Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/1114
Title: | A Scalable Approach to Modeling Cascading Risk in the MDAP Network |
Authors: | Anita Raja Mohammad Hasan Shalini Rajanna Ansaf Salleb-Aouissi |
Keywords: | Major Defense Acquisition Program Selected Acquisition Reports Risk Prediciton |
Issue Date: | 30-Apr-2014 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Modeling Risk SYM-AM-14-063 |
Abstract: | 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. |
Description: | Acquisition Management / Defense Acquisition Community Contributor |
URI: | https://dair.nps.edu/handle/123456789/1114 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Size | Format | |
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SYM-AM-14-063.pdf | 2.07 MB | Adobe PDF | View/Open |
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