Public Policy and Decision Science: Difference between revisions
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=== Modeling and planning === |
=== Modeling and planning === |
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* [[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. |
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* [[Agent-based modeling]] |
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* Energy system modeling |
* Energy system modeling |
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* [[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. |
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* [[Power System Planning]] |
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* [[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. |
* [[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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