose of the study was to examine the role of the take the
first heuristic and self-efficacy in decision-making on a simulated sports task.
Students participated in the study and performed 13 trials in each of two video-based
basketball decision tasks.
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Take-The-First Heuristic
One task required participants to verbally generate options before making a final
decision on what to do next, while the other task simply asked participants to make a
decision regarding the next move as quickly as possible. Decision-making self-efficacy
was assessed using a questionnaire comprising various aspects of decision-making in
basketball. Participants also rated their confidence in the final decision. Results
supported many of the tenets of the take the first heuristic, such that people used
the heuristic on a majority of the trials (70%), earlier generated options were better
than later ones, first options were meaningfully generated, and final options were
meaningfully selected. Results did not support differences in dynamic inconsistency or
decision confidence based on the number of options. Findings also supported the link
between self-efficacy and the take the first heuristic. Participants with higher self-
efficacy beliefs used take the first more frequently and generated fewer options
than those with low self-efficacy. Thus, not only is take the first an important
heuristic when making decisions in dynamic, time-pressure situations, but self-efficacy
plays an influential role in take the first (Hepler and Feltz, 2012).
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Take-The-First Heuristic
Take-the-first is a heuristic that can be used by players to choose among practical
options. There is evidence that experienced players do not try to exhaustively
generate all possible options. Instead, they seem to rely on the order in which options
are spontaneously generated in a particular situation and choose the first option that
comes to mind (Johnson and Raab, 2003).
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Take-The-Best Heuristic
The take-the-best algorithm, has the policy that is "take the best, ignore the rest."
The take-the-best algorithm assumes a subjective rank order of cues according to
their validities (Gigerenzer and Goldstein, 1996).
Consider the task of which of two alternatives to choose given several binary cues to
some unobservable criterion. An example is deciding which of two cities is the bigger,
given such cues as whether each has a university or has a football team in the premier
league (Hutchinson and Gigerenzer, 2005).
García-Retamero and Dhami (2009) tested how policemen, professional burglars, and
laypeople infer which of two residential properties is more likely to be burgled.
Comparison of experts and novices in terms of the cues they considered to be
important for choosing which of a pair of residential properties was more likely to be
burgled, and in terms of the strategy that best predicted their choices in such1.218
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task.
Take-The-Best Heuristic
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Take-The-Best Heuristic
Positive and Negative Values for the Eight Cues
Cue
Positive Value
Negative Value
Garden in the property
Tall hedges/bushes
Signs of care
Not well-kept property
Type of property
Short hedges/bushes
Well-kept property
Flat
House
Light in the property
Off
On
Letterbox
Stuffed with post
Empty
Location of the property
Corner of the street
Middle of the street
Access to the property
Doors/windows
Security in the property
on
ground
Doors/windows
on
floor
floor
No burglar alarm system
Burglar alarm system
second
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One-Clever Cue Heuristicsic
Use only one “clever cue” or single criterion to make a decision. Example: Female
peacocks select mates based on the males’ number of eyespots. Peahens choose the
male with greatest number of eyespots (Gigerenzer and Goldstein, 1996).
Many animal species appear to rely on a single “clever” cue for locating food, nest
sites, or mates. For instance, in order to pursue a prey or a mate, bats, birds, and fish
do not compute trajectories in three-dimensional space, but simply maintain a constant
optical angle between their target and themselves — a strategy called the gaze
heuristic (Gigerenzer, 2007, Shaffer et al., 2004). In order to catch a fly ball,
baseball outfielders and cricket players rely on the same kind of heuristics rather
than trying to compute the ball’s trajectory (McLeod and Dienes, 1996). Similarly, to
choose a mate, a peahen investigates only three or four of the peacocks displaying in a
lek and chooses the one with the largest number of eyespots (Petrie and Halliday,
1994).
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Hiatus Heuristic
If a customer has not purchased within a certain number of months (the hiatus), the
customer is classified as inactive; otherwise, the customer is classified as active. Here
is one example where heuristics have proven to be more accurate than the models of
rationality.
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Hiatus Heuristic
Recently, academics (Wübben and Wangenheim, 2008) have shown interest and
enthusiasm in the development and implementation of stochastic customer base
analysis models. Using the information these models provide, customer managers
should be able to
(1) distinguish active customers from inactive customers,
(2) generate transaction forecasts for individual customers and determine future best
customers,
(3) predict the purchase volume of the entire customer base.
