A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation

• Published in: Environmental modelling & software : with environment data news Vol. 172; p. 105905
• Main Authors: De Visscher, Lander, De Baets, Bernard, Baetens, Jan M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2024
• Subjects: Agent-based models; Approximate Bayesian Computation; Bayesian theory; Calibration; computer software; Individual-based models; Inference; observational studies; Simulation; Approximate Bayesian Computation; Individual-based models; Inference; Agent-based models; Calibration; Simulation
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2023.105905

The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian Computation (ABC) offers a simulation-based approach for inferring these parameters from observational data. But similar to the flexibility ingrained in agent-based models, the flexible nature of ABC involves several design choices. Here we systematically review how ABC is currently applied in combination with agent-based models, with about half of the reviewed applications being set in an ecological context. We provide a critical discussion of common practices, accompanied by illustrative examples with a benchmark model from the Agents.jl Julia package. This sets out guidelines to aid modellers that are unfamiliar with the subject in their research endeavors. •We review the application of ABC for estimating parameters of agent-based models.•We find that necessary validation methods are applied too infrequently.•Our remarks are illustrated by simulations with a benchmark model in Agents.jl.