Leveraging Structural Characteristics of Interdependent Networks to Model Non-linear Cascading Characteristics

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. This document captures the progress that my team and I have made for the duration of this project. Specifically, we discuss the decision support architecture 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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 29, 2015
Accession Number
AD1014664

Entities

People

  • Anita Raja

Organizations

  • Cooper Union

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Batch Processing
  • Case Studies
  • Commerce
  • Computations
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Computers
  • Contracts
  • Conversion
  • Dictionaries
  • Digital Data
  • Image Processing
  • Information Processing
  • Information Systems
  • Machine Learning
  • Military Acquisition
  • Public Policy
  • Risk
  • Risk Analysis
  • Robotics
  • Text Mining

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Defense Acquisition Program Management
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.