Calibrating Agent-based Models to Microdata with Graph Neural Networks. (arXiv:2206.07570v1 [cs.MA])

Web: http://arxiv.org/abs/2206.07570

Calibrating agent-based models (ABMs) to data is among the most fundamental
requirements to ensure the model fulfils its desired purpose. In recent years,
simulation-based inference methods have emerged as powerful tools for
performing this task when the model likelihood function is intractable, as is
often the case for ABMs. In some real-world use cases of ABMs, both the
observed data and the ABM output consist of the agents' states and their
interactions over time. In such cases, there is a …

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