Small increases in agent-based model complexity can result in large increases in required calibration data. (April 2021) - Srikrishnan, Vivek; Keller, Klaus
Abstract: 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. Highlights: Adding one parameter can substantially increase the amount of data required to constrain an agent-based model. Small amounts of data can make it difficult to identify the presence of an additional feedback mechanism. Sound and informative priors are important for reliable inference for agent-based models.