Public Policy and Decision Science

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Revision as of 13:53, 25 August 2020 by SashaL (talk | contribs) (First draft of this page, some TODOs remain)

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TODO: add context regarding contribution to emissions, connection to ML, and selected readings (summarize)

Decision + Policy

When creating policies, decision-makers must often negotiate fundamental uncertainties in the underlying data. ML can help alleviate some of this uncertainty by extracting information from satellite imagery, sensors, social media posts, policy documents, and other sources (as detailed elsewhere in the paper).

Decision-makers often construct mathematical models to help them assess or trade off between different policy alternatives. ML can help provide new techniques for working with integrated assessment models, multi-objective optimization, and other models commonly used by decision-makers.

When creating new policies, decision-makers may wish to understand previous policies and analyze how these policies performed. ML can help on both fronts by analyzing the text of existing policies and by performing causal inference on historical data.

Markets

Carbon pricing and other market-based measures can incentivize the reduction of greenhouse gas emissions. ML can help predict prices in carbon markets and analyze the main drivers of these prices.

When designing market-based strategies, 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.

Data

TODO: Intro Paragraph

Methods and Software

TODO: Intro par


Recommended Readings

General

Policy Design

Markets and Pricing

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Important considerations

Next steps

References