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008180306s2017    onc    fo    f000 0 eng d
040 |aCaOODSP|beng
043 |an-cn---
0861 |aD69-50/2017E-PDF
24500|aRestructuring structured analytic techniques in intelligence |h[electronic resource] / |cWelton Chang ... [et al.].
260 |a[Toronto] : |bDefence Research and Development Canada, Toronto Research Centre, |cc2017.
300 |a1 v.
4901 |aExternal literature (P) ; |vDRDC-RDDC-2017-P113
500 |aCover title.
500 |aPublished in: Intelligence and National Security, 2017.
504 |aIncludes bibliographic references.
5203 |aStructured analytic techniques (SATs) are intended to improve intelligence analysis by checking the two canonical sources of error: systematic biases and random noise. Although both goals are achievable, no one knows how close the current generation of SATs comes to achieving either of them. We identify two root problems: (1) SATs treat bipolar biases as unipolar. As a result, we lack metrics for gauging possible over-shooting—and have no way of knowing when SATs that focus on suppressing one bias (e.g., overconfidence) are triggering the opposing bias (e.g., under-confidence); (2) SATs tacitly assume that problem decomposition (e.g., breaking reasoning into rows and columns of matrices corresponding to hypotheses and evidence) is a sound means of reducing noise in assessments. But no one has ever actually tested whether decomposition is adding or subtracting noise from the analytic process—and there are good reasons for suspecting that decomposition will, on balance, degrade the reliability of analytic judgment. The central shortcoming is that SATs have not been subject to sustained scientific of the sort that could reveal when they are helping or harming the cause of delivering accurate assessments of the world to the policy community
69207|2gccst|aSecurity intelligence
69207|2gccst|aResearch
7001 |aChang, Welton.
7102 |aDefence R&D Canada. |bToronto Research Centre.
830#0|aExternal literature (P) (Defence R&D Canada)|vDRDC-RDDC-2017-P113|w(CaOODSP)9.854437
85640|qPDF|s346 KB|uhttps://publications.gc.ca/collections/collection_2018/rddc-drdc/D69-50-2017-eng.pdf