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Public Policy and Decision Science: Difference between revisions
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=== Modeling and planning ===
* [[Agent-based modeling]]: ABMs are used in simulating the actions and interactions of agents in their environment, and ML can help integrate data-driven insights into these models, for example by learning rules or models for agents based on observational data.
* Energy system modeling
* [[Power System Planning]]: Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
* [[Integrated assessment models]]: IAMs are large simulations used to explore future societal pathways that are consistent with climate goals, which can be improved with ML.
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