Bayesian calibration of differentiable agent-based models

TL;DR

This paper introduces a Bayesian inference method tailored for differentiable agent-based models, enabling robust parameter estimation in complex systems like COVID-19 simulations.

Contribution

It proposes using generalized variational inference for differentiable ABMs, addressing a gap in Bayesian inference techniques for such models.

Findings

Accurate Bayesian parameter inferences demonstrated on COVID-19 ABM.

Method shows robustness to model misspecification.

Potential for improved real-world complex system modeling.

Abstract

Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. These difficulties have in turn generated research on approximate Bayesian inference methods for ABMs and on constructing differentiable approximations to arbitrary ABMs, but little work has been directed towards designing approximate Bayesian inference techniques for the specific case of differentiable ABMs. In this work, we aim to address this gap and discuss how generalised variational inference procedures may be employed to provide misspecification-robust Bayesian parameter inferences for differentiable ABMs. We demonstrate with experiments on a differentiable ABM of the COVID-19 pandemic that our approach can result in accurate inferences, and discuss avenues for future work.