A differentiable model of supply-chain shocks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909892711809024 |
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| author | Hamid, Saad Moran, José Mungo, Luca Quera-Bofarull, Arnau Towers, Sebastian |
| author_facet | Hamid, Saad Moran, José Mungo, Luca Quera-Bofarull, Arnau Towers, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A differentiable model of supply-chain shocks Hamid, Saad Moran, José Mungo, Luca Quera-Bofarull, Arnau Towers, Sebastian Physics and Society Machine Learning Multiagent Systems 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. |
| title | A differentiable model of supply-chain shocks |
| topic | Physics and Society Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2511.05231 |