A Scalable Approach to Modeling Cascading Risk in the MDAP Network

Report Number: NPS-AM-14-C11P17R01-063

Series: Acquisition Management

Category: Modeling Risk

Report Series: Proceedings Paper

Authors: Anita Raja, Mohammad Hasan, Shalini Rajanna, Ansaf Salleb-Aouissi

Title: A Scalable Approach to Modeling Cascading Risk in the MDAP Network

Published: 2014-04-01

Sponsored By: Acquisition Research Program

Status: Published--Unlimited Distribution

Research Type: Other Research Faculty

Full Text URL: http://acquisitionresearch.net/files/FY2014/NPS-AM-14-C11P17R01-063.pdf

Keywords: Major Defense Acquisition Program, Selected Acquisition Reports, risk prediciton


The overarching goal of our multi-year research agenda is to proactively model the non-linear cascading effects of interdependencies in Major Defense Acquisition Program (MDAP) networks. We use this to identify the associated data acquisition challenges so that appropriate governance mechanisms can then be isolated. In this paper, we describe our progress towards a scalable, automated approach for extracting and analyzing the data in the form of Selected Acquisition Reports (SAR) and Defense Acquisition Executive Summaries documents of a network of MDAPs to support a decision-theoretic risk prediction model. Automation is necessitated by the volume and complexity of the data. We will discuss the role of topic modeling, image extraction, and identification of topological features of the MDAP network in this approach.