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

Georg Jäger

Mathematical and Computer Modelling of Dynamical Systems, 2021, vol. 27, issue 1, 162-178

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.

Date: 2021
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DOI: 10.1080/13873954.2021.1889609

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