In recent years, many scholars have praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models . We find that, indeed, there is a broad range of possible applications of ML to support and complement ABMs in many different ways . We seethat, so far, ML is mainly used in ABM for two broad cases: First, themodelling of adaptive agents equipped with experience learning and, second, theanalysis of outcomes produced by a given ABM . While these are the most frequent, there also exist a variety of many more interesting applications . Researchers should dive deeper into the analysis of whenand how which kinds of ML techniques can support ABM, e.g. by conducting a morein-depth analysis and comparison of different use cases . Nonetheless, as theapplication of ML comes at certain costs, researchers should not use ML for ABMs just for the sake of doing it . Back to Mail Online Homepage Live! Back to the page you came from:

Author(s) : Johannes Dahlke, Kristina Bogner, Matthias Mueller, Thomas Berger, Andreas Pyka, Bernd Ebersberger

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Keywords : ml - abm - learning - abms - researchers -

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