  Decision making under severe lack of information is a ubiquitous situation in
nearly every applied field of engineering, policy, and science. A severe lack
of information precludes our ability to determine a frequency of occurrence of
events or conditions that impact the decision; therefore, decision
uncertainties due to a severe lack of information cannot be characterized
probabilistically. To circumvent this problem, information gap (info-gap)
theory has been developed to explicitly recognize and quantify the implications
of information gaps in decision making. This paper presents a decision analysis
based on info-gap theory developed for a contaminant remediation scenario. The
analysis provides decision support in determining the fraction of contaminant
mass to remove from the environment in the presence of a lack of information
related to the contaminant mass flux into an aquifer. An info-gap uncertainty
model is developed to characterize uncertainty due to a lack of information
concerning the contaminant flux. The info-gap uncertainty model groups nested,
convex sets of functions defining contaminant flux over time based on their
level of deviation from a nominal contaminant flux. The nominal contaminant
flux defines a reasonable contaminant flux over time based on existing
information. A robustness function is derived to quantify the maximum level of
deviation from nominal that still ensures compliance for each decision. An
opportuneness function is derived to characterize the possibility of meeting a
desired contaminant concentration level. The decision analysis evaluates how
the robustness and opportuneness change as a function of time since remediation
and as a function of the fraction of contaminant mass removed.
