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Using a Bayesian Approach to Quantify Scale Compatibility Bias

  • Richard M. Anderson


    (Department of Geography and Environmental Engineering, 313 Ames Hall, The Johns Hopkins University, Baltimore, Maryland 21218)

  • Benjamin F. Hobbs


    (Department of Geography and Environmental Engineering, 313 Ames Hall, The Johns Hopkins University, Baltimore, Maryland 21218)

This paper proposes a new analytical framework to quantify and correct for scale compatibility bias in the assessment of trade-off weights in multiattribute value analysis. The procedure is demonstrated with an application to a fisheries management problem. Trade-off judgments are elicited from a group of fisheries experts with management responsibility in the Lake Erie basin. Then we use a Bayesian method to compute posterior probability distributions of attribute weights. In computing the Bayesian weights, our measurement model assumes that the weight ratios produced by each respondent's judgments are subject to random error and an unknown scale compatibility bias. Ratios are log-transformed and analyzed by a Bayesian linear model with a noninformative prior distribution. Posterior distributions are then developed for the weights and the bias. We estimate the compatibility bias for each person and, in most cases, it is found to be large and in the predicted direction, suggesting the importance of its consideration in deriving trade-off weights. In addition, the Bayesian framework is shown to be useful for quantifying the value of additional information about multiattribute weights. Finally, a simple heuristic procedure for assessing the weights appears to be effective in eliminating the bias.

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Article provided by INFORMS in its journal Management Science.

Volume (Year): 48 (2002)
Issue (Month): 12 (December)
Pages: 1555-1568

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Handle: RePEc:inm:ormnsc:v:48:y:2002:i:12:p:1555-1568
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  1. Gregory W. Fischer & Jianmin Jia & Mary Frances Luce, 2000. "Attribute Conflict and Preference Uncertainty: The RandMAU Model," Management Science, INFORMS, vol. 46(5), pages 669-684, May.
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  4. Han Bleichrodt & Jose Luis Pinto & Peter P. Wakker, 2001. "Making Descriptive Use of Prospect Theory to Improve the Prescriptive Use of Expected Utility," Management Science, INFORMS, vol. 47(11), pages 1498-1514, November.
  5. Gregory W. Fischer & Ziv Carmon & Dan Ariely & Gal Zauberman, 1999. "Goal-Based Construction of Preferences: Task Goals and the Prominence Effect," Management Science, INFORMS, vol. 45(8), pages 1057-1075, August.
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  7. Peter C. Fishburn, 1967. "Methods of Estimating Additive Utilities," Management Science, INFORMS, vol. 13(7), pages 435-453, March.
  8. Katrin Borcherding & Thomas Eppel & Detlof von Winterfeldt, 1991. "Comparison of Weighting Judgments in Multiattribute Utility Measurement," Management Science, INFORMS, vol. 37(12), pages 1603-1619, December.
  9. Paul J. H. Schoemaker & C. Carter Waid, 1982. "An Experimental Comparison of Different Approaches to Determining Weights in Additive Utility Models," Management Science, INFORMS, vol. 28(2), pages 182-196, February.
  10. Philippe Delquié, 1997. ""Bi-Matching": A New Preference Assessment Method to Reduce Compatibility Effects," Management Science, INFORMS, vol. 43(5), pages 640-658, May.
  11. F. Hutton Barron & Bruce E. Barrett, 1996. "Decision Quality Using Ranked Attribute Weights," Management Science, INFORMS, vol. 42(11), pages 1515-1523, November.
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