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Agent-based models (ABMs) are widely used to model coupled natural-humansystems. Descriptive models require careful calibration with observed data.However, ABMs are often not calibrated in a statistical sense. Here we examinethe impact of data record structure on the calibration of an ABM for housingabandonment in the presence of flood risk. Using a perfect model experiment, weexamine the impact of data record structures on (i) model calibration and (ii)the ability to distinguish a model with inter-agent interactions from onewithout. We show how limited data sets may not constrain a model with just fourparameters. This indicates that many ABMs may require informative priordistributions to be descriptive. We also illustrate how spatially-aggregateddata can be insufficient to identify the correct model structure. Thisemphasizes the need for utilizing independent lines of evidence to select soundand informative priors.

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