This is an idea proposed in 2023 as a Cambrige Computer Science Part III or MPhil project, and has been completed by Pedro Sousa. It was supervised by Anil Madhavapeddy and Sadiq Jaffer as part of my Remote Sensing of Nature project.
Governments increasingly rely on simulation tools to inform policy design. Agent-based models (ABMs) simulate complex systems to study the emergent phenomena of individual behaviours and interactions in agent populations. However, these ABMs force an iterative, time-consuming, unmethodical parameter tuning of key policy "levers" (or input parameters) to steer the model towards the envisioned outcomes. To unlock a more natural workflow, this project investigates reverse emulation, a novel approach that streamlines policy design using probabilistic machine learning to predict parameter values that yield the desired policy outcomes.
This project was a followup to one in the previous year by Sharan Agrawal on Scalable agent-based models for optimized policy design.