home Anil Madhavapeddy, Professor of Planetary Computing  

Reverse emulating agent-based models for policy simulation

This is an idea proposed in 2023 as a Cambridge Computer Science Part III or MPhil project, and has been completed by Pedro Sousa. It was co-supervised with Sadiq Jaffer.

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.

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This project was a followup to one in the previous year by Sharan Agrawal on Scalable agent-based models for optimized policy design.

# 1st Jan 2023   iconideas abm ai climate idea-done idea-hard policy

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