Calibrating Agent-based Models to Microdata with Graph Neural Networks
J. Farmer,
Joel Dyer,
Patrick Cannon and
Sebastian Schmon
INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford
Abstract:
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 tension between the desire to make full use of the rich information content of such granular data on the one hand, and the need to reduce the dimensionality of the data to prevent difficulties associated with high-dimensional learning tasks on the other. A possible resolution is to construct lower-dimensional time-series through the use of summary statistics describing the macrostate of the system at each time point. However, a poor choice of summary statistics can result in an unacceptable loss of information from the original dataset, dramatically reducing the quality of the resulting calibration. In this work, we instead propose to learn parameter posteriors associated with granular microdata directly using temporal graph neural networks. We will demonstrate that such an approach offers highly compelling inductive biases for Bayesian inference using the raw ABM microstates as output.
Pages: 8 pages
Date: 2022-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-hme
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.inet.ox.ac.uk/files/10.48550_arxiv.2206.07570.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:amz:wpaper:2022-30
Access Statistics for this paper
More papers in INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by INET Oxford admin team (info@inet.ox.ac.uk).