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...

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Vydáno v:Environmental modelling & software : with environment data news Ročník 172; s. 105905
Hlavní autoři: De Visscher, Lander, De Baets, Bernard, Baetens, Jan M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.01.2024
Témata:
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
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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. •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.
AbstractList 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.
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.
ArticleNumber 105905
Author De Visscher, Lander
De Baets, Bernard
Baetens, Jan M.
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  givenname: Lander
  orcidid: 0000-0003-3918-8648
  surname: De Visscher
  fullname: De Visscher, Lander
  organization: KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000, Ghent, Belgium
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  givenname: Bernard
  orcidid: 0000-0002-3876-620X
  surname: De Baets
  fullname: De Baets, Bernard
  organization: KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000, Ghent, Belgium
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  givenname: Jan M.
  orcidid: 0000-0003-4084-9992
  surname: Baetens
  fullname: Baetens, Jan M.
  email: jan.baetens@ugent.be
  organization: BIONAMIX, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000, Ghent, Belgium
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Individual-based models
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Agent-based models
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Snippet The agent-based modelling paradigm often results in complex, highly detailed models, containing unknown or uncertain parameters. Approximate Bayesian...
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SubjectTerms Agent-based models
Approximate Bayesian Computation
Bayesian theory
Calibration
computer software
Individual-based models
Inference
observational studies
Simulation
Title A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation
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