Since they are uncertain to the terrorists, U.S. decisions (e.g., interdiction) and uncertain events (e.g., detection) would be modeled using probability distributions. Any of the consequences that have credible models could be used. Decision trees can be used to find the terrorist strategy (a sequential set of decisions) that maximizes the terrorist objectives by averaging out and rolling back the decision tree. The decision tree can be solved multiple times for each single objective or can be solved once with combined consequences (Parnell, 2007). There are at least three ways of combining the consequences: converting each consequence to dollars, using a multiple attribute value model to normalize and weight the consequences, or using a multiple attribute utility model to normalize and weight the consequences. Each of the techniques has different assumptions and data requirements. All have been used on major national studies.
The 18 node event tree (with consequences) could be simplified especially if credible data are not available from subject matter experts. However, in order to use as much as possible of the existing 2006 BTRA event tree method, we directly converted the event tree to a decision tree. Using a format similar to Figure 3.4 in Chapter 3 of this report, Figure D.3 lists one possible set of assumptions that could be used to convert the DHS event tree to the bioterrorist decision tree. The figure adds new node numbers, type of node, rationale, average branches, and probability distributions to be assessed. The phases are the same but are not included due to space limitations on the page.
Several assumptions were made in Figure D.3. First, the old nodes numbers 1 (frequency of attack) and 16 (potential for multiple attacks) were deleted for the reasons discussed above. Second, we converted all terrorism decisions to decision nodes.1 That left six chance nodes: four interdiction nodes, one detection node, and one consequence node. Each of these would be uncertain to the bioterrorist. Third,