A differentiable model of supply-chain shocks
arXiv:2511.05231
Abstract
Modelling how shocks propagate in supply chains is an increasingly important challenge in economics. Its relevance has been highlighted in recent years by events such as Covid-19 and the Russian invasion of Ukraine. Agent-based models (ABMs) are a promising approach for this problem. However, calibrating them is hard. We show empirically that it is possible to achieve speed ups of over 3 orders of magnitude when calibrating ABMs of supply networks by running them on GPUs and using automatic differentiation, compared to non-differentiable baselines. This opens the door to scaling ABMs to model the whole global supply network.
Accepted to 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: Differentiable Systems and Scientific Machine Learning (EurIPS)