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Automatically Generated Weight Methods for Human and Machine Decision-Making

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Advances and Trends in Artificial Intelligence. Theory and Applications (IEA/AIE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13925))

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Abstract

In real-life decision analysis, whether in human-deliberated situations or non-human (real-time semi-intelligent machine/agent) situations, there are well-documented problems regarding the elicitation of probabilities, utilities, and criteria weights. In this paper, we investigate automatic multi-criteria weight-generating methods with a detailed investigation method not seen before. The results confirm that the Sum Rank method for the ordinal case, and the corresponding Cardinal Sum Rank method for the cardinal case, outperform all other methods regarding robustness. New findings include that there is no indication that the difference in the results in the weight generation is diminished as the number of degrees of freedom grows which was previously thought to be true. Further, as expected the cardinal models outperform the ordinal models. More unexpectedly, though, the performance of the dominance intensity-based weight models is at most mediocre for some combinations and not even suitable for other combinations. Another insight from the investigation in this paper is that previous literature is not homogeneous in the modelling of the attribute values, resulting in not all methods considered in this investigation can be directly compared.

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Acknowledgement

This paper is dedicated to the co-author, dear friend, and esteemed colleague Professor Love Ekenberg, who passed away in September 2022 during the research and writing leading up to the paper.

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Authors and Affiliations

  1. Department of Computer and Systems Sciences, Stockholm University, PO Box 7003, SE-164 07, Kista, Sweden

    Sebastian Lakmayer, Mats Danielson & Love Ekenberg

  2. International Institute for Applied Systems Analysis, IIASA, Schlossplatz 1, AT-2361, Laxenburg, Austria

    Mats Danielson & Love Ekenberg

Authors
  1. Sebastian Lakmayer
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  2. Mats Danielson
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  3. Love Ekenberg
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Corresponding author

Correspondence to Mats Danielson .

Editor information

Editors and Affiliations

  1. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Hamido Fujita

  2. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

  3. Fudan University, Shanghai, China

    Yanghua Xiao

  4. Texas State University, San Marcos, TX, USA

    Ali Moonis

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Lakmayer, S., Danielson, M., Ekenberg, L. (2023). Automatically Generated Weight Methods for Human and Machine Decision-Making. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_17

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  • DOI: https://doi.org/10.1007/978-3-031-36819-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36818-9

  • Online ISBN: 978-3-031-36819-6

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