Facilitating Decision Choices with Cascading Consequences in Interdependent Networks

Abstract

Our research goal is to proactively model the non-linear cascading effects of interdependencies in highly dependent networks. Specifically, we examine Department of Defense (DoD) acquisition from the context of the joint space of Major Defense Acquisition Programs (MDAPs), the space where MDAPs exchange and share resources for the purpose of establishing joint capabilities. Our hypothesis is that examining the interdependent regions among MDAPs from multiple perspectives using non-linear methods will allow for ''what-if'' analyses and will help decision-makers gain insight into the cascading effects of perturbations and take appropriate measures to handle them. Additionally, we also ascertain whether a popular decision theoretic model for decision-making and planning for cascading effects in the face of uncertainty is appropriate to study the cascading effects among MDAPs. Our approach is to use a case study to determine whether the data required to build an effective decision-theoretic model is available. We also capture the data investigation process and identify the challenges that were encountered. Our results show that it is possible to recast the study of cascading effects in MDAPs as a sequential decision problem. We also have captured the informational value in the existing data and the challenges inherent in the data collection process.