Experimental investigations by psychologists have revealed significant deviations of actual human decision behavior from classical rational theories of judgment and decisionmaking. Weakening the assumptions of the latter has led to the development of new theories such as prospect theory or rank-dependent subjective expected utility theory. The first part of the paper reviews recent work in this area with an emphasis on its implications for prescriptive modeling. These implications manifest themselves at every step of the decision analytic process, potentially limiting the effectiveness of these tools, as discussed in the second part of the paper. Finally, it is proposed that it may be possible to enhance the effectiveness of decision analytic tools or numerical optimization if they are coupled with some symbolic reasoning capability. The third part of the paper discusses the relevance of artificial intelligence techniques for enhancing existing decision tools. The paper shows some links between the descriptive decision research of psychology, prescriptive models of operations research, and symbolic reasoning research of artificial intelligence. The paper argues for the necessity and usefulness of future coordination of research in these three areas.
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