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Using Neural Networks for a Universal Framework for Agent-based Models

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  • Georg Jäger

Abstract

Traditional agent-based modelling is mostly rule-based. For many systems, this approach is extremely successful, since the rules are well understood. However, for a large class of systems it is difficult to find rules that adequately describe the behaviour of the agents. A simple example would be two agents playing chess: Here, it is impossible to find simple rules. To solve this problem, we introduce a framework for agent-based modelling that incorporates machine learning. In a process closely related to reinforcement learning, the agents learn rules. As a trade-off, a utility function needs to be defined, which is much simpler in most cases. We test this framework to replicate the results of the prominent Sugarscape model as a proof of principle. Furthermore, we investigate a more complicated version of the Sugarscape model, that exceeds the scope of the original framework. By expanding the framework we also find satisfying results there.

Suggested Citation

  • Georg Jäger, 2021. "Using Neural Networks for a Universal Framework for Agent-based Models," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 27(1), pages 162-178, January.
  • Handle: RePEc:taf:nmcmxx:v:27:y:2021:i:1:p:162-178
    DOI: 10.1080/13873954.2021.1889609
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