RESEARCH METHOD COHEN ok

COMPUTER SIMULATIONS 249
On the other hand, Bailey (1994: 324–5)
reports several reservations about computer
simulations:
artificiality: they mimic life, rather than being
the real thing
cost: e.g. for the purchase of computer
simulations
training of participants: many computer simulations
require considerable training
quantitative problems: software, not just the
computer simulation itself, may require
programming expertise.
There are several potential concerns about, and
criticisms of, computer simulations. To the charges
that they artificially represent the world and that
they are a reductio ad absurdum, itcanbestated
that researchers, like theorists, strive to construct
the best fit with reality, to provide the most
comprehensive explanation, and that the closer
the analogy – the simulation – fits reality, the
better (Tymms 1996: 130). That is an argument
for refining rather than abandoning simulations.
We only need to know key elements to be able to
construct an abstraction, we do not need complete,
fine-grain detail.
To the charges that a computer simulation
is no better than the assumptions on which
it is built, and that a computer can only do
what it is programmed to do (rendering human
agency and freedom insignificant), it can be stated
that: simulations can reveal behaviours that occur
‘behind the backs’ of social actors – there are social
facts (Durkheim 1956) and patterns; simulations
can tell us what we do not know (Simon
1996) – we may know premises and starting points
but not where they might lead to or what they
imply; we do not need to know all the workings
of the system to be able to explain it, only those
parts that are essential for the model.
Other concerns can be voiced about simulations,
for example:
Complexity and chaos theory that underpin
many mathematical simulations might explain
diverse, variable outcomes (as in school effectiveness
research), but how do they enable
developers to intervene to promote improvement,
e.g. in schools – explanation here is retrospective
rather than prospective (Morrison
2002a); this charge is refutable in the possibility
of researchers to manipulate the parameters
of the variables and to see what happens when
they do this.
How does one ascertain the key initial conditions
to build into the simulation (i.e. construct
validity) and how do simulations from these
lead to prescriptions for practice
How acceptable is it to regard systems as the
recurring iteration and reiteration of the same
formula/model
In understanding chaotic complexity (in the
scientific sense), how can researchers work
back from this to identify the first principles or
elements or initial conditions that are important
– the complex outcomes might be due to
the interaction of completely different sets of
initial conditions. This is akin to Chomsky’s
(1959) withering critique of Skinner’s behaviourism
– it is impossible to infer a particular
stimulus from an observation of behaviour,
we cannot infer a cause from an observation or
putative effect.
Simulations work out and assume only the interplay
of initial conditions, thereby neglecting
the introduction of additional factors ‘on the
way’, i.e. the process is too deterministic (that
said, there are computer simulations in which
the computer ‘learns’ during the simulation).
What is being argued here is only common
sense, that the interaction of people
produces unpredicted and unpredictable behaviour.
That is also its greatest attraction – it
celebrates agency.
Planned interventions might work at first but
ultimately do not work (a reiteration, perhaps,
of the Hawthorne effect); all we can predict is
that we cannot predict.
Manipulating human variables is technicist.
Chapter 10