An Iterative Bayesian Decision Aid: Toward Improving the User-Aid and User-Organization Interfaces

Abstract
An iterative Bayesian decision aid was developed to give users greater control of the decisionmaking process and thereby increase the likelihood of aid implementation. The aid permits users to modify the initially assessed likelihood ratios and resulting posterior odds for the hypotheses under consideration until they feel comfortable with the inferred implications of the data. The aid was applied to the area of military tactical intelligence analysis and evaluated experimentally by experienced analysts on a training task representative of that decisionmaking environment. Experienced and inexperienced (but trained) analysts working with the aid were 1) better able to discriminate between the most and least likely hypotheses and 2) assigned final likelihoods to the hypotheses that were more similar to normative likelihoods derived from Bayes' theorem than unaided analysts. In addition, analyses of individual analysts revealed large differences in decisionmaking strategies. These differences demonstrate that users can approach the inference problem using their own decisionmaking style, thereby ensuring user control over all stages of the decisionmaking process.

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