Wednesday, April 29, 2009

Bayes' Theorem for Intelligence Analysis

Jack Zlotnick
CIA Historical Review Program


Author’s Note: Released by the CIA’s Historical Program in the early 1990’s, Jack Zlotnick wrote this piece in the 1970’s. At the time, the CIA was still in the process of testing Bayes’ Theorem. Due to the ongoing testing period (at that time), Zlotnick does not offer a position on the utility and validity of the Bayesian method with regards to intelligence. In fact, Zlotnick spends a considerable amount of time in the article discussing the ways the theorem should continue to be tested.

Summary
Due to the very nature of intelligence, analysts should be naturally interested in the Bayesian Theorem. Intelligence is probabilistic in nature. Intelligence analysts usually conduct their analysis based on incomplete evidence in which they must address probabilities (thus WOEP’s).

For intelligence applications, Bayes’ Theorem is represented by the equation R=PL. “R” is the revised estimate of the odds favoring one hypothesis over another competing hypothesis (the odds of a particular hypothesis occurring after new evidence is entered into the equation). “P” is the prior estimate on the hypotheses probabilities (the odds before considering the new evidence entered into the equation). The analyst must offer judgments about “L” or the likelihood ratio. This variable is the analyst’s evaluation of the “diagnosticity” of an item of evidence. For instance, if a foreign power mobilizes its troops, what are the chances that “X” will happen over “Y”.

The principle features of the Bayes Theorem distinguish it from conventional intelligence analysis in three ways. First, it forces analysts to quantify judgments that are not ordinarily expressed in numeric terms. Second, the analyst does not take the available evidence as given and draw conclusions. And third, the analyst makes his/her own judgments about the bits and pieces of evidence. He/she does not sum up the evidence as he/she would if he/she had to judge its meaning for a final conclusion. The mathematics does the summing up.

The author is skeptical that the complex tasks analysts are forced to consider can be reduced to numeric values. Bayes’ Theorem, however, may be useful for examining strategic warning by uncovering patterns of activity by foreign powers.
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2 comments:

  1. Zlotnick had two articles I know of in Studies in Intelligence:

    Zlotnick 1967. A theorem for prediction.
    Zlotnick 1972. Bayes' theorem for intelligence analysis.

    The first is fairly basic, and the second (your article) considers it more closely in the context of analysis.

    A more detailed example is:
    Fisk 1972. The Sino-Soviet border dispute: a comparison of conventional and Bayesian methods for Intelligence warning. Studies in Intelligence 16:2 53-62.

    Another from what I presume must have been the same ingroup:
    Schweitzer 1976. Bayesian analysis for intelligence: some focus on the Middle East. 20:2 31-44

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  2. I agree you cannot reduce such things to numerical value. I think what really matters is the ability to empathise with the other. The only way you can do that is by immersing yourself in their culture, thus necessitating the risk of agents turning. There is a fine line.

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