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

被引:1
作者
De Visscher, Lander [1 ,2 ]
De Baets, Bernard [1 ]
Baetens, Jan M. [2 ]
机构
[1] Univ Ghent, Dept Data Anal & Math Modelling, KERMIT, Coupure Links 653, B-9000 Ghent, Belgium
[2] Univ Ghent, Dept Data Anal & Math Modelling, BionamiX, Coupure Links 653, B-9000 Ghent, Belgium
关键词
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;
D O I
10.1016/j.envsoft.2023.105905
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页数:17
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