Acting Irrationally
There are a number of biases that affect the way we as ‘Knowers’ process information and which prevent us from
acting rationally, some of these are:
·
The
Narrative bias
·
The
Corroborative bias
·
The
Experiential bias
·
The
Humanisation effect
·
The
Herding effect
·
Group
polarisation
·
The
Negative bias
Camus says that “Human beings are never rational, but they
are consistently rationalising”. This is sometimes called ‘backwards
rationalisation’, i.e. we do things first without thinking them through
properly or because we are afraid, angry or otherwise emotional and then, after
we have done something we come up with rationalisations or explanations or
excuses for why the things we did without thinking were actually the sensible
thing to do anyway.
The Narrative bias:
Creating a story around a piece of information feeds the
narrative bias. Psychologists discovered that when more detail is provided
(whether or not the data is specifically relevant) we are more inclined to
believe the tale. This is even true when the extra detail in fact limits the
probability of a certain event (Kahneman, Tvorsky).
Consider the following two scenarios:
·
A:
Tom is a healthy and fit 45 year-old. He dropped dead in a supermarket.
·
B:
Tom is a healthy and fit 45 year-old. He dropped dead in a supermarket, and was
discovered to have suffered from a heart defect that could have killed him at
any time.
Most choose B as more likely
But people do not realise that if B is ‘true’ the A is also
‘true’ because B is already contained as a possibility within A. In fact, B is
less probable than A because it limits the possible reasons for Tom’s death. With
A any number of causes of death are possible. With B
these other possibilities are specifically excluded, yet we still tend to
believe that B is more likely.
Why don’t we realise this? It has something to do with the
way our brains process information. We can easily imagine someone dying of
unknown heart condition (the explicit reason in B). In fact, we may know
someone who suffered something similar, we may have read about it or it might
just be something we could imagine so the presentation of the narrative frame
means our minds can more easily process and store the information – so B just
seems more likely, because there is extra information.
The Corroborative (pattern)
bias:
Seeks to confirm what we already think we know, and
dismisses data which conflicts with that knowledge
Consider deaths from cancer in any given year. Can we tell
from a patient’s age, sex, and type of cancer whether they are a smoker or not?
What we think we know often leads us to false conclusions. Because
we ‘know’ smokers die younger and that smokers get certain types of cancer
(e.g. lung cancer) more easily we look for patterns to be reproduced. So data
such as death aged 35 – 50, from lung cancer or similar, we assume points to
smokers’ behaviour and we ignore those deaths that appear to indicate more
natural causes – aged 80+, breast or prostate cancer or having already suffered
and beaten cancer.
However, smokers all have at least one story of a friend or
relative who smoked constantly, lived to be 100, and never contracted cancer. So,
of the following patients, who is more likely to suffer (and die from) cancer?
·
Patient
A is male, 37 years old, a factory worker, smokes 60 cigarettes a day, and has
a history of minor chest complaints
·
Patient
B is female, 72 years old, retired teacher, who has never smoked nor drank, and
had benign growth removed last year
The single most relevant fact (but one which is almost
ignored) is age – that has far greater determinacy in assessing cancer risk, so
B is more likely to die from Cancer. But is does not conform to all we think we
know, and is often the last to be considered.
The Experiential bias:
States when we remember something happening recently or as a
common event we will think of it as more probable
Consider the specific example of Tom, the man with the
undetected heart defect. As we have more experience of such a thing happening,
we think it more likely to occur to others, too. If we know someone who also
suffered an unexpected death because of a similar undiagnosed condition, we
think the story more likely. If that knowledge is recent, we again increase the
probability to compensate
The Humanisation
effect:
This is best described by the chilling quotation, “One death
is a tragedy, a million merely a statistic.” [Stalin]
We can only conceive of a million people as mass. We cannot
see them as a collection of individuals with lives, feelings, families and because
of this we cannot grieve for them. The brain is not made to cope with such
large numbers as concepts and after a while all begin to blur together. Studies
by Kahneman and Tvorsky
suggest ‘blurring effect’ occurs with crowds as small as forty people.
So one death is a tragedy, because
it is not just a death: it is an imaginable and quantifiable loss of a life
(with all the history, laughter, love and sorrow that it contained). However, we cannot conceive on a
million individuals in the same way so we are not affected by their lives so
powerfully. This is why charity companies choose the story of one child in
The Herding effect:
Herding effect is explained by evolutionary theory. In the
past man became a social animal because working together with other humans gave
us a competitive advantage over stronger, quicker, larger prey. No we no longer
need to hunt prey but we still have a need to ‘belong’, to fit in with the
group.
This is best illustrated in experiments by Asch and Milgram in early 1960s, and by the infamous Stanford Prison
Experiment.
Where, when others do something, even something we would
normally think of as immoral such as delivering high voltage electric shocks to
other people as in the Milgram experiments, we are
more likely to do it ourselves, because of this continuing need to run with the
herd.
This is also the reason why social punishments, such as ‘naming
and shaming’, appear to be so effective in controlling low-level, community
crime because often, fear of being stigmatised by one’s community is greater
than the fear of more official sanction by the state such as time serving time
in jail.
Group polarisation:
This works in tandem with the herding effect: when a
particular herd is joined, members then strive to be as much like the other
members as possible – they try to be like the best, stereotypical example of
the group that they can be. This does not mean that everyone tries to be the
same because there are lots of different herds – even the loners and
individuals join the ‘loner’ herd, and feel validated by it.
This is best illustrated by special interest or lobby groups
such as environmentalists, religious sects or campaigners where there is a
competition to be the most green, the most devout, and so on. This polarisation
can lead to dangerous extremes.
The Negative bias:
This is our sense of pessimism: when faced with two
potential outcomes, the more negative is considered more probable and this leads
us to put greater effort into guarding against negative outcomes than on working
towards positive ones. Essentially we are more worried by the potential costs
of getting something wrong than the possible benefits of getting it right –
even when we know that the statistics may be in favour of the more positive
option.
Remember these biases interact in different ways and many
may work together at the same time in the same situation to have an effect on
the Knower in question. Look at the ‘Risk Case Study’ on this page to see how
these biases work in a real example.