Topics 4 Model

We build models to discover the hidden and illuminate the opaque in real world systems, process and entities. Specifically a model or set of models are applied to:

• Estimating or recovering a lost or unknown value of a system parameter of interest.
• Measuring the impact of changes to the system.
• Verification of responses intended to influence the outcome of a system.
• Locating of high leverage or change resistant areas within a system.
• Identified of hidden factors within a system.

The key characteristic of a model is its bidirectional nature.

• Forward execution generates outcomes based on model assumptions and provide evidence.
• Backwards execution generates a set of plausible explanations and inputs for an observed outcome.

These characteristic are the corner stones of interactive iterative execution that delivers actionable knowledge.

4.1 Components

A model is built up by compositing together various components.

4.1.1 Parameters

Sometimes referred to as random variable, we deliberately used the term parameter here to avoid confusion with variables in the implementation environment.