A flexible elicitation procedure for additive model scale constants
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This paper contributes to the process of eliciting additive model scale constants in order to support choice problems, thereby reducing the effort a decision maker (DM) needs to make since partial information with regard to DM preferences can be used. Procedures related to eliciting weights without a tradeoff interpretation of weights are justified based on assumptions that DM is not able to specify fixed weight values or if DM is able to do so, this would not be reliable information. As long as partial information is provided, the flexible elicitation procedure performs dominance tests based on a linear programming problem to explore the DM’s preferences as a vector space which is built using the DM’s partial information. To provide evidence of the satisfactory performance of the flexible elicitation procedure, an empirical test is presented with results that indicate that this procedure requires less effort from DMs.
KeywordsFlexible elicitation Partial information MAVT Tradeoff FITradeoff Additive model
- Barron, F. H., & Barrett, B. E. (1996). Decision quality using ranked attribute weights. Science, 42, 1515–1523.Google Scholar
- Ben Amor, S., Zaras, K., & Aguayo, E. A. (2016). The value of additional information in multicriteria decision making choice problems with information imperfections. Annals of Operations Research. doi:10.1007/s10479-016-2318-x.
- Franc, V., & Sonnenburg, S. (2009). Optimized cutting plane algorithm for large-scale risk minimization. Journal of Machine Learning Research, 10, 2157–2192.Google Scholar
- Gusmão, A. P. H., & Medeiros, C. P. (2016). A model for selecting a strategic information system using the FITradeoff. Mathematical Problems in Engineering. doi:10.1155/2016/7850960.
- Keeney, R. L., & Raiffa, H. (1976). Decision making with multiple objectives, preferences, and value tradeoffs. New York: Wiley.Google Scholar
- Li, J., Chen, Y., Yue, C., & Song, H. (2012). Dominance measuring-based approach for multi-attribute decision making with imprecise weights. Journal of Information & Computational Science, 9(8), 3305–3313.Google Scholar
- Medeiros, C. P., Alencar, M. H., & de Almeida, A. T. (2016). Hydrogen pipelines: Enhancing information visualization and statistical tests for global sensitivity analysis when evaluating multidimensional risks to support decision-making. International Journal of Hydrogen Energy, 41(47), 22192–22205. doi:10.1016/j.ijhydene.2016.09.113.CrossRefGoogle Scholar
- Palha, R. P., de Almeida, A. T., & Alencar, L. H. (2016). A model for sorting activities to be outsourced in civil construction based on ROR-UTADIS. Mathematical Problems in Engineering. doi:10.1155/2016/9236414.
- Park, K. S. (2004). Mathematical programming models for characterizing dominance and potential optimality when multicriteria alternative values and weights are simultaneously incomplete. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(5), 601–614.CrossRefGoogle Scholar
- Punkka, A., & Salo, A. (2013). Preference programming with incomplete ordinal information. European Journal of Operational Research, 231(1), 141–150. doi:10.1016/j.ejor.2013.05.003.
- Riabacke, M., Danielson, M., & Ekenberg, L. (2012). State-of-the-art prescriptive criteria weight elicitation (p. 24). Cairo: Hindawi Publishing Corporation Advances in Decision Sciences.Google Scholar
- Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
- Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics (2nd ed.). New York: McGraw-Hill.Google Scholar
- Winterfeldt, D. V., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.Google Scholar