The Exploratory Policy project aims to develop causal AI tools that formulate (synthesize and simulate) and analyze the impacts of policies within complex socio-economic systems. By integrating agent-based modeling, causal inference, and large language models, the project seeks to provide policymakers with interactive platforms to explore potential outcomes, identify unintended consequences, and enhance decision-making processes.
We believe that policy, like science and engineering, must be exploratory in nature—capable of simulating, testing, and adapting to uncertain technological frontiers. By applying structured exploratory methodologies, we seek to bridge the gap between speculative foresight and actionable governance, ensuring that societies can proactively engage with the accelerating pace of scientific and technological change.
Imagine trying to navigate a maze blindfolded. That's kind of what it's like when policymakers try to anticipate how their policies will play out. Traditional methods often fall short, leading to:
In short, we need better tools to understand the complex web of cause and effect in our socio-economic systems.