Cover: Causal Models and Exploratory Analysis in Heterogeneous Information Fusion for Detecting Potential Terrorists

Causal Models and Exploratory Analysis in Heterogeneous Information Fusion for Detecting Potential Terrorists

Published Nov 17, 2015

by Paul K. Davis, David Manheim, Walter L. Perry, John S. Hollywood

Download Free Electronic Document

FormatFile SizeNotes
PDF file 0.6 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

We describe research fusing heterogeneous information in an effort eventually to detect terrorists, reduce false alarms, and exonerate those falsely identified. The specific research is more humble, using synthetic data and first versions of fusion methods. Both the information and the fusion methods are subject to deep uncertainty. The information may also be fragmentary, indirect, soft, conflicting, and even deceptive. We developed a research prototype of an analyst-centric fusion platform. This uses (1) causal computational models rooted in social science to relate observable information about individuals to an estimate of the threat that the individual poses and (2) a battery of different methods to fuse across information reports. We account for uncertainties about the causal model, the information, and the fusion methods. We address structural and parametric uncertainties, including uncertainties about the uncertainties, at different levels of detail. We use a combination of (1) probabilistic and parametric methods, (2) alternative models, and (3) alternative fusion methods that include nonlinear algebraic combination, Bayesian inference, and an entropy-maximizing approach. This paper focuses primarily on dealing with deep uncertainty in multiple dimensions.

This paper is based on prior research sponsored by the Office of Naval Research and the Office of the Secretary of Defense.

This report is part of the RAND working paper series. RAND working papers are intended to share researchers' latest findings and to solicit informal peer review. They have been approved for circulation by RAND but may not have been formally edited or peer reviewed.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.