# Calibrating Agent-based Models to Microdata with Graph Neural Networks

*Source* · `src-dyer-2022-abm-microdata-gnn` · status: **n/a**

| field | value |
|---|---|
| kind | Preprint |
| authors | Joel Dyer, Patrick Cannon, J. Doyne Farmer, Sebastian M. Schmon |
| date | 2022-06-15 |
| url | https://arxiv.org/abs/2206.07570 |
| arxiv | 2206.07570 |
| accessed | 2026-10-24 |
| notes | Temporal graph neural networks learn ABM parameter posteriors directly from agent-level microstates instead of hand-chosen summary statistics. |

## Backlinks (computed)

**cited by:** [Simulation-based inference for calibrating simulators](../technique/simulation-based-inference.md)

## Citation

Joel Dyer, Patrick Cannon, J. Doyne Farmer, Sebastian M. Schmon (2022-06-15). *Calibrating Agent-based Models to Microdata with Graph Neural Networks*.  https://arxiv.org/abs/2206.07570

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Provenance: created in run `2026-10-24-techniques-sim-math`.