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- W848476123 abstract "Intelligence failures have been attributed to an inability to correlate many small piecesof data into a larger picture. This thesis has sought to investigate how the fusion andanalysis of uncertain or incomplete data through the use of Bayesian Belief Networks(BBN) compares with people’s intuitive judgements. These flexible, robust, graphicalprobabilistic networks are able to incorporate values from a wide range of sourcesincluding empirical values, experimental data and subjective values. Using the latter,elicited from a number of serving military officers, BBNs provide a logicalframework to combine each individual’s set of one-at-a-time judgements, allowingcomparisons 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 differentelicitation techniques lead to equivalent or differing judgements. The techniquescompared were: direct ranking of the variables’ perceived importance fordiscriminating between given hypotheses, likelihood ratios and conditionalprobabilities. The experimental results showed that individuals were unable tocorrectly manipulate the dependencies between information as evidence accumulated.The results also showed varying beliefs about the importance of informationdepending upon the elicitation technique used. Little evidence was found of a highcorrelation between direct normative rankings of variables’ importance and thoseobtained from the BBNs’ combination of one-at-a-time judgements. Likelihoodvalues should only be used as an elicitation technique by those who either regularlymanipulate uncertain information or use ratios. Overall, conditional probabilitydistributions provided the least troublesome elicitation technique of subjectivepreferences.In conclusion, Bayesian Belief Networks developed through the use of subjectiveprobability distributions offer a flexible, robust methodology for the development of anormative model for the basis of a decision support system for the quantitativeanalysis of intelligence data." @default.
- W848476123 created "2016-06-24" @default.
- W848476123 creator A5021640804 @default.
- W848476123 date "2008-08-04" @default.
- W848476123 modified "2023-09-27" @default.
- W848476123 title "Investigating the applicability of bayesian networks to the analysis of military intelligence" @default.
- W848476123 hasPublicationYear "2008" @default.
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