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Using imprecise estimates for weights

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  • A Jessop

    (Durham University Business School)

Abstract

In multi-attribute decision problems the decision to differentiate between alternatives will be affected by the precision with which weights are specified. Specifications are imprecise because of the uncertainty characteristic of the judgements on which weights are based. Uncertainties are from two sources, the accuracy with which judgements are articulated and the inconsistency when multiple judgements are made and must be reconciled. These uncertainties are modelled using probabilistic weight estimates integrated by the Dirichlet distribution. This ensures the consistency of the estimates and leads to the calculation of significance of the differences between alternatives. A simple plot of these significant differences helps in the final decision whether this is selection or ranking. The method is used to find weight estimates in the presence of both types of uncertainty acting separately and together.

Suggested Citation

  • A Jessop, 2011. "Using imprecise estimates for weights," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1048-1055, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.46
    DOI: 10.1057/jors.2010.46
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    1. Hogarth, Robin M. (ed.), 1990. "Insights in Decision Making," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226348551, September.
    2. Paulson, Dan & Zahir, Sajjad, 1995. "Consequences of uncertainty in the analytic hierarchy process: A simulation approach," European Journal of Operational Research, Elsevier, vol. 87(1), pages 45-56, November.
    3. Hauser, David & Tadikamalla, Pandu, 1996. "The Analytic Hierarchy Process in an uncertain environment: A simulation approach," European Journal of Operational Research, Elsevier, vol. 91(1), pages 27-37, May.
    4. R Bañuelas & J Antony, 2007. "Application of stochastic analytic hierarchy process within a domestic appliance manufacturer," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 29-38, January.
    5. 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.
    6. Donald L. Keefer & William A. Verdini, 1993. "Better Estimation of PERT Activity Time Parameters," Management Science, INFORMS, vol. 39(9), pages 1086-1091, September.
    7. Saaty, Thomas L. & Vargas, Luis G., 1987. "Uncertainty and rank order in the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 32(1), pages 107-117, October.
    8. Levary, Reuven R. & Wan, Ke, 1998. "A simulation approach for handling uncertainty in the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 106(1), pages 116-122, April.
    9. Gregory W. Fischer & Mary Frances Luce & Jianmin Jia, 2000. "Attribute Conflict and Preference Uncertainty: Effects on Judgment Time and Error," Management Science, INFORMS, vol. 46(1), pages 88-103, January.
    10. Insua, David Rios & French, Simon, 1991. "A framework for sensitivity analysis in discrete multi-objective decision-making," European Journal of Operational Research, Elsevier, vol. 54(2), pages 176-190, September.
    11. Joseph J. Moder & E. G. Rodgers, 1968. "Judgment Estimates of the Moments of Pert Type Distributions," Management Science, INFORMS, vol. 15(2), pages 76-83, October.
    12. A Jessop, 2002. "Prioritisation of an IT budget within a local authority," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 36-46, January.
    13. Simon French, 2003. "Modelling, making inferences and making decisions: The roles of sensitivity analysis," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 11(2), pages 229-251, December.
    14. F. Hutton Barron & Bruce E. Barrett, 1996. "Decision Quality Using Ranked Attribute Weights," Management Science, INFORMS, vol. 42(11), pages 1515-1523, November.
    15. Edwards, Ward & Barron, F. Hutton, 1994. "SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement," Organizational Behavior and Human Decision Processes, Elsevier, vol. 60(3), pages 306-325, December.
    16. C. Perry & I. D. Greig, 1975. "Estimating the Mean and Variance of Subjective Distributions in PERT and Decision Analysis," Management Science, INFORMS, vol. 21(12), pages 1477-1480, August.
    17. Donald L. Keefer & Samuel E. Bodily, 1983. "Three-Point Approximations for Continuous Random Variables," Management Science, INFORMS, vol. 29(5), pages 595-609, May.
    18. Butler, John & Jia, Jianmin & Dyer, James, 1997. "Simulation techniques for the sensitivity analysis of multi-criteria decision models," European Journal of Operational Research, Elsevier, vol. 103(3), pages 531-546, December.
    19. Lipovetsky, Stan & Tishler, Asher, 1999. "Interval estimation of priorities in the AHP," European Journal of Operational Research, Elsevier, vol. 114(1), pages 153-164, April.
    20. Mustajoki, Jyri & Hamalainen, Raimo P. & Lindstedt, Mats R.K., 2006. "Using intervals for global sensitivity and worst-case analyses in multiattribute value trees," European Journal of Operational Research, Elsevier, vol. 174(1), pages 278-292, October.
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    6. Scholten, Lisa & Schuwirth, Nele & Reichert, Peter & Lienert, Judit, 2015. "Tackling uncertainty in multi-criteria decision analysis – An application to water supply infrastructure planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 243-260.

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