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Designing Decisions In The Unknown: Towards A Generative Decision Model For Management Science

Author

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  • 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)

  • 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)

  • Mario Le Glatin

    (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)

Abstract

This study examines how design theory enables us to extend decision-making logic to the "unknown," which often appears as the strange territory beyond the rationality of the decision-maker. We contribute to the foundations of management by making the unknown an actionable notion for the decision-maker. To this end, we build on the pioneering works in "managing in the unknown" and on design theory to systematically characterize rational forms of action to structure the exploration of the unknown from a decision-making perspective. We show that action consists of designing decisions in the unknown and can be organized on the basis of the notion of a "decision-driven design path," which is not yet a decision but helps to organize the generation of a better decision-making situation. Our decision-design model allows us to identify four archetypes of decision-driven design paths. Two involve generating "wishful decisions," either by improvement or by genericity, while the other two involve generating "decision-changing states" by generating a "best-choice hacking state" or an "all-decisions hacking state." These archetypes correspond to forms of collective action characterized by a specific strategy of knowledge acquisition, a specific performance, and specific organizations. In particular, they enable us to discuss the variety of known organizational forms that managers can rely on to explore the unknown. Le Masson, P., Hatchuel, A., Le Glatin, M., and Weil, B. (2018). "Designing decisions in the unknown: towards a generative decision model for management science." European Management Review, To be published, pp.

Suggested Citation

  • 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.
  • Handle: RePEc:hal:journl:hal-01937103
    DOI: 10.1111/emre.12289
    Note: View the original document on HAL open archive server: https://minesparis-psl.hal.science/hal-01937103
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    Cited by:

    1. Maria Elmquist & Annabelle Gawer & Pascal Le Masson, 2019. "Innovation Theory and the (Re‐)foundation of Management: Facing the Unknown," Post-Print hal-02291545, HAL.
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    3. Pierrick Bouffaron & Benoit Weil & Pascal Le Masson & Cédric Denis-Rémis, 2019. "Re-inventing corporate innovation through incubation. The VINCI Leonard case study," Post-Print hal-02321451, HAL.
    4. Armand Hatchuel, 2023. "The quest for non-Bayesian decision-making heuristics : towards a logic of imagination," Post-Print hal-04300547, HAL.

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