Bayesian Statistics In The Real World Of Intelligence Analysis: Lessons Learned
By Kristan Wheaton, Jennifer Lee, & Hemangini Deshmukh
Journal of Strategic Studies, vol. 2 n.1
In this article, Kris Wheaton, in collaboration with Jennifer Lee and Hemangini Deshmukh, agree that alternative methods, ones that are more structured, should be applied to the intelligence process in order to improve intelligence analysis. Recognizing the potential that Bayesian Statistics can bring to the field of intelligence, the author questions the ease in which entry-level intelligence analysts can apply and use this advanced statistical method.
Following the model of an experiment conducted by Gerd Gigenrenzer to test the accuracy of a diagnosis by two groups of doctors (with one group using traditional statistic formulations [5% or .05] and the other using natural frequencies [5 times out of 100]), the author tested 67 Senior Intelligence Studies students at Mercyhurst College. The findings of Wheaton’s experiment were extremely similar to that of Gigenrenzer’s, showing that groups who receive natural frequencies have a much higher rate of being accurate when using Bayesian statistics (79% versus 18% accuracy). This accuracy is attributed to the power of ‘framing’ questions. The author concludes, “natural frequencies are an effective method for encouraging Bayesian reasoning.”
In addition to the experiment, the article covers a brief how-to and overview of Bayesian Analysis. In short, Bayesian statistics is particularly useful due to its ability to take-in-account probabilities of one event affecting another, allowing the analyst to rationally update a prior assessment in light of new evidence. This process also helps in the reduction of two very common cognitive biases, the vividness and recency biases. See article for relevant examples of how the Bayes Theorem can be applied to intelligence-related issues.
The Bayes Theorem is illustrated below: