Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

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

TitleSmall increases in agent-based model complexity can result in large increases in required calibration data
Publication TypeJournal Article
Year of Publication2021
JournalEnvironmental Modelling & Software
Volume138
Pages104978
Abstract / Summary

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.

URLhttp://dx.doi.org/10.1016/j.envsoft.2021.104978
DOI10.1016/j.envsoft.2021.104978
Funding Program: 
Journal: Environmental Modelling & Software
Year of Publication: 2021
Volume: 138
Pages: 104978
Publication Date: 04/2021

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.

DOI: 10.1016/j.envsoft.2021.104978
Citation:
Srikrishnan, V, and K Keller.  2021.  "Small increases in agent-based model complexity can result in large increases in required calibration data."  Environmental Modelling & Software 138: 104978.  https://doi.org/10.1016/j.envsoft.2021.104978.