{"ID":2844929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05231","arxiv_id":"2511.05231","title":"A differentiable model of supply-chain 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.","short_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....","url_abs":"https://arxiv.org/abs/2511.05231","url_pdf":"https://arxiv.org/pdf/2511.05231v1","authors":"[\"Saad Hamid\",\"José Moran\",\"Luca Mungo\",\"Arnau Quera-Bofarull\",\"Sebastian Towers\"]","published":"2025-11-07T13:27:52Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cs.LG\",\"cs.MA\"]","methods":"[]","has_code":false}