用GPU和自动微分加速供应链模型校准，提升千倍效率。
A differentiable model of supply-chain shocks
- 将供应链代理模型在GPU上运行并引入自动微分
- 校准速度相比传统方法提升超过1000倍
- 适合需要大规模仿真全球供应链的研究者
建模供应链中冲击的传播是经济学中的重要挑战，近年新冠疫情和俄乌冲突凸显了其重要性。基于代理的模型（ABMs）为此提供了有前景的解决方案，但校准难度大。本文实证表明，通过在GPU上运行并使用自动微分，可使供应链网络的ABM校准速度提升超过3个数量级，相比非可微基线方法。这为构建涵盖全球供应链的大型模型打开了可能。
原文摘要 · Abstract (English)
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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