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Hiatus Heuristic
However, there is also a growing frustration among academics insofar as these models
have not found their way into wide managerial application. The authors compare the
quality of these models when applied to managerial decision making with the simple
heuristics that firms typically use. The authors find that the simple heuristics
perform at least as well as the stochastic models with regard to all managerially
relevant areas, except for predictions regarding future purchases at the overall
customer base level. The authors conclude that in their current state, stochastic
customer base analysis models should be implemented in managerial practice with much
care. Furthermore, they identify areas for improvement to make these models
managerially more useful (Wübben and Wangenheim, 2008).
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Hiatus Heuristic
Clothing retailer
Hiatus Heuristic
Pareto Model
83% accurate
75%
(83% of customers were correctly classified)
Airline
77%
74%
Online CD Store
77%
77%
Note that while the hiatus heuristic works better than the Pareto/NBD model for classifying
customers for the clothing retailer and airline, the two prediction methods tie when it comes to
classifying customers for the online CD store (APPsychTextbk - Accuracy-Effort Trade-off).
[The fanciest modern approach to deciding uses the Pareto/NBD model, which uses the negative
binomial distribution, a statistics method, to determine which customers will be active or inactive.
Pareto/NBD and related models have seen much discussion in academic literature. The foundation
was laid by Schmittlein et al. in a 1987 paper and expanded upon in 2004 by Fader et al. For more
practical information, Bruce Hardie provides a multitude of tutorials and Excel spreadsheets for
using probabilistic models in a marketing context (source).]
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Tallying Heuristic - UnitWeight Linear Model
To estimate a criterion, do not estimate weights but simply count the number of
favouring cues (Dawes, 1979).
Tallying of positive evidence, the number of positive cue values for each object is
tallied across all cues, and the object with the largest number of positive cue values is
chosen validities (Gigerenzer and Goldstein, 1996).
Example: In deciding between frozen yogurt and ice cream, a student chooses frozen
yogurt because frozen yogurt has the greater number of favouring cues.
How would you decide?
Frozen yogurt:
1) healthier; 2) more toppings; 3) cheaper
Ice Cream:
1) more flavours
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Tallying Heuristic - UnitWeight Linear Model
Magnetic Resonance Imaging (MRI) or simple bedside rules?
There are about 2.6 million emergency room visits for dizziness or vertigo in the
United States every year (Kattah et al. 2009). The challenging task for the
emergency physician is to detect the rare cases where dizziness is due to a
dangerous brainstem or cerebellar stroke. Frontline misdiagnosis of strokes
happens in about 35% of the cases. One solution to this challenge could be
technology. Getting an early MRI with diffusion-weighted imaging takes 5 to 10
minutes plus several hours of waiting time, costs more than $1,000, and is not
readily available everywhere.
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Tallying Heuristic - UnitWeight Linear Model
Magnetic Resonance Imaging (MRI) or simple bedside rules?
However, Kattah et al. (2009) developed a simple bedside eye examination that
actually outperforms MRI and takes only about one minute: It consists of three
tests and raises an alarm if at least one indicates a stroke. This simple tallying rule
correctly detected 100% of those patients who actually had a stroke (sensitivity),
whereas an early MRI only detected 88%. Out of 25 patients who did not have a
stroke, the bedside exam raised a false alarm in only one case (i.e., 4% false
positive rate = 96% specificity). Even though the MRI did not raise any false
alarms, the bedside examination seems preferable in total, given that misses are
more severe than false alarms and that it is faster, cheaper, and universally
applicable.
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Tallying Heuristic - UnitWeight Linear Model
Avoiding avalanche accidents.
Hikers and skiers need to know when avalanches could occur. The obvious clues
method is a tallying heuristic that checks how many out of seven cues have been
observed enroute or on the slope that is evaluated (McCammon and Hägeli 2007).
These cues include whether there has been an avalanche in the past 48 hours and
whether there is liquid water present on the snow surface as a result of recent
sudden warming. When more than three of these cues are present on a given slope,
the situation should be considered dangerous. With this simple tallying strategy,
92% of the historical accidents (where the method would have been applicable)
could have been prevented.
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Satisficing Heuristic
Satisficing is a decision-making strategy or cognitive heuristic that entails searching
through the available alternatives until an acceptability threshold is met. Search
through alternatives, and choose the first one that exceeds your aspiration level
(Dudey and Todd, 2002).
If you were searching for a house, for instance, you may decide you want a clean house
in a suburban area that is below $300,000. It is possible that you would satisfice when
choosing a house to buy because it is near impossible to look at all available houses
everywhere and then select the best option. This means you would probably buy the
first house that met your aspiration level (Snook and Cullen, 2008).
