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.

翻译：基于主体建模（ABM）是建模复杂系统的一种强大且直观的方法；然而，ABM似然函数的难解性及其构成模型数学运算的非可微性，给其在现实世界中的应用带来了挑战。这些困难进而催生了针对ABM的近似贝叶斯推断方法研究，以及构建任意ABM可微分近似的研究，但针对可微分ABM这一特定案例设计近似贝叶斯推断技术的工作尚属鲜见。本研究旨在填补这一空白，探讨如何利用广义变分推断程序为可微分ABM提供对模型误设定具有鲁棒性的贝叶斯参数推断。我们通过一项关于COVID-19大流行的可微分ABM实验证明，我们的方法能够得出准确的推断结果，并讨论了未来工作的方向。