Page 21 - DCAP601_SIMULATION_AND_MODELING

Unit 1: Introduction to System Simulation

represents a legitimate model for the structure and serves the important purpose of providing Notes
guidance for its construction. Many definitions of a model can be found in the literature. One
that we feel is especially noteworthy was suggested by Shannon. ‘A model is a representation of
an object, system or idea in some form other than itself.’
Although outside the scope of our considerations, it is important to recognise a particular and
distinctive class of models called physical models. These provide the basis for experimentation
activity within an environment that mimics the physical environment in which the problem
originates. An example here is the use of scale models of aircraft or ships within a wind tunnel
to evaluate aerodynamic properties; another is the use of ‘crash-test dummies’ in the evaluation
of automobile safety characteristics. A noteworthy feature of physical models is that they can, at
least in principle, provide the means for direct acquisition of relevant experimental data.
However, the necessary instrumentation may be exceedingly difficult to implement.
A fundamental dichotomy among models can be formulated on the basis of the role of time;
more specifically, we note that some models are dynamic whereas others are static. A linear
programming model for establishing the best operating point for some enterprise under a
prescribed set of conditions is a static model because there is no notion of time dependence
embedded in such a model formulation. Likewise, the use of tax software to establish the
amount of income tax payable by an individual to the government can be regarded as the
process of developing.
Another important attribute of any model is the collection of assumptions that are incorporated
into its formulation. These assumptions usually relate to simplifications and their purpose is to
provide a means for managing the complexity of the model. Assumptions are invariably present
but often they are not explicitly acknowledged and this can have very serious consequences. The
assumptions embedded in a model place boundaries around its domain of applicability and
hence upon its relevance not only to the project for which it is being developed but also to any
other project for which its reuse is being considered.
Making the most appropriate choices from among possible assumptions can be one of the most
difficult aspects of model development. The underlying issue here is identifying the correct
balance between complexity and credibility where credibility must always be interpreted in
terms of the goals of the project. It’s worth observing that an extreme, but not unreasonable,
view in this regard is that the development of any model is simply a matter of making the
correct selection of assumptions from among the available options (often a collection of substantial
size).
The assumptions embedded in a model are rarely transparent. It is therefore of paramount
importance to ensure, via the documentation for the project, that all users of the model are
cognisant of its limitations as reflected in the assumptions that underlie its development.
As might be expected, the inherent restricted applicability of any particular model as suggested
above has direct and significant consequences upon the simulation activity. The implication is
simply that restrictions necessarily emerge upon the scope of the experiments that can be
meaningfully carried out with the model. This is not to suggest that certain experiments are
impossible to carry out but rather that the value of the results that are generated is questionable.
The phenomenon at play here parallels the extrapolation of a linear approximation of a complex
function beyond its region of validity. The need to incorporate in simulation software
environments a means for ensuring that experimentation remains within the model’s range of
credibility has been observed. Realisation of this desirable objective, however, has proved to be
elusive.

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