Status: laufend
Beschreibung
In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of the opportunities, we conduct a systematic literature review (SLR) and classify the literature on the application of ML in and for ABM according to a theoretically derived classification scheme. We do so to investigate how exactly machine learning has been utilized in agent-based models in different disciplines so far and to identify the most important use cases in the literature. We find that, indeed, there is a broad range of possible applications of ML that might help ABMs to unfold their full potential. Further, we see that, ML is so far mainly used in ABM for the modeling of adaptive agents equipped with experience learning. While these are the most frequent, there is also a variety of many more interesting applications which do not directly meet the eye. This being the case, researchers should dive deeper into the analysis of when and how which kinds of ML techniques can support ABM, e.g. by conducting a more in-depth analysis and comparison of different use cases. Nonetheless, as the application of ML in and for ABM comes at certain costs, researcher should not use ML for ABMs just for the sake of doing it.
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