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The Learning Model for Data-Driven Decision Making of Collaborating Enterprises

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Human and Artificial Rationalities (HAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14522))

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Abstract

Determining the principles for building an intelligent management system aimed at making optimal decisions becomes an urgent need to achieve sustainable development of cooperating enterprises and requires the search for appropriate management models based on data dynamics. The purpose of the work was to indicate the determinants of sustainable development of collaborating enterprises used to determine the management model. The application of systems analysis and multi-agent simulation principles to human-system interactions has indicated a way to construct the data-driven decision making reinforcement learning model. The results obtained indicate that such management eliminates subjectivity in decision-making, leading to the stabilization of the economic development of cooperating enterprises, and hence to sustainable development.

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Acknowledgments

We thank two anonymous reviewers for their very useful and relevant comments.

Author information

Authors and Affiliations

  1. LAGA, Université Sorbonne Paris Nord, 93430, Villetaneuse, France

    Charles El-Nouty

  2. RDS, Nancy, France

    Darya Filatova

Authors
  1. Charles El-Nouty
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  2. Darya Filatova
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Corresponding author

Correspondence to Charles El-Nouty .

Editor information

Editors and Affiliations

  1. Université Paris 8, Saint Denis, France

    Jean Baratgin

  2. Université Paris 8, Saint-Denis, France

    Baptiste Jacquet

  3. Osaka City University, Osaka, Japan

    Hiroshi Yama

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El-Nouty, C., Filatova, D. (2024). The Learning Model for Data-Driven Decision Making of Collaborating Enterprises. In: Baratgin, J., Jacquet, B., Yama, H. (eds) Human and Artificial Rationalities. HAR 2023. Lecture Notes in Computer Science, vol 14522. Springer, Cham. https://doi.org/10.1007/978-3-031-55245-8_22

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  • DOI: https://doi.org/10.1007/978-3-031-55245-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-55244-1

  • Online ISBN: 978-3-031-55245-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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