Leveraging Structural Characteristics of Interdependent Networks to Model Non-Linear Cascading Risks

Abstract

This paper describes our continuing efforts to forge new ground in identifying the effects of interdependency on acquisition and, if needed, uncovering early indicators of interdependency risk so that appropriate governance oversight methods can then be isolated. Specifically, we seek to study the topologies of Major Defense Acquisition Programs (MDAPs) networks and associated cascading consequences of interdependencies in such highly dependent networks. Since the start of this new project phase a couple of months ago we have begun harnessing the extensive data that has been collected over the years in the form of Defense Acquisition Execution Summary (DAES) documents for the MDAPs. We present a road map of our research plan and our preliminary results in our ongoing efforts on leveraging network structure and automatic data extraction to study cascading risks. We will also identify the challenges to data acquisition.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2013
Accession Number
ADA584765

Entities

People

  • Anita Raja
  • Ansaf Salleb-aoussi
  • Mohammad R. Hasan
  • Shalini Rajanna

Organizations

  • University of North Carolina at Charlotte

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Computer Languages
  • Computer Science
  • Data Acquisition
  • Data Analysis
  • Department Of Defense
  • Governments
  • Information Systems
  • Link Analysis
  • Machine Learning
  • Military Acquisition
  • Network Science
  • Operations Research
  • Probability
  • Probability Distributions

Readers

  • Computational Modeling and Simulation
  • Defense Acquisition Program Management