Multilevel Probabilistic Morphological Analysis for Facilitating Modeling and Simulation of Notional Scenarios

Despite advances in computational power, simulation-based experiments of complex problems remain time-consuming. Significant efforts are required to define an accurate representation of the problem and draw relevant conclusions about relationships between input parameters and output performance measures. To reduce the computational burden without sacrificing accuracy, it is customary to design a set of relevant scenarios. Doing so necessitates that the problem be decomposed into relevant parameters and that the consistency of these parameters be analyzed. To address this challenge, we propose a multilevel probabilistic morphological analysis. Traditionally, morphological analysis documents the decomposition of a system into its main components, and performs binary compatibility assessments to document relational data between component options. However, this approach leads to a rigid definition of the problem space and a static solution space that does not account for uncertain and dynamic behaviors associated with complex systems-of-systems. This research incorporates two important improvements. First, it explicitly incorporates a multilevel approach accommodating any successive decomposition steps that may be required when dealing with a complex system-of-systems problem. Second, this research introduces probabilistic cross-consistency assessments instead of traditional binary assessments to account for uncertain future states of the world. This probabilistic scheme is used to describe degrees of likelihood that two elements may coexist in a given scenario. This way, more complex interactions may also be captured and more realistic scenarios may be defined. Finally, the proposed method is applied to a problem investigating the design of architectures of detection systems for the protection of homeland critical assets.