You can get feedback, the 'gold dust' and primary purpose of testing, from:
1. The exemplars
2. Yourself
3. The 'real world'
4. Other acquirers
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 ...
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