States. Since decision analysis also calculates the cumulative consequence distribution for each strategy, absolute risk could easily be displayed for each agent.
Decision analysis models are transparent. Commercial decision analysis tools provide a range of powerful sensitivity analysis tools (Clemen, 1996) to increase understanding and improve credibility. The model can be quickly resolved if any stakeholder provides an alternative set of data assumptions. Sensitivity analysis bar charts (Tornado diagrams) can be used to show the most significant data assumptions. Value of information calculations can be performed to find out what uncertainties have the most impact on the agent risk.
So far we have focused on the use of decision analysis as a modeling framework to support bioterrorism risk assessments. The Bioterrorist Decision Model would provide the baseline risk for the bioagents analyzed. Since the model can be run quickly, it could be a very useful tool to support DHS risk management decision making.
The bioterrorism risk is impacted by the U.S. ability to reduce the threat (prevent an attack or interdict an attack in progress), reduce the nation’s vulnerabilities, and mitigate the consequences given that an attack has occurred. Government agencies, including the intelligence community, the Department of Homeland Security, and the Department of Health and Human Services, expend significant resources each year to increase security against attacks on our nation, including bioterrorist attacks. In the Bioterrorist Decision Model, U.S. capabilities are reflected in the probabilities assigned to the uncertain nodes (the interdiction, detection, and consequence nodes). To assess the risk reduction of risk management alteratives we can modify the model to change the probabilities for each risk management alternative or set of alternatives. Due to the complexities of risk assessment mentioned in Chapter 2 of this report, the results may be initially non-intuitive. For example, a large reduction in the consequences of the highest-risk bioagent may not have a large reduction in overall risk since the second-highest-agent consequences might not be affected. In some cases, we would have to consider sets of alternatives since, in general, the risk reduction would not be additive. Some risk management alternatives may be synergistic (impact greater than the sum of their individual benefits) or complementary (impact less than the sum of their individual benefits).
There are several important insights from the analysis presented in this appendix. First, converting the event tree to a decision tree greatly simplifies the probability assessment tasks. Second, the decision tree should allow the tree to be solved using commercially available software using complete enumeration or Monte Carlo simulation. Third, the new challenge is how to develop consequence models that use the decision parameters in the decision tree that will allow for rapid evaluation of the decision tree for each path. Fourth, further opportunities exist to simplify the decision tree. For example, if a decision does not impact the consequences, it can be removed from the decision tree.
In the introduction we listed the most fundamental concerns with the 2006 BTRA methodology: not considering intelligent adversary decision making, huge data demands, more complexity than the available data support, lack of transparency for decision makers/stakeholders (see Chapter 3), and lack of a clear linkage to DHS risk management decision making. The Bioterrorist Decision Model effectively addresses each of these concerns.
The Bioterrorist Decision Model solves the problem of modeling an intelligent adversary by selecting the bioagents that will maximize the objectives of the terrorists. The model greatly reduces the huge data demands by converting terrorist decisions to decision nodes, deleting the two most problematic nodes—frequency of attack and multiple attacks—and not using probability distributions for each arc on each node. Finally, the model improves transparency by using commercially available software with built-in sensitivity analysis capabilities.
Clemen, R. 1996. Making Hard Decisions, 2nd edition. Belmont, Calif.: Duxbury Press.
Dillon-Merrill, R.L., G.S. Parnell, and D.L. Buckshaw. 2007. “Logic Trees: Fault, Success, Attack, Event, Probability, and Decision Trees.” In John G. Voeller (ed.), Wiley Handbook of Science and Technology for Homeland Security. Hoboken, N.J.: Wiley and Sons. Forthcoming.
Keeney, R.L. 1992. Value-Focused Thinking: A Path to Creative Decisionmaking. Cambridge, Mass.: Harvard University Press.
Keeney, R.L., and H. Raiffa. 1976. Decision Making with Multiple Objectives Preferences and Value Tradeoffs. New York: Wiley.
Kirkwood, C.W. 1997. Strategic Decision Making: Multiobjective Decision Analysis with Spreadsheets. Belmont, Calif.: Duxbury Press.
Maxwell, D.T. 2006. “Improving Hard Decisions.” OR/MS Today, pp. 5161. [Biannual survey of decision analysis software]
Parnell, G.S. 2007. “Multi-objective Decision Analysis.” In John G. Voeller (ed.), Wiley Handbook of Science and Technology for Homeland Security. Hoboken, N.J.: Wiley & Sons. Forthcoming.
Parnell, G.S., P.J. Driscoll, and D.L. Henderson (eds.). 2008. Decision Making for Systems Engineering and Management. Wiley Series in Systems Engineering, Andrew P. Sage (ed.). Hoboken, N.J.: Wiley and Sons.
Paté-Cornell, E.E., and R.L. Dillon. 2006. “The Respective Roles of Risk and Decision Analysis in Decision Support.” Decision Analysis 3(4):220-232.
Raiffa, H. 1968. Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Boston, Mass.: Addison-Wesley.