Assessing Uncertainty in Intelligence
This article addresses the challenge of managing uncertainty when producing estimative intelligence. Much of the theory and practice of estimative intelligence aims to eliminate or reduce uncertainty, but this is often impossible or infeasible. This article instead argues that the goal of estimative intelligence should be to assess uncertainty. By drawing on a body of nearly 400 declassified National Intelligence Estimates as well as prominent texts on analytic tradecraft, this article argues that current tradecraft methods attempt to eliminate uncertainty in ways that can impede the accuracy, clarity, and utility of estimative intelligence. By contrast, a focus on assessing uncertainty suggests solutions to these problems and provides a promising analytic framework for thinking about estimative intelligence in general.
|Date of creation:||Jun 2012|
|Date of revision:|
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Web page: http://www.ksg.harvard.edu/research/working_papers/index.htm
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