Small increases in agent-based model complexity can result in large increases in required calibration data. (April 2021)

• Record Type: Journal Article
• Authors: Srikrishnan, Vivek; Keller, Klaus
• Is Part Of: Environmental modelling & software. Volume 138(2021)
• Publication Date: 2021-04
• Subjects: Agent-based modeling -- Statistical calibration -- Model selection; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.104978
• Languages: English
• ISSNs: 1364-8152

Abstract

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.