A Distributed Dynamic Intelligence Aggregation Method
M. B. Boslough · 2004
This paper describes an intelligence aggregation system for generating actionable knowledge by addressing the dynamical and distributed nature of the problem. Ever-changing and compart-mentalized information can be used for evaluat-ing hypotheses by trading among analysts. Trad-ing rules are designed for self-assembly of meta-data structures that attach actual data to the trades, allowing analysts to associate hypotheses with the raw data. Adaptive aggregation refers to built-in error correction by weighting the most current and relevant information, thereby ad-dressing the dynamic aspect. The raw data and sources of information remain distributed as re-quired for reasons of security, privacy, or turf. Knowledge is generated collectively using a sys-tem of hypothesis generation, investment, and probability discovery through trading. 1.