Improving inspections of agricultural products
theory research has also focused on the notion of robustness in decision making under uncertainty, but without any information on probabilities. Ben-Haim (2006) has developed a new approach known as information-gap (info-gap) decision theory, which he designed for cases in which probability distributions for uncontrolled events are not available. “The essence of info-gap analysis is the pursuit of decisions that are robust in the sense that, roughly speaking, they maximize the range of uncertainty in the decision environment within which the decision maker is certain to achieve a specified performance requirement, the authors explain. “One decision is more robust than another if the range of uncertainty under which the performance requirement is met is larger. Given a performance criterion, a robust decision gives the decision maker maximum confidence that his or her performance criterion will be met.”
Info-gap and inspection
The authors adopt Ben-Haim’s approach to the problem of determining robust inspection protocols for detecting invasive species in imported agricultural goods. They prefer this approach because, in this problem, they are uncertain about the likelihood of the presence of an invasive species in the goods being inspected and the economic impact of inspection failure.
The authors note that info-gap decision theory is increasingly applied to real-world applications in which probabilities or a convex set of probabilities are hard to identify but acceptable performance is not. Applications include, but are not limited to:
- financial risk assessment (Ben-Haim 2005)
- search behavior in animal foraging models (Carmel and Ben-Haim 2005)
- policy decisions in marine reserve design (Halpern et al. 2006)
- natural resource conservation decisions (Moilanen et al. 2006)
- inspection decisions by port authorities to detect terrorist weapons (Moffitt et al. 2005) and invasive species (Moffitt et al. 2007; Moffitt et al. 2008)
- the choice of environmental policies (Stranlund and Ben-Haim 2008)
- engineering model-testing (Vinot et al. 2005)
Moffitt et al. (2007 and 2008) examine the inspection problem with info-gap models of uncertainty. Moffitt et al. (2007) develop a robust sample size for a risk averse decision maker faced with inspecting a generic shipping problem in which a shipment may contain at most a single contaminated item. Moffitt et al. (2008) evaluate the relative robustness of alternative inspection rules for a risk neutral decision maker when the number of contaminated items can vary, but they assume that the loss when an invasive pest gets past port inspections is known.
The authors of the current study extend this earlier work in two important directions. First, they allow several elements of the inspection problem to be uncertain, including the number of contaminated items in a shipment, the costs of inspections, and potential losses due to inspection failure. Second, they use recently available unpublished data provided by DHS to illustrate the potential of our model to determine robust inspection rules.
They demonstrate the utility of our approach by comparing robust inspection rules to the AQI 2 percent rule. They find that “optimal inspection rules provide significant increases in robustness over the AQI rule over a wide range of feasible performance criteria. Moreover, robust inspection rules suggest significantly more scrutiny of incoming shipments than the AQI rule.” This suggests a reallocation of federal resources to more intense inspections and away from efforts to deal with invasives that get through the inspection process.
-read more in L. Joe Moffitt, John K. Stranlund, and Craig D. Osteen, “Securing the Border from Invasives: Robust Inspections Under Severe Uncertainty,” Working Paper No. 2009-6 (University of Massachusetts Amherst, Department of Resource Economics)
