Investigating the applicability of bayesian networks to the analysis of military intelligence

Abstract

Intelligence failures have been attributed to an inability to correlate many small pieces of data into a larger picture. This thesis has sought to investigate how the fusion and analysis of uncertain or incomplete data through the use of Bayesian Belief Networks (BBN) compares with people’s intuitive judgements. These flexible, robust, graphical probabilistic networks are able to incorporate values from a wide range of sources including empirical values, experimental data and subjective values. Using the latter, elicited from a number of serving military officers, BBNs provide a logical framework to combine each individual’s set of one-at-a-time judgements, allowing comparisons with the same individuals’ many-at-a-time, direct intuitive judgements. This was achieved through a serie s of fictitious and historical case studies. Building upon this work, another area of interest was the extent to which different elicitation techniques lead to equivalent or differing judgements. The techniques compared were: direct ranking of the variables’ perceived importance for discriminating between given hypotheses, likelihood ratios and conditional probabilities. The experimental results showed that individuals were unable to correctly manipulate the dependencies between information as evidence accumulated. The results also showed varying beliefs about the importance of information depending upon the elicitation technique used. Little evidence was found of a high correlation between direct normative rankings of variables’ importance and those obtained from the BBNs’ combination of one-at-a-time judgements. Likelihood values should only be used as an elicitation technique by those who either regularly manipulate uncertain information or use ratios. Overall, conditional probability distributions provided the least troublesome elicitation technique of subjective preferences. In conclusion, Bayesian Belief Networks developed through the use of subjective probability distributions offer a flexible, robust methodology for the development of a normative model for the basis of a decision support system for the quantitative analysis of intelligence data