Abstract
Sound assessment and ranking of alternatives are fundamental to effective decision making. Creating an overall ranking is not trivial if there are multiple criteria, and none of the alternatives is the best according to all criteria. To address this challenge, we propose an approach that aggregates criteria scores based on their joint (probability) distribution and obtains the ranking as a weighted product of these scores. We evaluate our approach in a real-world use case based on a funding allocation problem and compare it with the traditional weighted sum aggregation model. The results show that the approaches assign similar ranks, while our approach is more interpretable and sensitive.
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Vinnova, a Swedish government agency under the Ministry of Enterprise and Innovation https://www.vinnova.se/en/.
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ggplot2, https://ggplot2.tidyverse.org/reference/.
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Emcdf, https://github.com/cran/Emcdf.
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rankdist, https://CRAN.R-project.org/package=rankdist.
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Acknowledgments
This research was supported by The Knowledge Foundation within the project “Software technology for self-adaptive systems” (ref. number 20150088). We are also grateful to Jan Sandred at Vinnova, for contributing with a real-world dataset, fruitful discussions, and feedback.
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Ulan, M., Ericsson, M., Löwe, W., Wingkvist, A. (2019). Multi-criteria Ranking Based on Joint Distributions. In: Pańkowska, M., Sandkuhl, K. (eds) Perspectives in Business Informatics Research. BIR 2019. Lecture Notes in Business Information Processing, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-31143-8_6
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