Hutson (The 7 Laws of Magical
Thinking) calls this ‘magical thinking’. Magical thinking is most observable in
fundamental attribution error Magical thinking is appealing because it
gives people a feeling of predictability and control and helps explain the
unexplainable, ‘happenstance’ and coincidence. The seeking of illusory patterns
and cause in information is also known as Apophenia. One of the best examples
of Apophenia or fundamental attribution error was when Apple launched ‘The
Shuffle’, an ipod device that played music randomly. The story of The Shuffle
shows how much people are tuned to recognizing patterns and so poor at
producing randomness. Listeners complained to Apple that the device would play
strings and patterns of songs from the one artist, that it wasn’t really
random. Apple had to introduce a new feature called ‘smart shuffle’ to allow
users to manually avoid repetition. Apple announced ‘we have made the shuffle less
random to make it feel more random’. Taleb wrote about this phenomena in
his book Fooled by Randomness. When an organisations thinking in safety is
commanded by a number, no wonder they misattribute the connection of data as
cause to culture.
Coincidence is the fuel for the fire
of magical thinking. We go to a sports game and see patterns in penalties or
the ‘hot hand’ in shooting goals. When things are uncertain and lack
predictability we attribute cause even when there is none. We seem unsatisfied
with the explanation that things with humans happen randomly and that there is
no deliberative or controlled cause. We use counterfactual thinking to
attribute blame when there is no real connection but only randomness. We attribute
the turn around in our football team’s performance to the ‘pep talk’ at half
time or the substitution of a player. We find cause in wearing the ‘right’
clothes or sitting in our favourite place in the stand. Then when there is a
break in the ‘pattern’ of wins or loss, we attribute all sorts of reasons as to
why our team lost the game. Many people attribute such superstitious or magical
thinking to cause and circumstance. Steve Waugh was obviously a successful
cricketer because he carried a red handkerchief in his pocket. Just watch the
tennis and observe the behaviour of the players, magical thinking (http://www.tennis.com.au/news/2012/07/13/friday-10-to-1-player-superstitions
).
Magical
thinking or attribution gymnastics doesn’t require much effort. There is a
cause to be found in any pattern or connection between data and outcome. It
doesn’t matter whether the attribution is illogical or unreal, it provides
comfort and that helps the attribution make sense.
One
fascinating exercise in attributive gymnastics with injury data seems to be the
delusional correlation of injury data to risk and safety culture. Time and time
again safety people parade out injury data as if it is the measure of culture.
This is certainly the case in Canberra where the Getting Them Home Safely
Report and the regulator constantly use injury data to attribute safety culture
causality (http://www.worksafe.act.gov.au/publication/view/1991).
The report presents discussion on safety culture as if injury data describes
safety culture. The data is used to attribute all kinds of connections to
culture that indeed, have no logical correlation. Without knowledge of
‘Regression to the Mean’ (Kahneman Thinking Fast and Slow), and with such bias
to regulatory-only thinking, the regulators find cause in injury data and
obtuse time frames and then overlook a host of other cultural indicators in
order to present their agenda.
What
often happens as a result of magical thinking is that the regulator
misattribute systems failure as culture failure or, introduces systems
solutions for cultural problems. As a result supposed ‘safety culture’ surveys
attribute a range of systems indicators as if they are what comprises culture.
The NSW Workcover Safety Culture Survey serves as an example of
this misattribution. There are so many safety culture indicators omitted from
the NSW Workcover Survey, for example no assessment of:
1.
Double speak
2.
Induction contradictions
3.
Language and discourse patterns
4.
Implicit knowledge
5.
Symbols and power
6.
Artefacts
7.
Organisational history
8.
Authoritarianism and attribution
9.
Denial, overconfidence, doubt
10.
Perception and motivation
11.
Hidden dimensions to communications
12.
Subversion in subcultures,
Such
misattribution of systems for culture comes from the confusion of understanding
injury data and regulatory failure as culture indicators. The assessment of the
dozen indicators above would give a far better understanding of culture than
the ‘naïve realism’ that misattributes compliance for safety ownership. In the
case of the Getting Them Home Safely Report, systems language was used
synonymously with culture throughout the report as if they were one and the
same. The outcome of the report has resulted in more intense systems, increased
inspections, audits, penalties, on the spot fines and replicated safety data
attribution yet an insidious change in the field to cultural measures such as
negativity, scepticism, cynicism and double speak. So, the by-product of more
systems has led to deterioration in culture rather than an improvement in
culture, similar can be observed in the effects of the Office of the Federal
Safety Commissioner on the culture of building and construction. All the data
shifting in the world is used to explain cultural change, when indeed, it is
not a measure of culture.
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