Our policy synthesis work focuses on using AI to generate policy ideas based on specific goals, constraints, and contexts. This approach allows for the exploration of a wider solution space than traditional policy development methods.
Our impact analysis work combines agent-based modeling with causal inference to simulate how policies might affect different stakeholders, industries, and economic indicators over time.
Developing agent-based models to simulate the effects of different AI governance frameworks on innovation, safety, and economic outcomes.
Using causal inference to identify optimal policy mixes for climate change mitigation while minimizing economic disruption.
Simulating the effects of different healthcare policies on access, quality, and cost across diverse populations.