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. 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.

 

Generally speaking, carbon markets aim to reduce GHG emissions by setting limits on emissions and enabling the trading of emission units, which represent emission reductions. ML can help predict prices in carbon markets and analyze the main drivers of these prices to improve their efficiency. In terms of the design of market-based strategies, such as carbon tax or cap-and-trade programs, it is necessary to understand how effectively each strategy will reduce emissions, as well as how the underlying socio-technical system may be affected. ML can help assess the outcomes of market-based strategies to ensure they are effective and equitable.

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