Public Policy and Decision Science: Difference between revisions

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 (''ex-ante policy analysis''). 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.). When deciding between different policy options, it is necessary to understand how effectively each strategy will reduce emissions, as well as how society may be affected. ML can also help retroactively, by analyzing the text of existing policies and by performing causal inference on historical data (''ex-post policy analysis'').