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

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.