Although model simplifications are beneficial, a poor
choice of simplification, or over-simplifying a model,
may seriously affect the accuracy of the simulation.
• A good simplification is one that brings the benefits
of faster model development and run-speed (utility),
while maintaining a sufficient level of accuracy
(validity).
• How can a modeller determine whether a
simplification is good or not?
• There are two broad approaches.
What is a good simplification?
• The first is to use judgement in deciding whether a
simplification is likely to have a significant effect on
model accuracy.
• This should be determined by discussion between
the modeller, client and other members of the
simulation project team.
• The project specification is a useful mechanism for
explaining and discussing the efficacy of proposed
simplifications.
What is a good simplification?
• The second approach is to test the simplification in
the computer model; a form of prototyping.
• The modeller develops two computer models, one
with and one without the simplification.
• It is then possible to compare the results from the
two models to see the effect on accuracy.
• This, of course, provides much greater certainty over
the appropriateness of a simplification, but the
advantage of faster model development is lost.
What is a good simplification?
• Apart from maintaining a sufficient level of accuracy
(validity), a good simplification should not
compromise credibility either.
• Over-simplification can make a model less
transparent, reducing its credibility.
• It is sometimes necessary to include a greater scope
and level of detail than is required to assure the
accuracy of the model, in order to assure the model’s
credibility.
• A poor simplification is one that causes a client to
lose confidence in a model.