Public Policy and Decision Science: Difference between revisions
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''This page is about the intersection of policy-making and machine learning. For an overview of policy-making and decision science, please see the [https://en.wikipedia.org/wiki/Policy Wikipedia page] on this topic.''
When creating policies, decision-makers must often negotiate fundamental uncertainties in the underlying data and construct mathematical models to help them assess or trade off between different policy alternatives. ML can help alleviate some of this uncertainty by extracting information from satellite imagery, sensors, social media posts, policy documents, and other source and provide new techniques for working with models commonly used by decision-makers (e.g. integrated assessment models, multi-objective optimization, etc.) ML can also help retroactively, by analyzing the text of existing policies and by performing causal inference on historical data.
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