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Modelling

The objective of this page and the course is to act as an introduction to modelling. It is a technique that generally requires specialist modelling skills.

Mathematical models have a long history but what is now known as system dynamic modelling originates from the 1950's. As a result of advances in computing power and developments in modelling software, system dynamic models are now increasingly used in futures analysis. Other futures analysis tools are often used as part of a modelling project and scenario planning and modelling techniques are tending to converge. Models can be used to simulate both current systems and future scenarios. They are a powerful tool for reviewing future opportunities and challenges and policy options, particularly if appropriate visualisation methodology is applied. The example below shows a visualisation of a model of a epidemic.

©Business-Laboratory.com

As with all systems thinking, before commencing to build a model, it is vital to have a clear understanding of the required scope of the model and how it is to be used. It is then important to determine the elements that need to be included in the model and the relationships between them. Key to developing a robust model is effective engagement with experts and key stakeholders as part of the data collection and analysis. If looking into the future, it is also important to ensure that the underlying assumption will remain valid and there are no tipping points.

One of the issues on using models is that the 'customers' for the results may place too high dependence on them, beyond the prudent bounds of their reliability. Outputs of models are seen as the 'truth,' as they are based on a mathematical foundation with quantitative outputs; whereas other methods of futures analysis such as horizon scanning are often seen as 'imagination' and therefore thought of as less valid in assessing future situations. In reality they are complementary tools and models should be used with equal caution.

Models are powerful tools for assessing risk and managing events and they can be incorporated into the management of a system

If developing models or being presented with results from them, it is recommended that the following questions are asked:

  1. Is it possible to define how a system works, both now and in the future?
  2. Is the theory underpinning the model sound?
  3. Will the underlying assumptions remain valid?
  4. Is the system modelled subject to unintended consequences?
  5. Are there tipping points that will fundamentally change the underlying assumptions?
  6. Does the model accurately represent the starting conditions?
  7. Does the model accurately model the past (hind-casting)?
  8. Can the model be tested through short term forecasts?

There are likely to be accelerating increases in our scientific understanding, the quality and quantity of data, computation power and developments in modelling tools. This will lead to increasing application of system dynamic modelling for anticipating the future and the generation of more accurate probabilistic forecasts.

 
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