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An Experimental Study of the Effectiveness of Three Debiasing Techniques*
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This research was partially funded by an operating grant (A6743) to the second author from the Natural Science and Engineering Research Council of Canada. The authors are listed alphabetically and contributed equally to this study.
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Associate Professor of Marketing at Concordia University, Montreal, Canada. He holds a Ph.D. from Indiana University. and a B.A. and M.B.A. from Bogazici University, Istanbul. Turkey. Dr. Buylikkurt has published in the Journal of Consumer Reseurch and Journul o/Marketing Reseurrh. His current research i n t m t s include probabilistic choice models. measurement of subjective uncertainty and preferences. and interactive scaling.
Abstract
Subjective probability distributions constitute an important part of the input to decision analysis and other decision aids. The long list of persistent biases associated with human judgments under uncertainy [16] suggests, however, that these biases can be translated into the elicited probabilities which, in turn, may be reflected in the output of the decision aids, potentially leading to biased decisions.
This experiment studies the effectiveness of three debiasing techniques in elicitation of subjective probability distributions. It is hypothesized that the Socratic procedure [18] and the devil's advocate approach [6] [7] [31] [32] [33] [34] will increase subjective uncertainty and thus help assessors overcome a persistent bias called “overconfidence.” Mental encoding of the frequency of the observed instances into prespecified intervals, however, is expected to decrease subjective uncertainty and to help assessors better capture, mentally, the location and skewness of the observed distribution. The assessors' ratings of uncertainty confirm these hypotheses related to subjective uncertainty but three other measures based on the dispersion of the elicited subjective probability distributions do not. Possible explanations are discussed. An intriguing explanation is that debiasing may affect what some have called “second order” uncertainty. While uncertainty ratings may include this second component, the measures based on the elicited distributions relate only to “first order” uncertainty.