Representing Variable Source Credibility in Intelligence Analysis
with Bayesian Networks

By: Ken McNaught and Peter Sutovsky

**Summary**:

McNaught and Sutovsky used a
Bayesian Network (BN) to show a computational platform to support information
fusion. The authors make the point that they are “not advocating the routine
use of this approach in intelligence analysis, partly due to the difficulty of
quantifying such models.” Although, they
believe using BNs can help to understand the aspects of uncertainty. This paper
explores the possibility of using BNs with the combination of evidence from credible
and variable sources.

One strength of utilizing a BN is
that it can help overcome cognitive biases. Other occupations can also utilize
BNs due to its flexible and powerful probabilistic modeling framework.
According to the authors, “risk modeling and forensic analysis are two fields
which share some commonalities with intelligence analysis and in which
applications of BNs are increasing.” Using a BN would permit the exploration of
“what if” statements, which could help create hypotheses and questions.

Below is an example of a BN of
information that has three main categories, a summary of the “analyst’s current
understand of the situation, an analysis of evidence gaps and key
uncertainties, and finally a list of actions required.” Using a BN encourages
additional thinking to find missing information, other resources, and new
investigated leads. This method also supports a collaborative environment so
colleagues can utilize each other’s work.

Although the authors believe BNs support logical reasoning,
they concluded that networks do not advocate routine quantification to help calculate
probabilities of various hypotheses. In
order to be able to calculate the probabilistic inference, each node would need
to be quantified with probability distributions. This would require many
probabilities including a number of uncertainties.

The authors conclude that BNs could help intelligence
analysts overcome some cognitive bases and help “provide important insights
regarding the combination of evidence and the sensitivity of inferences to
source credibilities.”

*For more BN examples in this research, please see the
source below.

**Critique**:

Although this research was somewhat complex to learn without
former knowledge of the subject, the authors did a good job breaking down the
material and giving a background of the theory. However, further explanations
of real world situations would have made the material easier to understand. I found
the authors interesting when they related the information to other academic
fields, making it a useful tools for other analysts.

**Source**:

Representing Variable Source Credibility in Intelligence
Analysis with Bayesian Networks. (2012). http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1021&context=asi&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Dbayesian%2520intelligence%2520analysis%26source%3Dweb%26cd%3D10%26ved%3D0CHIQFjAJ%26url%3Dhttp%253A%252F%252Fro.ecu.edu.au%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1021%2526context%253Dasi%26ei%3DgvMpVIuTNYiNyATPwoH4DA%26usg%3DAFQjCNHLT6Z9K3R5AKRaBy4SJXlCnPRulQ%26sig2%3DwJ2gFqDtacg0Mqzq3OauXA%26bvm%3Dbv.76247554%2Cd.aWw#search=%22bayesian%20intelligence%20analysis%22

Joy,

ReplyDeleteI looked at the BN examples in the article and noticed the advocacy of BNs as auxiliary models "to explore complex, uncertain problem situations and to learn about the sensitivity of probabilistic inferences to assumptions made."

Since the article's advocacy for BNs are not routine, do you think there are situations where a BN would be particularly useful? Maybe when an unforeseen event drastically alters the probabilities of one or more hypotheses?

Joy,

ReplyDeleteThe authors state that BNs should not be used routinely and are only showing increased use in a few areas of intelligence. Are their any projects you have worked on that you think this method would have assisted you?

Ricardo, I do believe that there are situations where BNs are completely useful, for example, and to also answer Harrison's question, a BN could have helped during the Latin American project in communications. It could have been used to narrow down and to quantify the areas of high risk for drug trafficking.

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