Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis

Abstract

Intelligence analysis often tackles questions shrouded by deep uncertainty, such as those that deal with chemical and biological terrorism or nuclear weapon detection. In dealing with such questions, the task falls on intelligence analysts to assemble collected items of information and determine the consistency of the body of reporting with a set of conflicting hypotheses. One popular procedure within the Intelligence Community for distinguishing a hypothesis that is “least inconsistent” with evidence is analysis of competing hypotheses (ACH). Although ACH aims at reducing confirmation bias, as typically implemented, it can fall short in diagramming the relationships between hypotheses and items of evidence, determining where assumptions fit into the modeling framework, and providing a suitable model for “what-if” sensitivity analysis. This paper describes a facilitated process that uses Bayesian networks to (1) provide a clear probabilistic characterization of the uncertainty associated with competing hypotheses, and (2) prioritize information gathering among the remaining unknowns. We illustrate the process using the 1984 Rajneeshee bioterror attack in The Dalles, Oregon, USA.