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Satisficing Heuristic
Facione and Gittens 2013 “Snap Judgments – Risks and Benefits of Heuristic Thinking”
– Think Critically
Example: Being thirsty, how much water would we drink? Only enough to slake our
thirst.
Example: Seeking a new job, how hard would we look? Hard enough to find one that
meets whatever are our basic criteria for pay, proximity to home, nature of
the work, etc.?
Example: Having arrested a suspect who had the means, motive, and opportunity to
commit the crime, how hard can we expect police detectives to strive to
locate other suspects? Satisficing suggests hardly at all. The question of the
actual guilt or innocence of the subject becomes the concern of the
prosecuting attorney and the courts.
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1⁄N; Equality Heuristic
Allocate resources equally to each of N alternatives (De Miguel et al., 2009).
People use equality heuristically. This means that people do not always think deeply and
analytically about their decisions. Instead, they often take a quick read on a situation
and make a decision by applying some form of the idea of equality (Messick, 1995).
Subjects read a story in which five business partners needed to allocate the profits
and expenses of the partnership in a fair and reasonable manner. Each of the partners
worked independently and produced different gross incomes between $140 and $285.
The gross incomes were to be divided into expenses and profits. Subjects were asked
to fill in fair allocations in an accounting ledger. Three factors were manipulated: the
target of the allocation task (either the expenses or the profits), the causal
attributions for the differences in gross incomes (internal, external. or both). And
whether the subjects were asked to fill in both columns in the ledger (expenses and
profits) or just one (Messick and Schell, 1992).
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1⁄N; Equality Heuristic
The results supported the hypothesis that the subjects heuristically used equality to
make their allocations. Over 70% of the subjects allocated at least one column equally
(although the frequency of equality use varied as a function of both the target of the
allocation and the attribution given). Subjects allocated the target columns equally
more often than non-target columns, even though equality for one column implied
inequality for the other. The use of equality was also sensitive to the attribution given
for the performance differences. Differences due to external factors. i.e., the
number of people showing up at the market, produced the most equal allocations of
profits (with unequal expenses) while the internal attribution produced the highest
proportion of equal expense allocations (with unequal profits) (Messick and Schell,
1992).
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Default Heuristic
If there is a default, do nothing about it (Johnson and Goldstein, 2003).
If an agent is indifferent or conflicted between options it may involve too much
cognitive effort to base a choice on explicit evaluations. In that case she might
disregard the evaluations and chose according to the default heuristic instead which
simply states “if there is a default, do nothing about it” (doi:Gigerenzer, 2008).
There is inconsistency in many people’s choice of electricity. When asked, they say
they prefer a ‘green’ (i.e., environmentally friendly) source for this energy. Yet,
although green electricity is available in many markets, people do not generally buy it.
Why not? Motivated by behavioural decision research, we argue that the format of
information presentation drastically affects the choice of electricity. Specifically, we
hypothesise that people use the kind of electricity that is offered to them as the
default. They present two natural studies and two experiments in the laboratory that
support this hypothesis. In the two real-world situations, there was a green default,
and most people used it. In the first laboratory experiment, more participants chose
the green utility when it was the default than when ‘grey’ electricity was the default.
In the second laboratory experiment, participants asked for more money to give up
green electricity than they were willing to pay for it. They argue that changing
defaults can be used to promote pro-environmental behaviour. Potential policymaking
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applications are discussed (Pichert and Katsikopoulos, 2008).
Tit-For-Tat Heuristic
Cooperate first, keep a memory of size one, and then imitate your partner’s last
behaviour (Axelrod, 1984).
The tit-for-tat heuristic memorizes only the last of the partner’s actions and forgets
the rest (a form of forgiving) but can lead to better cooperation and higher monetary
gain than more complex strategies do, including the rational strategy of always
defecting (e.g., in the prisoner’s dilemma with a fixed number of trials).
If you interact with another person and have the choice between being kind
(cooperate) or nasty (defect), then:
(a) be kind in the first encounter, thereafter
(b) keep a memory of size one, and
(c) imitate your partner’s last behaviour (kind or nasty).
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Tit-For-Tat Heuristic
‘‘Keep a memory of size one’’ means that only the last behaviour (kind or nasty) is
imitated; all previous ones are ignored or forgotten, which can help to stabilize a
relationship. Tit-for-tat can coordinate the behaviour in a group in the sense that all
actors will end up cooperating but are simultaneously protected against potential
defectors. As with imitate your peers and the default heuristic, tit-for-tat illustrates
that the same heuristic can lead to opposite behaviours, here kind or nasty, depending
on the social environment.
