DIVIDE-AND-CONQUER - EFFECTS OF DECOMPOSITION ON THE ACCURACY AND CALIBRATION OF SUBJECTIVE-PROBABILITY DISTRIBUTIONS

This research tests the divide and conquer principle of decision analysis in the context of assessing subjective probability distributions (SPDs) for continuous quantities. In the Direct Assessment condition, subjects directly estimated five fractiles of an SPD for each of a set of uncertain almanac quantities. In two decomposition conditions, they assessed fractiles for a set of components for each quantity. In the Experimenter′s Decomposition condition, the component variables were specified by the experimenter, assessed by the subject, then combined according to an algorithm provided by the experimenter. In the Subject′s Decomposition condition, the components were identified and assessed by the subject, then combined using an algorithm generated by the subject. Contrary to the divide and conquer principle, decomposition did not significantly affect either the accuracy of assessed medians or the calibration of the SPDs. Nor was there a difference between the accuracy of medians or calibration of the experimenter′s and subjects′ decompositions. Instead, decomposition changed a bias towards underestimating uncertain quantities in the Direct Assessment condition into a bias towards overestimating them in both decomposition conditions. Similarly, direct assessment produced many high surprises (outcomes above the 99th fractile) whereas decomposed assessment resulted in many low surprises (outcomes below the 1st fractile). These findings point to the need for a more extensive empirical examination of the divide and conquer principle and its associated biases. © 1993 Academic Press, Inc.