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Beyond Models and Decisions: Situating Design through generative functions

Author

Listed:
  • Armand Hatchuel

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Yoram Reich

    (TAU - Tel Aviv University)

  • Pascal Le Masson

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Benoit Weil

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Akin Kazakçi

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper aims to situate Design by comparison to scientific modeling and optimal Decision. We introduce " generative functions " characterizing each of these activities. We formulate inputs, outputs and specific conditions of the generative functions corresponding to modeling (G m), Optimization (G o) and Design (G d): G d follows the classic view of modeling as a reduction of observed anomalies in knowledge by assuming the existence of unknown objects that may be observed and described with consistency and completeness. G o is possible when free parameters appear in models. Gd bears on recent Design theory, which shows that design begins with unknown yet not observable objects to which desired properties are assigned and have to be achieved by design. On this basis we establish that: i) modeling is a special case of Design; ii) the definition of design can be extended to the simultaneous generation of objects (as artifacts) and knowledge. Hence, the unity and variety of design can be explained, and we establish Design as a highly general generative function that is central to both science and decision. Such findings have several implications for research and education.

Suggested Citation

  • Armand Hatchuel & Yoram Reich & Pascal Le Masson & Benoit Weil & Akin Kazakçi, 2013. "Beyond Models and Decisions: Situating Design through generative functions," Post-Print hal-01485144, HAL.
    • Handle: RePEc:hal:journl:hal-01485144
    Note: View the original document on HAL open archive server: https://minesparis-psl.hal.science/hal-01485144v1
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    Citations

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    Cited by:

    1. Mario Le Glatin & Pascal Le Masson & Benoit Weil, 2017. "Decision design and re-ordering preferences: the case of an exploration project in a large firm," Post-Print hal-01529620, HAL.
    2. Mario Le Glatin & Pascal Le Masson & Benoit Weil, 2016. "Measuring the generative power of an organisational routine with design theories: the case of design thinking in a large firm," Post-Print hal-01367471, HAL.
    3. Quentin Plantec & Pascal Le Masson & Benoit Weil, 2020. "Impact of knowledge search practices on the originality of inventions: a study in the oil & gas industry," Post-Print hal-02613665, HAL.
    4. Salembier, Chloé & Segrestin, Blanche & Berthet, Elsa & Weil, Benoît & Meynard, Jean-Marc, 2018. "Genealogy of design reasoning in agronomy: Lessons for supporting the design of agricultural systems," Agricultural Systems, Elsevier, vol. 164(C), pages 277-290.
    5. Plantec, Quentin & Le Masson, Pascal & Weil, Benoît, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    6. Lacombe, Camille & Couix, Nathalie & Hazard, Laurent, 2018. "Designing agroecological farming systems with farmers: A review," Agricultural Systems, Elsevier, vol. 165(C), pages 208-220.
    7. Jean-Alain Héraud & Nathalie Popiolek, 2022. "Research management models to promote breakthrough innovation: analyzing success case stories of simultaneous discovery-invention research processes," Working Papers of BETA 2022-12, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    8. Pascal Le Masson & Armand Hatchuel & Mario Le Glatin & Benoit Weil, 2018. "Designing Decisions In The Unknown: Towards A Generative Decision Model For Management Science," Post-Print hal-01937103, HAL.
    9. Thomas Gillier & Sylvain Lenfle, 2015. "Experimenting in the unknown : lessons from the Manhattan project," Working Papers hal-01483018, HAL.
    10. Caroline Jobin & Sophie Hooge & Pascal Le Masson, 2021. "The logics of double proof in proof of concept: a design theory-based model of experimentation in the unknown," Post-Print hal-03178574, HAL.
    11. Thomas Gillier & Sylvain Lenfle, 2015. "Experimenting in the unknown : lessons from the Manhattan project," Working paper serie RMT - Grenoble Ecole de Management hal-01483018, HAL.
    12. Quentin Plantec & Pascal Le Masson & Benoit Weil, 2019. "The role of participating in user-driven research projects on scholar's academic performances: a model through C-K design theory," Post-Print hal-02165721, HAL.

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