2018
DOI: 10.1016/j.jedc.2018.03.011
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Agent-based model calibration using machine learning surrogates

Abstract: Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to g… Show more

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Cited by 182 publications
(126 citation statements)
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References 102 publications
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“…Notice that alternative approaches for large scale models are under development. SeeBarde (2016);Lamperti (2017Lamperti ( , 2016;Lamperti et al (2017);Guerini and Moneta (2016).43 These feedbacks have been calibrated according toSterman et al (2013) and C-ROADS model documentation. See https: //www.climateinteractive.org/tools/c-roads/technical.…”
mentioning
confidence: 99%
“…Notice that alternative approaches for large scale models are under development. SeeBarde (2016);Lamperti (2017Lamperti ( , 2016;Lamperti et al (2017);Guerini and Moneta (2016).43 These feedbacks have been calibrated according toSterman et al (2013) and C-ROADS model documentation. See https: //www.climateinteractive.org/tools/c-roads/technical.…”
mentioning
confidence: 99%
“…No matter the empirical validation procedures actually employed, an important domain of analysis regards the sensitivity of the model to parameter changes, through different methods including Kriging meta-modeling (Salle and Yıldızoglu, 2014;Bargigli et al, 2016;Dosi et al, 2017dDosi et al, ,c, 2018c, and machine-learning surrogates (Lamperti et al, 2018c). Such methodologies provide detailed sensitivity analyses of macro ABMs, allowing one to get a quite deep descriptive knowledge of the behavior of the system.…”
Section: Macroeconomic Agent-based Modelsmentioning
confidence: 99%
“…Advances in the area have been so intertwining that new models have started to feed from different trends of spatial-modeling literature bridging transportation and landuse models to macroeconomics [82] and actual life-cycle of individuals in order to generate individual demand models [83]. The bridge has also been generous when crossing automated computing techniques and traditional models [84] or when aiding its validation [85].…”
Section: Agent-based Modeling and Cellularmentioning
confidence: 99%