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Towards a Taxonomy of Multi-Agent Simulation Models to Determine Disruptive Technology Adoption Behaviour in the Wine Industry

In: Agribusiness Innovation and Contextual Evolution, Volume II

Author

Listed:
  • Michael Paul Kramer

    (Hochschule Geisenheim University)

  • Joe Viana

    (BI Norwegian Business School)

  • Rolf A. E. Mueller

    (Christian-Albrechts-Universität zu Kiel)

  • Claus-Hennig Hanf

    (Christian-Albrechts-Universität zu Kiel)

  • Jon H. Hanf

    (Hochschule Geisenheim University)

Abstract

Multi-agent modelling and simulation (MAMS) is an emerging field in agricultural economics. The models consist of multiple interacting agents representing real-world entities including stakeholders or organizations. They provide better understanding of the behaviour and decision-making processes in complex adaptive systems such as agri-food supply networks. We provide a taxonomy of multi-agent models in the agri-food sector based on their attributes, including agent types, degree of interaction, feedback, and learning. The article provides insights in their use in the wine supply network. We conducted a literature review and found a lack of research in the realms of stakeholder collaboration, technology adoption, and supply network efficiency. The results suggest that MAMS is used primarily in land use exploration and decision making for potential policy changes.

Suggested Citation

  • Michael Paul Kramer & Joe Viana & Rolf A. E. Mueller & Claus-Hennig Hanf & Jon H. Hanf, 2024. "Towards a Taxonomy of Multi-Agent Simulation Models to Determine Disruptive Technology Adoption Behaviour in the Wine Industry," Palgrave Intersections of Business and the Sciences, in association with Gnosis Mediterranean Institute for Management Science, in: Antonino Galati & Demetris Vrontis & Alkis Thrassou & Mariantonietta Fiore (ed.), Agribusiness Innovation and Contextual Evolution, Volume II, chapter 5, pages 103-130, Palgrave Macmillan.
    • Handle: RePEc:pal:pinchp:978-3-031-45742-5_5
    DOI: 10.1007/978-3-031-45742-5_5
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