Page 283 - Mechanical Engineers' Handbook (Volume 2)

274 Analysis, Design, and Information Processing

Structured modeling is a general conceptual framework for modeling. Cognitive maps, in-
teraction matrices, intent structures, Delta charts, objective and attribute trees, causal loop
diagrams, decision outcome trees, signal flow graphs, and so on are all structural models
that are very useful graphic aids to communications. The following are requirements for the
processes of structural modeling:

1. An object system, which is typically a poorly defined, initially unstructured set of
elements to be described by a model
2. A representation system, which is a presumably well-defined set of relations
3. An embedding of perceptions of some relevant features of the object system into the
representation system

Structural modeling, which has been of fundamental concern for some time, refers to the
systemic iterative application of typically graph-theoretic notions such that an easily com-
municable directed-graph representation of complex patterns of a particular contextual re-
lationship among a set of elements results. There are a number of computer software
realizations of various structural modeling constructs, such as cognitive policy evaluation
(COPE), interpretive structural modeling (ISM), and various multiattribute utility theory-
based representations, as typically found in most decision-aiding software.
Transformation of a number of identified issue formulation elements, which typically
represent unclear, poorly articulated mental models of a system, into visible, well-defined
models useful for many purposes is the object of systems analysis and modeling. The prin-
cipal objective of systems analysis and modeling is to create a process with which to produce
information concerning consequences of proposed actions or policies. From the issue for-
mulation steps of problem definition, value system design, and system synthesis, we have
various descriptive and normative scenarios available for use. Ultimately, as a part of the
issue interpretation step, we wish to evaluate and compare alternative courses of action with
respect to the value system through use of a systems model. A model is always a substitute
for reality but is, one hopes, descriptive enough of the system elements under consideration
to be useful. By posing a variety of questions using the model, we can, from the results
obtained, learn how to cope with that subset of the real world being modeled.
A model must depend on much more than the particular problem definition elements
being modeled; it must also depend strongly on the value system and the purpose behind
construction and utilization of the model. These influence, generally strongly, the structure
of the situation and the elements that comprise this structure. Which elements a client be-
lieves important enough to include in a model depend on the client’s value system.
We wish to be able to determine correctness of predictions and forecasts that are based
on model usage. Given the definition of a problem, a value system, and a set of proposed
policies, we wish to be able to design a simulation model consisting of relevant elements of
these three steps and to determine the results or impacts of implementing proposed policies.
Following this, we wish to be able to validate a simulation model to determine the extent
to which it represents reality sufficiently to be useful. Validation must, if we are to have
confidence in what we are doing with a model, precede actual use of model-based results.
There are three essential steps in constructing a simulation model:

1. Determination of those problem definitions, value systems, and system synthesis el-
ements that are most relevant to a particular problem
2. Determination of the structural relationships among these elements
3. Determination of parametric coefficients within the structure