System Dynamics Pitfalls and Pointers

Soft Variables and Intangibles

A hard variable is one which is used to describe attributes or relationships in a problem to which physical laws apply or where governing business rules are readily formulated using algebraic operators linking other variables which, themselves, are readily quantifiable and quantification can be validated. Those constructs for which quantitative metrics and numerical data are available are sometimes termed hard data or hard variables—the term hard being intended to show that numerical data are more accurate and real than qualitative data—qualitative data being seen by many modellers as insubstantial and unreliable.
     In contrast soft variables are a class of variables, which includes a sub-class known as intangibles. These relate to attributes of human behaviour or effects that variations in such behaviour produce. As far as soft variables are concerned, numerical data are often unavailable or non-existent. Despite this, such variables are known to be critical to decision making and, therefore should be incorporated into system dynamics models. But, the challenge is to incorporate them into our models in ways that are both scientifically sound and logically defendable.
     The following explains how to incorporate soft variables and intangibles in system dynamics models, in combination with hard variables .

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