NETEST: Estimating a Terrorist Network's Structure—Graduate Student Best Paper Award, CASOS 2002 Conference

  • Authors:

  • Matthew J. Dombroski;Kathleen M. Carley

  • Affiliations:

  • Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA. mdombros@andrew.cmu.edu;Institute for Software Research International, Carnegie Mellon University, Pittsburgh, PA 15213, USA. kathleen.carley@cmu.edu

  • Venue:
  • Computational & Mathematical Organization Theory
  • Year:
  • 2002

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Abstract

Since the events of September 11, 2001, the United States has found itself engaged in an unconventional and asymmetric form of warfare against elusive terrorist organizations. Defense and investigative organizations require innovative solutions that will assist them in determining the membership and structure of these organizations. Data on covert organizations are often in the form of disparate and incomplete inferences of memberships and connections between members. NETEST is a tool that combines multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network's structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network's size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.