CROWD-BASED TECHNIQUES TO IMPROVE INTELLIGENCE ANALYSIS
Simeral, Robert L.
Dahl, Erik J.
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The essential nature of the homeland security enterprise involves making consequential and complex policy decisions under uncertainty. The inputs that policy makers use in making these decisions are facts, analyses, and predictions (which can fit a definition of intelligence)—all of which are subject to significant uncertainty. This thesis seeks to improve analysis by developing a crowd-based analytic methodology to address the problem of intelligence analysis while accounting for, and taking advantage of, the unique characteristics of the intelligence analysis process and the U.S. Intelligence Community culture itself. The thesis’s proposed methodology applies learning regarding crowdsourcing and prediction markets–based forecasting in a new context—that of intelligence analysis and the Intelligence Community. If the Intelligence Community implements the crowd-based analytic proposed methodology, which has achieved results in other contexts, it should improve its predictions of real-world events.
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