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Can (and should) Automated Surrogate Modelling be used for Simulation Assistance?

Veronika Kurchyna; Jan Ole Berndt; Ingo Timm
In: Multi-Agent-Based Simulation XXIV,. International Workshop on Multi-Agent Systems and Agent-Based Simulation (MABS-2023), located at AAMAS, May 6, London, LNCS, Vol. 14558, ISBN Lecture Notes in Computer Scienc, Springer, 2024.


Recent advances in machine learning may be leveraged by researchers in the context of agent-based modelling. With the help of surrogate models, machine learned models based on samples of a more complex agent-based model, computationally expensive evaluation methods such as sensitivity analysis and calibration may be supported and sped up. To explore the outlook on using surrogate modelling to assist simulation, possible criteria for eligibility are defined. With regards to a use case such as simulation-based crisis management and decision support, existing literature in different fields is reviewed to assess the current state of the art and potentials for holistic approaches to surrogate modelling-based simulation assistance. This work acknowledges the potentials of surrogate modelling in combination with automated machine learning, but finds no evidence that the current state of the art allows for an accessible, wide-spread usage.