Disaster response agencies have been shifting from a paradigm of climate
forecasting towards one of anticipatory action: assessing not just what the
climate will be, but how it will impact specific populations, thereby enabling
proactive response and resource allocation. Machine learning models are
becoming exceptionally powerful at climate forecasting, but methodological gaps
remain in terms of facilitating anticipatory action. Here we provide an
overview of anticipatory action, review relevant applications of machine
learning, identify common challenges, and highlight areas where machine
learning can uniquely contribute to advancing disaster response for populations
most vulnerable to climate change.

灾难应对机构已从气候预测范式向预测行动范式转变，评估的不仅是气候情况，更是其对特定人群的影响，从而实现主动响应和资源分配。机器学习模型在气候预测方面变得异常强大，但在促进预测行动方面仍存在方法论上的差距。在本研究中，我们概述了预测行动，回顾了机器学习的相关应用，确定了共同的挑战，并强调了机器学习在推进对气候变化最脆弱人群的灾难应对方面的独特贡献领域。

预测行动的人工智能：超越气候预报

AI for Anticipatory Action: Moving Beyond Climate Forecasting

Reasoning does not work well when done in isolation from its significance,
both to the needs and interests of an agent and with respect to the wider
world. Moreover, those issues may best be handled with a new sort of data
structure that goes beyond the knowledge base and incorporates aspects of
perceptual knowledge and even more, in which a kind of anticipatory action may
be key.

从代理的需求和利益以及广泛的世界上的重要性来看，孤立无援地进行推理效果不佳。此外，这些问题可能最好通过一种新的数据结构来处理，该数据结构超越了知识库，包含了感知知识的方面，甚至可能是一种预期的行动所关键。