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Difference between a technique and a model


I am currently writing a programme on problem solving and decision making.
Part of the indicative content of this programme is that it contains details of decision making techniqes and decision making models.
As far as I can see, the two are one and the same. Examples are:- pros and cons analysis, decision trees, force field analysis, PMI, Buriden's Ass.
Can anyone determine the difference between a model and a technique for me and possibly provide me with examples of each as I am stuck at the moment. I can't get my head around the fact that they are different. Am I wrong or are they the same thing?
Lisa Birch

3 Responses

  1. Models and Techniques Defined and Refined

    Models and Techniques are different.

    Your confusion is probably caused by the realm of problem solving where:
    1. Techniques can be used to verify models.
    2. Models are sometimes used as techniques (consider Ishigawa diagrams).

    My suggestion is to define what you mean by model and technique, for example:

    A model is a way of representing the essence of the real world.
    A technique is a tool, method, or activity used for a particular task.

    In this way a shoe-box-model of a house may be built to discover how the full scale house will look from all angles. A technique used to build the house would be brick laying.

    Most sciences use models extensively to try and understand or predict what will happen in the real world. In terms of thinking, we don’t know exactly how the brain works, so we build a model of how we believe it works. We test the model against real life, and if the model doesn’t work we build a new model and try again. Once a useful model is built it can be used to generate techniques, or predict how well a technique will work.

    To master problem solving, I have developed two distinct types of models: one is in diagram form, and the other is a mathematical formula. This has allowed me to develop techniques that are effective, and can be improved on over time. Incidentally idea generation in problem solving has been modelled with greater than 99% accuracy since 2001. So it is very important to use techniques that are the most recent, and scientifically proven.

    Hope this helps.

    David Archibald is Senior Consultant at ukIDEA (

  2. Yes, they ARE different things
    Firstly a model is, by definition and in this context, not a real thing – a technique is.

    Take PMI.
    The model is that any topic/situation can give rise to observations and ideas thart can be grouped under the headings Plus, Minus and Interesting.

    You *might* draw it as a single box with the word “SUBJECT” in it, with three arrows radiating from it (each with “Ideas” written on it) pointing to three more boxes with “PUS”, “MINUS” and “INTERESTING” in them, and the number 1, 2 and 3 on the outside in that order.

    Now you’ve got a model, but what are you going to do with it?

    According to de Bono you get people to come up with as many ideas as possible in each category in a fairly limited time (about 2-3 minutes each?), and that people can ONLY work on one category at a time, and that it MUST be in the sequence P, then M, then I.

    That’s the technique.

    If you get worthwhile results you still don’t know if it is true of “reality” – but at least you know it can be useful.

    If you get duff results you modify your model, or you develop a new one.

    Alternatively you can, of course, vary the technique and THEN go back and amend your model based on what you’ve learnt.

    Best wishes

  3. Or, more simply
    Hi Lisa!

    I assume you are referring to the learning outcomes for ILM certificate in first line management, so I’m not sure how useful the previous answers will be.

    My ‘official’ book on the subject does contain material on woollier decision-making methods such as PMI (this is a technique, not a model), but mainly covers mathematical decision making models commonly used in business, such as the EOQ model for stock control, resource allocation models, probability-based models, and forecasting models.

    The models themselves can be reduced to variables and mathematical formulae.

    The underlying techniques are simply mathematical techniques–such as substituting values for variables, taking a square root, mutiplying, dividing etc–and the model defines the specific series of steps you should take, such as evaluating a decision tree using probabilities and the rollback technique.

    In theory you could explain that the models rely on calculus, probability theory etc, but this is way beyond the scope of the ILM.

    The latest information I have is that the official book (ISBN 0 7506 5890 8) will be available in April, so you may be re-inventing the wheel.

    Best wishes



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