If a husband and wife both cooperate when engaging in their first interaction and
subsequently always imitate the other’s behaviour, the result can be a long harmonious
relationship. If, however, she relies on tit-for-tat but he on the maxim ‘‘always be
nasty to your wife, so that she knows who is the boss,’’ her initially kind behaviour will
turn to being nasty to him as well. Behaviour is not a mirror of a trait of being kind or
nasty, but results from an interaction between mind and environment.
An explanation of the tit-for-tat players’ behaviour in terms of traits or attitudes
would miss this crucial difference between process (tit-for-tat) and resulting 1.236
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behaviour (cooperate or not) (Gigerenzer, 2010 alternately).
Imitate The Majority Heuristic
Look at a majority of people in your peer group, and imitate their behaviour (Boyd and
Richerson, 2005).
Imitate-the-majority heuristic, also referred to follow-the-majority heuristic. An
agent using the heuristic would imitate the behaviour of the majority of agents in his
reference group. For instance, in deciding which restaurant to choose, people tend to
choose the one with the longer waiting queue (Raz and Ert, 2008).
In a situation of uncertainty, individuals follow the actions or choices of the majority
of their peers regardless of their social status. The domain of pro-environmental
behaviour provides numerous illustrations for this strategy, such as littering behaviour
in public places (Cialdini, Reno, and Kallgren 1991), the reuse of towels in hotel rooms
(Goldstein, Cialdini, and Griskevicius 2008), and changes in private energy consumption
in response to information about the consumption of the majority of neighbours
(Schultz, Nolan, Cialdini, Goldstein, and Griskevicius 2007).
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Imitate The Successful Heuristic
Look for the most successful person and imitate his or her behaviour. Imitate-thesuccessful heuristic, also referred to follow-the-best heuristic. An agent using the
heuristic would imitate the behaviour of the most successful person in her reference
group (Boyd and Richerson, 2005).
Organizations extensively use groups to perform a variety of cognitive tasks and
collective decisions are essential for organizational performance. Reliance on groups in
social life is built on a strong assumption, namely that the array of information
exchanged, explored and integrated in groups enhances decision quality relative to
individual choices. Similarly, other species organize and work in collectives in order to
enhance their survival chances. For example, homing and migrating birds collectively
decide on communal routes that maximize their chances of survival and successful
arrival to their destination and swarms of bees and ants collectively choose new nest
sites on which their survival depends. Social interactions unfolding in such collectives
shape the emergence of collective choices that transcend a simple aggregation of
individual preferences or competencies (Meslec et al. 2014 plus cited references).
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Imitate The Successful Heuristic
Although groups have the potential to become superior (as interacting collectives) to
stand alone individuals or simple aggregation of individual actions or competencies, this
(emergent) potential is not always realized in real-life situations. Studies stemming
from the group synergy literature illustrate not only that groups do not manage to
achieve strong cognitive synergy (they fail to perform better than their best
individual member) but sometimes they even have difficulties to achieve weak
cognitive synergy (they perform worse than the average individual performance in the
group). Obviously, group synergy is a group emergent phenomenon that is rather
difficult to achieve in interacting groups. Therefore, understanding the way in which
individual choices and competencies are combined and coordinated through social
interactions in order to generate superior collective outcomes is of key importance to
understanding the emergence of collective cognitive competencies (Meslec et al. 2014
plus cited references).
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Availability Heuristic
Tversky and Kahneman (1973) proposed that people may use an
availability heuristic to judge frequency and the probability of
events. Using the availability heuristic, people would judge the
probability of events by the ease in which instances could be
brought to mind. Thus, using the availability heuristic, people would
judge an event to be more likely to occur if they could think of
more examples of that event.
For example after seeing many news stories of home foreclosures,
people may judge that the likelihood of this event is greater. This
may be true because it is easier to think of examples of this event.
Also people who read more case studies of successful businesses
may judge the probability of running a successful business to be
greater.
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Next Week
Judgement Biases
Give some thought to the assessments
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Menu
Overconfidence
Avoidance
Prudence
Avoidance
Recallability
Avoidance
Optimism And Wishful Thinking
Representativeness
Sample Size Neglect
The Law Of Small Numbers
Conservatism
Belief Perseverance
Anchoring
Avoidance
Confirmatory Bias
Avoidance
Availability Bias
Internalisation
Heuristics
Menu
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