Chopra, Quera-bofarull and Zhang. "Differentiable Agent-based Modeling: Systems, Methods and Applications". Tutorial at 23rd Autonomous Agents and Multi-agent Systems (AAMAS) 2024.

Abstract

In this tutorial, we will introduce a new paradigm for agent-based models (ABMs), where we leverage automatic differentiation to obtain the simulator’s gradients in a fast and accurate way. We will review automatic differentiation, formally define differentiable agent-based modeling and showcase examples of differentiable ABMs with millions of agents used to study disease spread in multiple countries. We will discuss state-of-the-art methods for simulation, calibration and analysis of differentiable ABMs and present technical advances in modeling and multi-agent learning that enabled them. We will walk through open-source platforms to build differentiable ABMs and attendees will apply these methods on a few comprehensive examples. Finally, we will deep-dive into a real-world implementation of differentiable ABMs for country-scale disease surveillance, in collaboration with the New Zealand crown research institute. The tutorial is of relevance to the AAMAS community as well as local modelers and policy makers in New Zealand. The corresponding presenter is Ayush Chopra [ayushc@mit.edu]