Calibrating with multiple criteria: A demonstration of dominance

Abstract

Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-based model is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. The model fit is assessed by the size and timing of maximum behaviour adoption, aswell as the more usual criterion of minimising mean squared error between actual and estimated behaviour. The rigorous approach to calibration supported explicit trading off between these criteria, and ultimately demonstrated that there were significant flaws in the model structure.