Analysis is one of the key components to the intelligence cycle, arguably the most crucial piece of the puzzle. There first has to be a need, from there analysts decide what we don't know, what we need to know, how we can we find the answers, etc... Once the information is gathered analysts have the daunting task of not only turning that information into actionable intelligence, but fitting these pieces of intelligence into the puzzle. To make their job all the more difficult, they are constantly working to overcome a slew of perceptual and cognitive biases that can negatively affect their results. Some of these biases include objectively evaluating an analyst's expectations. If the evidence even remotely suggests that the target is someone that the analyst expected to be the culprit, it's incredibly difficult to make a positive case for any evidence that suggests to the contrary. The bias of availability is merely the perceived likelihood of a result. When evidence suggests a result that the analyst perceives as unrealistic, they tend to discount all evidence supporting that end. A final example is that of consistency, when results are consistent even over a short period of time, which establishes a trend for the analyst that is conditioned to expect a similar result time and again. These are only a few of the many biases that cloud the judgment of all analysts, but there are techniques for overcoming these pitfalls.
One technique best used in the very beginning of the analytical process is to run a Key Assumptions Check. Nearly all situations are going to have some assumptions made by analysts that have any prior experience with similar situations. The trick is to not let these assumptions mold the estimates and analysis that comes out of the process. As a team, analysts lay out all of their assumptions and slowly pick them apart to ensure these assumptions are based on fact and logic. A second technique is the Analysis of Competing Hypotheses, particularly useful in scenarios where the possibilities are numerous and data is extensive. (Heuer 1999) Preferably as a team, lay out a matrix of all the evidence on one axis and possible hypotheses on the other. From there simply go down the line discussing which pieces of evidence support which hypotheses. There will probably be some arguments that rise from this discussion, but analysts may be shocked to find how the evidence they felt supported their hypothesis may similarly support another hypothesis, possibly better than their own. The final example is called Red Team Analysis, or in simpler terms, analysts putting themselves in their enemies’ “shoes.”(Tradecraft 2009) Many assumptions are made based on cultural and societal differences between the analyst and their objective. To overcome this bias, analysts are tasked with considering the situation from the perspective of the target. This can be a particularly challenging task when many of our adversaries have been culturally on opposite extremes from our own. How we would respond to a stimulus is often quite different from how a Middle Eastern society would respond. There are numerous techniques, which can and should be used regularly and in concert with one another to reach the most accurate un-biased conclusions.
A tradecraft primer: Structured analytic techniques for improving intelligence analysis. (2009). Washington, D.C.: US Government.
Heuer, R. J. (1999). Psychology of intelligence analysis. Washington, D.C.: Center for the Study of Intelligence, Central Intelligence Agency.
dqbnez
w6fr2.docx
0
