Describing and valuing interventions that observe or control decision situations

The value of information and value of control calculations have long been two separate parts of a decision analyst's efforts to extract as much insight as possible from a decision model. This paper unifies these concepts as interventions that modify the structure of the original problem, which have two key properties, purity and quality. Purity is an idealization that leads to Howard canonical form, clarifies the definition of control intervention, and allows us to extend and correct the calculation of the value of control. Quality is a characteristic that leads to generic models of imperfect intervention, which, because of their equivalence to any pure intervention, prevent misguided recommendations when the value of a perfect intervention is high but the value of a somewhat imperfect intervention is low. Quality is a number between 0 and 1 that normalizes and allows comparison of imperfect interventions between applications having very different value scales.