Public Policy and Decision Science

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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 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 (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).

Machine Learning Application AreasEdit

Gathering decision-relevant dataEdit

  • Data management, cleaning and imputation: ML can help with cleaning, merging and completing datasets that are relevant for policy-making.
  • Remote sensing: Satellite data can provide a lot of valuable information to policy makers, for example by helping map infrastructure, land use and ecosystem health. The field of remote sensing has seen large improvements with ML.
  • Computational text analysis: Text documents are an important source of information to inform climate policy. Natural language processing can help to analyze large corpora of text.

Decision scienceEdit

  • Decision science
  • Multi-criteria decision-making: Multi-criteria decision-making can also help policy-makers manage trade-offs between different policies. Computational approaches and machine learning can help with finding solutions to these optimization problems.

Modeling and planningEdit

  • Agent-based modeling: ABMs are used in simulating the actions and interactions of agents in their environment, and ML can help integrate data-driven insights into these models, for example by learning rules or models for agents based on observational data.
  • Energy system modeling
  • Urban planning: With new techniques such as surrogate modeling, ML can help with complex planning tasks.
  • Power System Planning: Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
  • Integrated assessment models: IAMs are large simulations used to explore future societal pathways that are consistent with climate goals, which can be improved with ML.

Ex-post policy analysisEdit

Background ReadingsEdit


  • Resources for Effective Climate Decisions. (Ch. 4) Informing an Effective Response to Climate Change (2010)[1]: A chapter from a report published after a series of five coordinated activities convened by the National Research Council in response to a request from Congress. Available here.
  • Social, Economic, and Ethical Concepts and Methods. (Ch. 3). (2014)[2] : a report by the Intergovernmental Panel on Climate Change (IPCC) regarding the social and economic aspects of climate change. Available here.
  • Climate Change Policies (2016)[3]: A report by the European Environmental Agency on defining successful, impactful policies for climate change. Available here.

Online Courses and Course MaterialsEdit

Educational resources for policy analysisEdit

  • Theory and Practice in Policy Analysis: Including Applications in Science and Technology (Morgan, 2017)[4]: A rich resource for teaching classes on policy analysis with a focus on science and technology. Available here.
  • Policy Analysis: Concepts and Practice (Weimer & Vining, 2017)[5]: Essential primer on policy analysis. Available here.

Conferences, Journals, and Professional OrganizationsEdit

Major conferencesEdit

Major societies and organizationsEdit

Libraries and ToolsEdit

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:


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

Climate policy databasesEdit

Climate change impacts and adaptationEdit

  • Vulnerability to Climate Change Dataset: quantifies the vulnerability of 233 countries to three major effects of climate change (weather-related disasters, sea-level rise, and reduced agricultural productivity).

Carbon price dataEdit

Data by or relevant to international organizationsEdit


  1. Council, National Research (2010-07-21). Informing an Effective Response to Climate Change. ISBN 978-0-309-14594-7.
  2. Social, Economic, and Ethical Concepts and Methods, Cambridge University Press
  3. Del Río, Pablo. "Climate Change Policies and New Technologies". Climate Change Policies. doi:10.4337/9781781000885.00016.
  4. Morgan, Granger (2017). Theory and Practice in Policy Analysis: Including Applications in Science and Technology. Cambridge: Cambridge University Press. doi:10.1017/9781316882665. ISBN 978-1-316-88266-5.
  5. Weimer DL, Vining AR. Policy analysis: Concepts and practice. Taylor & Francis; 2017 Mar 31.