Risk Assessment Models

Risks can be assessed in numerous ways, with various assumptions and biases. Transgenic crops have been assessed in the United States by APHIS utilizing a rigid risk analysis framework that lacks a formal approach for detecting potential for mistakes. Certainly the system is designed to limit mistakes, but mistakes do occur, and their significance is unknown. Two different analytical frameworks are discussed below that could be used to supplement present risk assessment practices—a fault-tree analysis and an event-tree analysis (Lewis 1980, Haimes 1998). Fault-tree analysis can be used to investigate potential risk. For example, one can hypothesize that Bt corn adversely affects some non-target organism. A fault tree could lead the analyst to investigate thoroughly all potential causal pathways.

Fault-Tree Analysis

Fault-tree analysis logically evaluates risk by tracing backwards in time or backwards through a suspected causal chain the many different ways that a particular risk could happen (Lewis 1980, Haimes 1998). The analysis is conditioned on a given hazard, that is, the analyst must have a particular hazard in mind before the analysis can be conducted. This is both its strength and its weakness. It is a strength because the analysis focuses on the ways that risks occur and does not waste time evaluating the ways that risks do not occur. By concentrating on the known hazards, the analysis provides a comprehensive and efficient methodology for assessing risk. It is a weakness because unknown or unanticipated hazards cannot be evaluated simply because they have not been identified. Because the hazards associated with complex systems cannot all be unambiguously identified ex ante, this is its most serious weakness.

Figure 2.4 is a simplified fictitious example of a fault-tree analysis of the risk of Bt corn to a non-target insect species. The total risk is a weighted “or” gate of the risks in cornfields, near cornfields, and far from cornfields, where the weight is the relative proportion of the insect population in each habitat area (in the example in Figure 2.4 these weights are equal). In an “or” gate the risk would occur if any one of the inputs occurs, and in an “and” gate the risk would occur only if all of the inputs occur. This risk calculation is detailed for risks to the species inside cornfields, which is an “or” gate of the risks associated with each of the three most common transformation events in Bt corn. This risk, in turn, is an “and” gate of the probability that monarchs are in Bt corn of the specific event and the probability that they will be killed by pollen from the event. This box is further detailed for Event 176, in which mortality is related via an “and”

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