Bayesian calibration of differentiable agent-based models
This work addresses a specific gap in computational modeling for researchers using differentiable ABMs, offering a method for robust parameter estimation, but it is incremental as it builds on existing variational inference techniques.
The paper tackled the problem of performing Bayesian parameter inference for differentiable agent-based models (ABMs), which lack existing specialized methods, by proposing a generalized variational inference approach that demonstrated accurate inferences in experiments on a COVID-19 pandemic model.
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.