IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v65y2023i2d10.1007_s12599-023-00790-2.html
   My bibliography  Save this article

AI-Enhanced Hybrid Decision Management

Author

Listed:
  • Dominik Bork

    (TU Wien)

  • Syed Juned Ali

    (TU Wien)

  • Georgi Milenov Dinev

    (TU Wien)

Abstract

The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.

Suggested Citation

  • Dominik Bork & Syed Juned Ali & Georgi Milenov Dinev, 2023. "AI-Enhanced Hybrid Decision Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 179-199, April.
    • Handle: RePEc:spr:binfse:v:65:y:2023:i:2:d:10.1007_s12599-023-00790-2
    DOI: 10.1007/s12599-023-00790-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-023-00790-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12599-023-00790-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kurt Sandkuhl & Hans-Georg Fill & Stijn Hoppenbrouwers & John Krogstie & Florian Matthes & Andreas Opdahl & Gerhard Schwabe & Ömer Uludag & Robert Winter, 2018. "From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(1), pages 69-80, February.
    2. Douglas Heaven, 2019. "Why deep-learning AIs are so easy to fool," Nature, Nature, vol. 574(7777), pages 163-166, October.
    3. Ning Xu & Jiangping Wang & Guojun Qi & Thomas Huang & Weiyao Lin, 2015. "Ontological Random Forests for Image Classification," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 5(3), pages 61-74, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Coleman, Andrew, 2018. "Forest-based carbon sequestration, and the role of forward, futures, and carbon-lending markets: A comparative institutions approach," Journal of Forest Economics, Elsevier, vol. 33(C), pages 95-104.
    2. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    3. Mingli Chen & Andreas Joseph & Michael Kumhof & Xinlei Pan & Xuan Zhou, 2021. "Deep Reinforcement Learning in a Monetary Model," Papers 2104.09368, arXiv.org, revised Jan 2023.
    4. Jin Xiao & Yuhang Tian & Yanlin Jia & Xiaoyi Jiang & Lean Yu & Shouyang Wang, 2023. "Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 986-1001, September.
    5. Sotiris P. Gayialis & Evripidis P. Kechagias & Georgios A. Papadopoulos & Nikolaos A. Panayiotou, 2022. "A Business Process Reference Model for the Development of a Wine Traceability System," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    6. Kristina Rosenthal & Benjamin Ternes & Stefan Strecker, 2021. "Business Process Simulation on Procedural Graphical Process Models," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(5), pages 569-602, October.
    7. Svyatoslav Kotusev & Sherah Kurnia & Rod Dilnutt & Rogier van de Wetering, 2024. "The Structuring of Enterprise Architecture Functions in Organizations," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 465-488, August.
    8. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:binfse:v:65:y:2023:i:2:d:10.1007_s12599-023-00790-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.