Policy Simulation Library meetup — hosted by AEI’s Open Source Policy Center – AEI – American Enterprise Institute: Freedom, Opportunity, Enterprise23rd November 2018
Join us for the DC Policy Simulation Library meetup hosted by AEI’s Open Source Policy Center. We’ll have presentations from model developers and users. Learn how computational simulation models are used to inform public policy decision-making.
For our first event, two models will be demoed: the Policy Change Index (PCI) for China and the Paid Family Leave–Cost Model (PFL-CM).
The PCI forecasts significant public policy deviations from the status quo. The PCI takes the full text of the People’s Daily — the official newspaper of the Communist Party of China — and uses machine learning techniques to detect changes in the way the newspaper prioritizes policy issues. By detecting relative changes in propaganda, the PCI can predict future changes in policy. PCI is programmed in Python using TensorFlow.
The Paid Family Leave–Cost Model is used to evaluate the total cost of paid family and medical leave policy proposals by estimating the take-up, leave duration, and other information and then applying that to the 2017 Current Population Survey to estimate the total cost of the policy. PFL-CM is programmed in Stata.
Everyone is welcome regardless of technical knowledge or experience, although please expect modeling code to be presented. To learn more about the Policy Simulation Library, visit its website.
Join the conversation on social media with @AEI on Twitter and Facebook.
If you are unable to attend, we welcome you to watch the event live on this page. Full video will be posted within 24 hours.