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Publication Date
1 April 2021

Small increases in agent-based model complexity can result in large increases in required calibration data



Agent-based models (ABMs) are widely used to analyze coupled natural and human systems. Descriptive models require careful calibration with observed data. However, ABMs are often not calibrated in a formal sense. Here we examine the impact of data record size and aggregation on the calibration of an ABM for housing abandonment in the presence of flood risk. Using a perfect model experiment, we examine (i) model calibration and (ii) the ability to distinguish a model with inter-agent interactions from one without. We show how limited data sets may not adequately constrain a model with just four parameters and relatively minimal interactions. We also illustrate how limited data can be insufficient to identify the correct model structure. As a result, many ABM-based inferences and projections rely strongly on prior distributions. This emphasizes the need for utilizing independent lines of evidence to select sound and informative priors.
“Small Increases In Agent-Based Model Complexity Can Result In Large Increases In Required Calibration Data”. 2021. Environmental Modelling & Software 138: 104978. doi:10.1016/j.envsoft.2021.104978.
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