CROWD-BASED TECHNIQUES TO IMPROVE INTELLIGENCE ANALYSIS
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Authors
Srinivasan, Sridhar
Subjects
intelligence
analysis
prediction markets
crowdsourcing
methodology
superforcasting
analysis
prediction markets
crowdsourcing
methodology
superforcasting
Advisors
Simeral, Robert L.
Dahl, Erik J.
Date of Issue
2018-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
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.
Type
Thesis
Description
Series/Report No
Department
National Security Affairs (NSA)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.