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

Lander De Visscher, Bernard De Baets (UGent) and Jan Baetens (UGent)
(2024) ENVIRONMENTAL MODELLING & SOFTWARE. 172.
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Abstract
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.
Keywords
Approximate Bayesian Computation, Inference, Simulation, Calibration, Agent-based models, Individual-based models, INDIVIDUAL-BASED MODELS, CHAIN MONTE-CARLO, POPULATION, CALIBRATION, ABC, DISPERSAL, EVOLUTION, BIOLOGY, WOLF, SIZE

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Citation

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MLA
De Visscher, Lander, et al. “A Critical Review of Common Pitfalls and Guidelines to Effectively Infer Parameters of Agent-Based Models Using Approximate Bayesian Computation.” ENVIRONMENTAL MODELLING & SOFTWARE, vol. 172, 2024, doi:10.1016/j.envsoft.2023.105905.
APA
De Visscher, L., De Baets, B., & Baetens, J. (2024). A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation. ENVIRONMENTAL MODELLING & SOFTWARE, 172. https://doi.org/10.1016/j.envsoft.2023.105905
Chicago author-date
De Visscher, Lander, Bernard De Baets, and Jan Baetens. 2024. “A Critical Review of Common Pitfalls and Guidelines to Effectively Infer Parameters of Agent-Based Models Using Approximate Bayesian Computation.” ENVIRONMENTAL MODELLING & SOFTWARE 172. https://doi.org/10.1016/j.envsoft.2023.105905.
Chicago author-date (all authors)
De Visscher, Lander, Bernard De Baets, and Jan Baetens. 2024. “A Critical Review of Common Pitfalls and Guidelines to Effectively Infer Parameters of Agent-Based Models Using Approximate Bayesian Computation.” ENVIRONMENTAL MODELLING & SOFTWARE 172. doi:10.1016/j.envsoft.2023.105905.
Vancouver
1.
De Visscher L, De Baets B, Baetens J. A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation. ENVIRONMENTAL MODELLING & SOFTWARE. 2024;172.
IEEE
[1]
L. De Visscher, B. De Baets, and J. Baetens, “A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation,” ENVIRONMENTAL MODELLING & SOFTWARE, vol. 172, 2024.
@article{01HKMGBXPGYQP39C9JWRZ3K7WX,
  abstract     = {{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.}},
  articleno    = {{105905}},
  author       = {{De Visscher, Lander and De Baets, Bernard and Baetens, Jan}},
  issn         = {{1364-8152}},
  journal      = {{ENVIRONMENTAL MODELLING & SOFTWARE}},
  keywords     = {{Approximate Bayesian Computation,Inference,Simulation,Calibration,Agent-based models,Individual-based models,INDIVIDUAL-BASED MODELS,CHAIN MONTE-CARLO,POPULATION,CALIBRATION,ABC,DISPERSAL,EVOLUTION,BIOLOGY,WOLF,SIZE}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation}},
  url          = {{http://doi.org/10.1016/j.envsoft.2023.105905}},
  volume       = {{172}},
  year         = {{2024}},
}
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