Tradecraft & Analysis Learning using Intelligence Scenarios with Methods- Anchoring (TALISMAN) Tradecraft & Analysis Learning using Intelligence Scenarios with Methods- Anchoring (TALISMAN)

ABSTRACT Analysts across the Intelligence Community (IC), including Department of Defense (DOD) intelligence components, face rapid growth in the volume and levels of fidelity of data to be transformed into intelligence. At the same time, the IC is experiencing a reduction of skilled senior analysts due to attrition and budget compression. At a time when all-source, multi-int, and open-source analysis are becoming critical to combating state-sponsored terrorism, transnational threats, and hostile proto-states, any knowledge, skills or experience gaps must be seriously examined and remedied. These challenges are familiar but new influences are exacerbating these gaps. Open source information has grown dramatically, driven by traditional news outlets offering digital editions and nontraditional conduits such as blogs and social media. Open-source intelligence (OSINT) has assumed greater importance as conflict zones are often in regions that lack traditional infrastructure and communications networks. In summary, analysts are facing unprecedented volumes of information and an acute requirement for advanced analytical techniques. A systemic improvement that could address these challenges is a new approach to training that embeds the effective use of proven, replicable methods for intelligence analysis in authentic scenarios. In this paper we describe a simulation enabled by online, scenario-based training incorporating a repeatable and proven analytic workflow. Tradecraft & Analysis Learning using Intelligence Scenarios with Methods-Anchoring (TALISMAN) applies scenario-based, technology-enriched training to create web-accessible interactive training scenarios. TALISMAN will equip analysts with the skills and knowledge to identify adversary locations, clandestine facilities, illicit activities, and other phenomenology using a proven composite signatures methodology. ABOUT THE AUTHORS Benjamin Bell is a Principal and founder of Aqru Research and Technology, LLC, where he applies artificial intelligence to simulation, training, human-machine interaction, and work support environments. Dr. Bell's research has addressed the use of simulation for training and education across a spectrum of applications, including K-12, higher education, military and national security training. He has held academic positions, leadership positions in industry, serves on the I/ITSEC conference committee and is an associate editor for the IEEE Transactions on Human-Machine Systems. Dr. Bell is an assistant adjunct professor at Embry Riddle, holds a Ph.D. from Northwestern University and is a graduate of the University of Pennsylvania. Michael Marks is founder and CEO of Intelligent Decision Partners, LLC, a professional services and consulting firm that specializes in providing multi-source intelligence services to the government and private sector. Mr. Marks has served as an Adjunct Staff Member of the Institute for Defense Analyses (IDA) and as Vice President for Corporate Development for SAIC. He also served in government, holding a number of high-level policy positions in both the Executive and Legislative branches