Publications

Kelic, Andjelka; Kaplan, Paul G.

Abstract not provided.

Hernandez, Jacquelynne H.; Kaplan, Paul G.; Conrad, Stephen H.

Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subset of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.

Wyss, Gregory D.; Pless, Daniel J.; Rhea, Ronald E.; Otero, Consuelo J.; Kaplan, Paul G.; Aguilar, Richard A.; Conrad, Stephen H.

Abstract not provided.

Wyss, Gregory D.; Otero, Consuelo J.; Pless, Daniel J.; Rhea, Ronald E.; Kaplan, Paul G.

Sandia National Laboratories performed a two-year Laboratory Directed Research and Development project to develop a new collaborative risk assessment method to enable decision makers to fully consider the interrelationships between threat, vulnerability, and consequence. A five-step Total Risk Assessment Methodology was developed to enable interdisciplinary collaborative risk assessment by experts from these disciplines. The objective of this process is promote effective risk management by enabling analysts to identify scenarios that are simultaneously achievable by an adversary, desirable to the adversary, and of concern to the system owner or to society. The basic steps are risk identification, collaborative scenario refinement and evaluation, scenario cohort identification and risk ranking, threat chain mitigation analysis, and residual risk assessment. The method is highly iterative, especially with regard to scenario refinement and evaluation. The Total Risk Assessment Methodology includes objective consideration of relative attack likelihood instead of subjective expert judgment. The 'probability of attack' is not computed, but the relative likelihood for each scenario is assessed through identifying and analyzing scenario cohort groups, which are groups of scenarios with comparable qualities to the scenario being analyzed at both this and other targets. Scenarios for the target under consideration and other targets are placed into cohort groups under an established ranking process that reflects the following three factors: known targeting, achievable consequences, and the resources required for an adversary to have a high likelihood of success. The development of these target cohort groups implements, mathematically, the idea that adversaries are actively choosing among possible attack scenarios and avoiding scenarios that would be significantly suboptimal to their objectives. An adversary who can choose among only a few comparable targets and scenarios (a small comparable target cohort group) is more likely to choose to attack the specific target under analysis because he perceives it to be a relatively unique attack opportunity. The opposite is also true. Thus, total risk is related to the number of targets that exist in each scenario cohort group. This paper describes the Total Risk Assessment Methodology and illustrates it through an example.

Pless, Daniel J.; Kaplan, Paul G.; Conrad, Stephen H.; Wyss, Gregory D.; Rhea, Ronald E.; Aguilar, Richard A.; Otero, Consuelo J.

Abstract not provided.

Stamber, Kevin L.; Ellison, James; Jones, Dean A.; Kaplan, Paul G.; Kelic, Andjelka; Starks, Shirley J.; Taylor, Robert A.; Aamir, Munaf S.; Turk, Adam L.; Vugrin, Eric D.; Warren, Drake E.; Bernstein, Orr Y.; Beyeler, Walter E.; Brodsky, Nancy S.; Brown, Theresa J.; Corbet, Thomas F.; Davis, Chad E.; Ehlen, Mark E.

Abstract not provided.

Kaplan, Paul G.

Abstract not provided.