3 Systems modelling in practice
3.1 The steps to systems modelling
Systems modelling in practice usually involves six broad steps, within each of which there may be many subsidiary steps and some checking and revision. There is also likely to be iteration back to the earlier steps, as issues which call for changes in earlier decisions are uncovered.
Nevertheless, in my experience, the following six steps are likely to cover the basics.
Identify the system of interest, in particular specify the system boundary and specify the level of detail in which you (and other participants or stakeholders) are interested. This usually involves specifying what are to be considered sub-systems or elements within the overall system. Another way of expressing this is the level of aggregation of the system. For example, is it relevant to describe what happens to each person in a particular system, or would some average (an aggregate measure) be more appropriate?
Recognise the purpose of the systems study, and identify the purpose of the modelling activity within it. The most common purposes of systems studies are:
improving understanding of a situation
identifying problems or formulating opportunities
supporting decision making.
Modelling can be used to support all of these – by predicting system behaviour, by predicting the outcome of an intervention or by providing a basis for discussion or dialogue. This should lead to increased understanding.
From items 1 and 2 above, identify the main features or behaviours of the system of interest. These will then become the state variables in the model. By implication this step involves further simplification by specifying areas of the system that can be aggregated together or omitted from the modelling activity. This is a critical step in the process, since omitting an important feature at this stage can decrease, or even destroy, the value of the modelling activity (Box 5 gives an example).
Select a modelling technique that will address the features/behaviour of the specified system in a way that matches the specified purpose. After working through this pack, you will begin to see how different techniques are suited to different purposes.
Using the rules, techniques, tools and general experience or ‘case law’ of the selected modelling technique, develop an outline of a suitable model. Use this outline to check compliance with items 1, 2 and 3 above – or modify the model or adjust the specifications in items 1, 2 or 3.
Develop a full version of the model by a process of iteration, expansion and inclusion of detailed data as required.
Box 5 Omissions from the model
A topical example at the time of writing (1999) is the debate over the introduction of genetically modified (GM) foods into the UK. The models used to support their introduction include estimates of the likely economic and nutritional benefits to the UK, and of the health risks involved (which are believed to be very small). However, these models did not include any consideration of the potential effects of GM crops, from which the foods would be produced, on the wildlife of farmland. This omission has been one major contributor to the acrimony of the debate, and the rejection by a substantial minority of the entire argument in favour of such crops.
Look at each of the stages of the process listed as 1–6 above, and explain how each relates to the specified activities (verbs) of the conceptual model set out at the beginning of Section 2.5.
My mapping of the stages of the actual mathematical modelling process against the conceptual model is given below:
|Stage of process||Activities in conceptual model|
|1. Identify the system of interest||A, B, C, D|
|2. Identify purpose of activity||A (C?)|
|3. Identify features to be modelled||B,C,D,E|
|4. Choose technique||E|
|5. Develop outline||F, G, E|
|6. Develop full version of model||E, F, G, H,|
A key feature both of the conceptual model and the practical sequence is that of choosing a method of modelling. This step is crucial, for several reasons. As you will see as you work through the rest of this pack, each of the different modelling techniques can be more or less appropriate for different situations, and for different types of system which have been identified. In the succeeding material, we will return regularly to this question of choosing an appropriate model from among a range of quantitative techniques. But first, we need to consider when it is even appropriate to think about using any form of quantit