Abstract
There are several MCDM methods attempting to elicit criteria weights, ranging from direct rating and point allocation methods to more elaborated ones. To facilitate the weight elicitation, some of the approaches utilize elicitation methods whereby prospects are ranked using ordinal importance information, while others use cardinal information. Methods are sometimes assessed in case studies, or more formally by utilizing systematic simulations. Furthermore, the treatment of corresponding methods for the handling of the alternative’s values has sometimes been neglected. There is a wish for methods with as little cognitive demand as possible, lowering the hurdle to employ such methods at all. In this paper, we explore simplified models mixing cardinal and ordinal statements and demonstrate which of them are more efficient than established methods. It turns out that weights are much more insensitive to cardinality than values, which has implications for all ranking methods.
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Notes
- 1.
Of course, this is not intended to be totally normative. Any interpretation is possible and can be formally handled in the same way.
- 2.
In the terminology of this paper, this could have been called C + C, but we retain the name by which it is more widely known.
- 3.
SMART is represented by the improved SMARTER version by Edwards and Barron (1994).
- 4.
AHP weights were derived by forming quotients wi/wj and rounding to the nearest odd integer. Also allowing even integers in between yielded no significantly better results.
- 5.
The final score is, of course, not a percentage in the sense of Table 4, but rather a score of suitability taking both performance and robustness into account.
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Acknowledgements
This research was supported by the EU-project Co-Inform (Co-Creating Misinformation-Resilient Societies H2020-SC6-CO-CREATION-2017) and strategic grants from the Swedish government within ICT – The Next Generation.
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Danielson, M., Ekenberg, L. (2022). Comparing Cardinal and Ordinal Ranking in MCDM Methods. In: de Almeida, A.T., Ekenberg, L., Scarf, P., Zio, E., Zuo, M.J. (eds) Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis. International Series in Operations Research & Management Science, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-030-89647-8_2
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