# A differentiable model of supply-chain shocks

arXiv: https://arxiv.org/abs/2511.05231 · HTML: https://mlsanity.com/paper/2511.05231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [sbi reloaded: a toolkit for simulation-based inference workflows](https://mlsanity.com/paper/2411.17337/) (2024)
- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [On learning agent-based models from data](https://mlsanity.com/paper/2205.05052/) (2022)
- [Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks](https://mlsanity.com/paper/2505.21426/) (2025)
- [Automatic Differentiation of Agent-Based Models](https://mlsanity.com/paper/2509.03303/) (2025)

## Abstract (arXiv)

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.