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Complexity and uncertainty in future food system transformation modelling.

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Current models inadequately represent complex food systems. Rethinking model design and use is crucial for integrating diverse stakeholder needs and enabling transformative change in food systems.

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Area of Science:

  • Food systems science
  • Integrated assessment modeling
  • Sustainability science

Background:

  • Food systems face complex environmental, social, health, and economic pressures.
  • Existing economic equilibrium and integrated assessment models have limitations in guiding food system transformation.
  • Future decision-making necessitates more inclusive and participatory approaches.

Purpose of the Study:

  • To evaluate the capability of current models in representing food systems.
  • To identify challenges and opportunities for transformative food system change.
  • To assess model integration with decision-making processes.

Main Methods:

  • Analysis of current modeling approaches for food systems.
  • Evaluation of model representation of key transformative aspects: socio-political dynamics, human-nature feedbacks, scale linkages, uncertainty, and stakeholder demands.
  • Assessment of model utility in decision-making.

Main Results:

  • Current models have limitations in capturing the multi-dimensional nature of food systems.
  • Challenges exist in representing socio-political dynamics, human-nature feedbacks, and scale interactions.
  • Models struggle with robustness under uncertainty and evolving stakeholder needs.
  • Effective integration into decision-making processes is hindered by current model design and application.

Conclusions:

  • A fundamental rethinking of model design and application is required.
  • Enhanced participatory processes are essential for effective food system transformation.
  • Models need to better incorporate socio-political factors and human-nature feedbacks.
  • Improved model integration is vital for informed and inclusive food system decision-making.