Making descriptive use of prospect theory to improve the prescriptive use of expected utility

This paper proposes a quantitative modification of standard utility elicita tion procedures, such as the probability and certainty equivalence methods, to correct for commonly observed violations of expected utility. Tradition ally, decision analysis assumes expected utility not only for the prescript ive purpose of calculating optimal decisions but also for the descriptive p urpose of eliciting utilities. However, descriptive violations of expected utility bias utility elicitations. That such biases are effective became cl ear when systematic discrepancies were found between different utility elic itation methods that, under expected utility, should have yielded identical utilities. As it is not clear how to correct for these biases without furt her knowledge of their size or nature, most utility elicitations still calc ulate utilities by means of the expected utility formula. This paper specul ates on the biases and their sizes by using the quantitative assessments of probability transformation and loss aversion suggested by prospect theory. It presents quantitative corrections for the probability and certainty equ ivalence methods. If interactive sessions to correct for biases are not pos sible, then the authors propose to use the corrected utilities rather than the uncorrected ones in prescriptions of optimal decisions. In an experimen t, the discrepancies between the probability and certainty equivalence meth ods are removed by the authors' proposal.