Dynamic Decision Theory and Probabilistic Information Processings

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Abstract
The development of a dynamic decision theory will be central to the impending rapid expansion of research on human decision processes. Of a taxonomy of six decision problems, five require a dynamic theory in which the decision maker is assumed to make a sequence of decisions, basing decision n + 1 on what he learned from decision n and its consequences. Kesearch in progress on information seeking, intuitive statistics, sequential prediction, and Bayesian information processing is reviewed to illustrate the kind of work needed. The relevance of mathematical developments in dynamic programming and Bayesian statistics to dynamic decision theory is examined. A man-computer system for probabilistic processing of fallible military information is discussed in some detail as an application of these ideas and as a setting and motivator for future research on human information processing and decision making.

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