Insight network

Node type	Interpretation
	Decision variable: This is the variable that a decision-maker has the power to control. The decision variable should always be at the top of the chain of causality, even if this is a subchain i.e. it should not have any parent variables. Essentially the decision variable should be regarded as a decision that has to be made; since many factors affect all decisions it is not (in the case of INTARESE) an efficient use of resources to attempt to model what leads a decision-maker to make his decision.
	General variable: This is a deterministic function of the quantities it depends on.
	Chance variable: This is a variable which is uncertain and uncontrollable (in a direct sense).
	Index variable: This identifies the dimensions of the variable to which it is linked. Note that these dimensions do not have to be numeric, but can also be classes etc.
	Objective variable: This is a quantitative criterion that you are trying to optimize. A particularly important variable in relation to the interests of the intended users of the assessment output (i.e. it must be a means of effective communication of assessment results). It must be in causal connection to the endpoints of the assessment and thus address causality throughout the full chain. It should reflect the use/purpose of the assessment. It should address and be adapted according to the target audience. It should be the ‘leading component’ in the assessment process.
	Proxy variable: A variable that may be preferentially used as a surrogate measure for another variable. The direction of the arrow would be from variable “A” to the proxy.
	Causal arrow: This states a causal relationship (or influence) of one variable onto another.