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

model calibration
uncertainty quantification
agent-based modeling
Authors

Vivek Srikrishnan

Klaus Keller

Published

2021

Doi

10.1016/j.envsoft.2021.104978

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.

Citation

@ARTICLE{SrikrishnanKeller2021,
  title    = "Small increases in agent-based model complexity can result in
              large increases in required calibration data",
  author   = "Srikrishnan, Vivek and Keller, Klaus",
  journal  = {Environmental Modelling \& Software},
  volume   =  138,
  pages    = "104978",
  year     =  2021,
  doi      = "10.1016/j.envsoft.2021.104978",
}