Consequence Simulation and Validation (CS&V)

Complex systems do not readily submit to reductionist analysis where an individual element is examined in isolation from other elements. This reductionist approach ignores the linkages that exist between system elements, and the fact that the linkages cannot be deduced from individual analysis. Complex systems exhibit a rich variety of behaviors, including many that are counter-intuitive. To capture these effects, complex systems should be analyzed from a holistic rather than a reductionist point of view.

To analyze interdependent systems in a more holistic way, Sandia has developed models utilizing an agent-based approach. An agent is an autonomous, computational entity that can be viewed as perceiving and acting upon its environment and is autonomous in that its actions or behavior depend at least partially on its own experience. This simulation technology capitalizes on technological advances in evolutionary learning algorithms and massively parallel computing. Interactions among elements are modeled individually by intelligent software agents representative of real world decision-makers. The agent-based model provides a picture of systems that can be examined both computationally and analytically, offering new ways of experimenting with and theorizing about the impact of perturbation or shocks.

We have found that agent based simulation models have the potential to improve the accuracy of complex system forecasting and to provide new insights into the factors or variables that are the primary drivers of emergent behaviors in multiple interdependent systems.

Areas of Research:

• Adaptive Intelligence Aggregation
  (Contact: Mark B. E. Boslough)
• Economic Systems
  (Contact: George A. Backus)
• Global Climate Change
  (Contact: Mark B. E. Boslough)
• Infrastructure
  (Contact: David A. Schoenwald )
• Logistics
  (Contact: David A. Schoenwald)
• Robotic Behavior
  (Contact: Mark B. E. Boslough)
• Soldier Behavior
  (Contact: Mark B. E. Boslough)