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assess tipping points in natural systems has profound implications for how humanity responds to climate change, manages land, and prepares for geohazards. This project bridges mathematics, earth science
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plausible land-use changes, with uncertainty explicitly represented to evaluate robustness rather than single-point forecasts. Third, a preference-learning reinforcement learning agent will propose
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