Embracing uncertainty: foundations of a learning system for food systems transformation

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Kai Mausch, Andy Hall, Caroline Hambloch, Costanza Conti, Michael Hauser, Salina Abraham, Philippa Hammond, Enayat A. Moallemi 


We propose a transformative learning system based on a review of uncertainty emerging from system complexity. The framework is built on locally led action and embedded in a learning system that aiming at transforming the food systems. It is widely agreed that food systems need transformative change to meet societal goals. However, despite this agreement, the implementation of a systems transformation agenda appears to have stalled. We argue that the reason for this failure can be attributed to the complexity of the task and the inherent uncertainty. Based on a review of uncertainty and complexity in change processes, we outline a transformative learning system that has the capacity to achieve the intended transformation. This system requires shifts in roles and modes of operation to facilitate change and to learn about system responses to localized disruptive change. Focusing on the core functions of the new system, we discuss who and how this change can be triggered and how this, in turn, will change the operational modalities of people, the process of change, and the structures and institutions involved in the process. We argue that the foundations of uncertainty and the focus on learning inherent in the new system will facilitate a more agile process. This will allow actors to learn from decentrally pursued food systems reforms and thereby the organic emergence of heterogeneous pathways.




Social and Behavioral Sciences


Complex Systems, sustainability, Equity, localization


Published: 2024-06-21 01:03

Last Updated: 2024-06-21 05:03


CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

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