E pluribus analysis: applying a "superforecasting" methodology to the detection of homegrown violence
Huse, James G.
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This thesis examines investigative decision making, cognitive biases, talent sharing, and the relationship between the random nature of lone-actor violence and a set of predefined decision-making protocols. This research included running four simulations using the Monte Carlo technique, which illustrated that with the dedication of additional resources came a concomitant effect of diminishing returns, opportunity cost, and exposure to liability. The simulations also suggested that regardless of an investigative agency’s decision-making processes, the outcome relies on the randomness of the event. To demonstrate a prototype for a new method of threat analysis, a superforecasting team of analysts participated in an experimental survey. Nine participants reviewed five threat scenarios and assigned a score based on factors including the potential for violence and immediacy of the threat. Analysis in the survey was accurate for four out of five scenarios. Survey participants also answered six prospect theory questions, set in a homeland security context, to assess their decision making under uncertainty. Considered together, the results from the simulations and the two-part survey explain the relative strength of certain threat assessments. They distinguish what may be detectable from what is statistically unpredictable through the use of a collaborative and multidisciplinary method of analysis.
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