Anyone interested in how to improve intelligence analysis might want to check out at the latest issue of the American Psychologist. It is a special issue devoted to understanding how research psychologists help solve real-world problems as parts of multidisciplinary teams (to riff on the title of the free-to-download introductory paper).

Three papers struck me as well worth a look. First up is a paper on The Science of Team Training. Intelligence managers and trainers keen to improve the performance of analyst teams should find this overview useful. They say: “we deliver (a) a historical account of team training research… (b) a synthesis of major contributions from… psychology; and (c) a collection of lessons learned in the science and practice of team training.” They have a useful summary table on the “Pillars and Key Principles of Effective Team Training.”

Second, a paper by Mellers and Tetlock provides a compact recapitulation of their excellent work, as leaders of the Good Judgement Project in IARPA’s ACE program, finding better ways to make forecasts. If you’re not already familiar with this stuff, you should read Superforecasting, but this paper contains a good precis of a slightly more technical nature. The latter half of the paper is interesting reflections on how the successful Good Judgement team worked.

The third is a bit more niche: an overview of Human Systems Engineering. Effective intelligence analyst teams are now complex human-machine systems. Analysts continuously interact with each other and with “machines” of various kinds (e.g. databases, analytical tools) and indeed they interact with each through these machines. The machines in these networks are increasingly sophisticated and indeed becoming incrementally more intelligent themselves. One challenge for those wanting to improve intelligence analysis is how to build the “machine” components of these complex systems, taking into account the nature of the human components. This is my take on what Human Systems Engineering is, and it is the business we are in at the SWARM Project.

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The papers mentioned above are behind a paywall. You might, if this your style, be able to access them via Sci-Hub. Thanks to geralt for the image