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Robert N. Phillips
CEO, Lasotell Pty Ltd.
www.lasotell.com.au
We ended the first
article in this series saying we would look at how to determine the adequacy of the model we
build of the "system" to try to understand the "system." (We also need
to verify the definition of "system.") Before we look any further at
the Soft Systems Methodology aspects, it is worth taking a detour
through Donald Norman's findings in his book,
The Design of Everyday Things,
about how we build internal models to understand how things work.
This article draws on just a few pages from Norman's book
that have parallels to Soft Systems Methodology models. The
complete book is certainly worth reading.
Norman points out that the simple things in life, such as
scissors, pens, and light switches, present us with a simple
model in which form follows function, so it is easy to
comprehend how they work.
However, understanding how things work is not always the
case in more complex systems. Norman's classic example of
such a disconnect is illustrated in the way his refrigerator
ought to work, on the basis of the controls it presents to
him—two dials, one for setting the freezer and one for
setting the fresh food compartment. The controls also
include recommendations for changing the settings. The
obvious assumption is that according to the visual
presentation, two dials mean two thermostats. Right?
Wrong—see the illustration on page 15 of Norman's book. The
reality is that the controls are not independent. One dial
is connected to a single thermostat (but in which
compartment?) and the other dial is connected to a gate
valve that controls the flow of cold air between the freezer
and the fresh food compartment. The conceptual model derived
from the physical presentation and the physical reality are
very different.
Norman shows that we construct our mental models through
experience, training, instructing, and by interpreting the
visible structure together with the observed actions of the
system. He calls the visible part of a device the system
image. For relatively straightforward systems, such as a
refrigerator, designers usually assume their model and the
system image are perfectly aligned and provide user
information accordingly. When this is not the case, as in
Figure 1, the users create their own conceptual models,
based on what they see and supplemented by how the device
behaves. Such models often have little or no alignment with
the true system image. The most obvious examples of such
disconnects are the average user's conceptual models for
operating or programming a mobile telephone, fax, and video
recorder.
Figure 1—Model Misalignments
Norman points out that alignment between the designer and
system image is essential because that is the only way the
designer communicates with the user. When we think about
that for a moment, we can see that the system image is even
more important because users tend to start playing with the
system long before they read the manual (if they ever do!).
Similar principles apply to human-based systems. Each
human-based system was designed by someone, even if it was a
long time ago. In organisations where there is no
documentation for the system, the designer's model has long
since been forgotten. People develop their own mental
models, based on a perceived image of how the system seems
to work. But the reality is often quite different,
particularly in large systems, which is one of the reasons
why "system" change or improvement projects fail—analysis
of the failures often shows that the initial mental model
was woefully inadequate. The important point is that in
systems involving numerous people, there are usually
numerous mental models.
One of the most important influences of the accuracy or
adequacy of the mental model depends very much on the source
of our knowledge. The fact is that when we do not have
certain or absolute knowledge, we rely on fragmentary
evidence and, if there is none, we use naïve (folk law)
knowledge or even imaginary (made up) "knowledge."
(Sometimes we give the latter knowledge a fancy
name—hypothesis!) But the consequence of inadequate
knowledge is that we have a faulty model. Norman's best
example of folk law knowledge describes a thermostat. If the
room is cold, will it warm up more quickly if the thermostat
is turned to maximum? People who subscribe to one of the
common thermostat folk theories (the timer or the valve
theory) will say yes. The right answer is no—the thermostat
is just an on-off switch that sets the machine fully "on" or
fully "off." Another well known example: if a bullet is
fired from a rifle at the same instant that an identical
bullet is dropped from an identical height, which one will
hit the ground first? The folk theory is that the dropped
bullet will hit the ground before the rifle bullet because
the rifle bullet is travelling so fast.
The difficulty with many folk law-based models is that they
are beyond the resources of people to test the answers in a
meaningful way. And therein lies a major difficulty in
trying to create models about human systems. If the system
is complex, people think it is too hard to test a scenario
to verify a predicted outcome.
In conclusion, the barriers we face, seen and unseen, in
trying to understand human-based systems are much the same
as those we face in trying to understand the working of
everyday things. We too easily create faulty models if we
encounter situations that project inappropriate images of
the system that is actually in place. Norman's examples are
a useful gauge for sanity checking that we are not
developing a faulty mental model. In the next article, we
will examine what Soft Systems Methodology calls a "system."
Reference
The Design of Everyday Things
Donald A. Norman
1988, New York, NY
Doubleday
ISBN: 0385267746
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