Facilitating Decision Choices with Cascading Consequences in Interdependent Program Networks

Abstract

Acquisition research has recently laid emphasis on the study of the cascading effects of interdependencies in the joint space of Major Defense Acquisition Programs (MDAPs). We are interested in proactively modeling the non-linear cascading effects of interdependencies in highly dependent networks. Specifically, in this report we examine DoD acquisition in the context of when 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 on 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 describe the informational value in the existing data and challenges inherent in the data collection process.