Information selection in intelligence processing
Dimitrov, Nedialko B.
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In many intelligence agencies, the processing of data into usable information ready for analysis poses a significant bottleneck. Typically, much more data is available than what can be processed in the limited time available for processing. We formulate the problem faced by an intelligence collection unit, when processing incoming raw information for delivery to intelligence analysts, as an exploration-exploitation problem: the processor has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the processor's knowledge and examine algorithms that allow the processor to maximize the discovery of relevant data given a time limit. We derive insights on the performance of different algorithms using a simulated case study.
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