Using influence diagrams for data worth analysis

Decision-making under uncertainty describes most environmental remediation and waste management problems. Inherent limitations in knowledge concerning contaminants, environmental fate and transport, remedies, and risks force decision-makers to select a course of action based on uncertain and incomplete information. Because uncertainties can be reduced by collecting additional data., uncertainty and sensitivity analysis techniques have received considerable attention. When costs associated with reducing uncertainty are considered in a decision problem, the objective changes; rather than determine what data to collect to reduce overall uncertainty, the goal is to determine what data to collect to best differentiate between possible courses of action or decision alternatives. Environmental restoration and waste management requires cost-effective methods for characterization and monitoring, and these methods must also satisfy regulatory requirements. Characterization and monitoring activities imply that, sooner or later, a decision must be made about collecting new field data. Limited fiscal resources for data collection should be committed only to those data that have the most impact on the decision at lowest possible cost. ng influence diagrams in combination with data worth analysis produces a method which not only satisfies these requirements but also gives rise to an intuitive representation of complex structures not possible in the more traditional decision tree representation. This paper demonstrates the use of influence diagrams in data worth analysis by applying to a monitor-and-treat problem often encountered in environmental decision problems.