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How to do a Modelling Project - Section 7


Stage 4: Testing your Model

Different forms of testing occur throughout the modelling process. The primary purpose of testing is to get feedback from:

The exemplars
Yourself
The 'real world'
Other acquirers


Testing your model with the exemplar

a. Test the components and steps of your model for accuracy.

As you gather information from your exemplar you can recap (in their words) as much of your model of their behaviours, abilities and states as you have. This will give them a chance 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 they can compare it to their own experience, 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.


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 starting Test criteria are triggered, through 'during' the Operations, until the ending 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?

Are there other contexts where the process would also be applicable?

At this stage you are acquiring the model 'for the moment'. You are not seeking to integrate it with any other 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.


Testing the model for real

Having tested your model with the exemplar, and used your own neurology as a test bed, your outcome changes. You now seek to test for the degree to which you can reproduce the required results. You want to compare the results you get with the results your 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 the model's efficacy and your competency.

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.

Other acquirers testing the model

If your modelling project is for other people (who were not involved in Stages 2-4), 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; etc. ...


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