Yakov Ben-Haim, Policy neutrality and uncertainty: An info-gap perspective, Intelligence and National Security, published online 18.12.2015.
Reducing uncertainty is a central goal of intelligence analysis. ‘Reducing uncertainty’ can mean (1) Reduce ignorance or ambiguity or potential for surprise in describing situations or intentions, or (2) Reduce adverse impacts of ignorance, ambiguity or surprise on decision outcomes.
We make two claims. First, the second meaning needs greater attention in intelligence analysis. Uncertainty itself isn’t pernicious, but adverse impact of surprise is. Some policy options are less vulnerable to uncertainty than others. These less vulnerable (i.e. more robust) options can tolerate more uncertainty. Analysts should identify policy options that are robust to uncertainty.
Second, reducing the impact of uncertainty requires awareness of policymakers’ goals. This needn’t conflict with analysts’ policy neutrality. Tension between neutrality and involvement arises in economics, engineering, and medicine. The method of info-gap robust-satisficing supports decision making under uncertainty in these and other disciplines. Implications for intelligence analysis are explored in this paper. We discuss the assessment of Iraqi WMD capability in 2002.
