A differentiable model of supply-chain shocks

Sebastian Rene Towers, José Moran, Saad Hamid, Luca Mungo, Arnau Quera-Bofarull

Keywords: Differentiable Programming, Agent-Based Modeling, Computational Economics, Supply Chain Networks, Shock Propagation, Automatic Differentiation, GPU Acceleration, Model Calibration, Simulation-Based Inference

TL;DR: We construct a differentiable model of supply-chain logistics, and use it to measure the impact of shocks.

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.

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Submission Number: 55