The GAVONTS Technique - 2

When we 'model' complex systems, there are two basic approaches to model selection:

1. We understand the internal dynamics of a system well enough to create a bespoke model that represents its behaviour mathematically.
2. We use a particular class of generic model; for example: a linear regression model, or a standard stochastic time-series model.

However, there are many situations where we might believe that a system can, in principle, be modelled, but we do not have a sufficiently clear insight into its internal dynamics to be able to construct such a model.

Also, many of these cases are fundamentally non-linear, and linear approximations often break down quickly and become unreliable. If we try to use them to predict or extrapolate a system's behaviour - we may not be able to trust the results that are produced.