Difference between revisions of "Public Policy and Decision Science"

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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.
 
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.
  
=== '''Carbon Markets''' ===
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=== Carbon Markets ===
 
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.
 
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.
 
== Data ==
 
== Data ==

Revision as of 15:29, 25 August 2020

Policy-making and decision science

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.

Carbon Markets

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.

Data

There are several sources of data at various global, regional, and national levels, all of which are useful for modeling the impact of policies, as well as markets

Methods and Software

Given the importance of representing the impacts of decision-making and market-based strategies, interactive simulation tools and packages for multi-objective optimization are particularly useful in this application. Some of these are listed below:

Recommended Readings

General

Policy Design

Markets and Pricing

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Important considerations

Next steps

References