1. Testing your model with the exemplar
a. Test the components and steps of your model for accuracy.
For each exemplar, describe (in their words) as much
of the model as you have of their behaviours/abilities/states and ask
them to evaluate your description for accuracy.
Use your sensory acuity to calibrate that the pace of your
description enables the exemplar to 'try on' your model of them so
that they can compare it to their own model, component-by-component
and step-by-step.
Every response you get from your exemplar is feedback as to
the accuracy of your model. They are the world's expert on
their model, and at this stage, that's what you are attempting
to reproduce. Anything they think is confusing, illogical, or that
doesn't fit, is a signal that your model is incomplete.
b. Test the logic of your model for accuracy
After you have confirmation of the accuracy of your
model from the exemplar, you can start to make predictions as to how
the exemplar has or would 'run' their model in some as yet
unspecified context.
The aim is to test if your understanding of the exemplar's logic
enables you to go beyond what you have been specifically told or
observed.
2. Testing your model on your own
'Try on' your model by 'running it through' your system
Can you run the model - from 'before' when the start
Test criteria are triggered, through 'during' the
Operations until the end Test criteria are met, and on
to Exit 'after' (TOTE model)?
Would you expect to get the required results?
Does it all fit together?
Can you break it - under what conditions would you not get the
required results?
At this stage you are only acquiring the model 'for the moment'.
You are not seeking to integrate it with your pre-existing models,
instead you 'put them aside' while you run your tests. In other
words, you are self-modelling to obtain feedback from your own system
within an 'as if' frame.
3. Testing the model for real
Having had your model tested by the exemplar, and used your own
neurology as a test bed, your outcome changes. You are now seeking to
test the model for the degree to which you can reproduce the required
results. You want to compare the results you get with the results the
exemplars get. To do this you need feedback from the external world.
Two ways to do this are:
a. Prepare safe 'test conditions'
Taking into account the ecology of the wider system
and depending on the potential effects of your model not working, you
may want to establish some 'test conditions' in which to test it's
efficacy.
b. Go 'live'
The ultimate personal test. Can you get similar
results to your exemplars under similar conditions? And can you do
that consistently and under a variety of conditions? (Steve Andreas
has said that when he constructs a new model for change, i.e. a new
NLP technique, he has to test it out with 20-30 clients before he is
confident he has ironed out the majority of creases.)
Remember, your model may work perfectly but you may not yet have
enough background knowledge or experience of running it to get the
same results as your exemplars. Acquiring Einstein's problem solving
strategy won't make you an Einstein overnight, but you can expect it
to give you access to a different way of thinking about problems and
to a wider range of solutions than you had before.
4. Other acquirers testing the model
If part of your modelling project is for other people (who were
not involved in Stages 2-4) to make use of your model, your outcome
for testing changes again. Your design for an acquisition process
(Stage 5) should include testing by the acquirers. The feedback you
want now is: To what degree are the results the acquirers get
similar to those achieved by the exemplars.
And to reiterate:
Test, get feedback, adjust model, test again, get feedback, adjust ...
