  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 these possibilities, 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 and for agent-based models so far and to critically discuss
the combination of these two promising methods. We find that, indeed, there is
a broad range of possible applications of ML to support and complement ABMs in
many different ways, already applied in many different disciplines. We see
that, so far, ML is mainly used in ABM for two broad cases: First, the
modelling of adaptive agents equipped with experience learning and, second, the
analysis of outcomes produced by a given ABM. While these are the most
frequent, there also exist a variety of many more interesting applications.
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, researchers should not
use ML for ABMs just for the sake of doing it.

Theoretical Economics;Multiagent Systems