Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

For more than 30 years, probabilistic risk analysis (PRA) has been a major tool for assessing risks and informing risk management decisions by government and businesses in areas as diverse as industrial safety, environmental protection, and medical decision making. The more recent application of PRA to terrorism risk is new, however, and not uncontroversial, as evidenced by the commentary by Parnell and colleagues in the December 2008 issue of "Biosecurity and Bioterrorism." The National Research Council on Methodological Improvements to the Department of Homeland Security's Biological Agent Risk Analysis has argued that because of the adaptive nature of the terrorist adversary, alternative tools like decision trees, game theory, and agent-based modeling are needed to assess the risks of terrorist events and that probabilistic risk analysis is not valuable. In this commentary, we take a broad view of PRA, including any probabilistic approach involving tools like event trees, fault trees, decision trees, and influence diagrams. We make 2 points: (1) PRA is useful to quantify terrorism risk, and (2) event trees can help to decompose the universe of terrorism scenarios