ArticlePloS one2022
Using machine learning as a surrogate model for agent-based simulations.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 115 citations in OpenAlex.
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- Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response.Scientific reports · 2026Article
- Variable deep learning training horizons reveal the temporal complexity of biological systems.microPublication biology · 2026Article
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- Gaussian process emulation for exploring complex infectious disease models.PLoS computational biology · 2025Article
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- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.European journal of clinical investigation · 2025Article
- Bayesian Calibration of Stochastic Agent Based Model via Random Forest.Statistics in medicine · 2025Article
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis.Journal of inflammation research · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study.Biomimetics (Basel, Switzerland) · 2024Article
- Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.Bioinformatics (Oxford, England) · 2024Article
- Perspectives on computational modeling of biological systems and the significance of the SysMod community.Bioinformatics advances · 2024Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Article
- HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models.PloS one · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM surrogate modelling in order to determine the approaches best suited as a surrogate for modelling the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process surrogates, currently the most commonly used method for the surrogate modelling of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for surrogate modelling, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.