Using Neural Networks for a Universal Framework for Agent-based Models. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Jäger, Georg
• Is Part Of: Mathematical and computer modelling of dynamical systems. Volume 27:Issue 1(2021)
• Page Start: 162
• Page End: 178
• Publication Date: 2021-01-02
• Subjects: Agent-based modelling -- Artificial Neural Networks -- modelling framework; Engineering -- Mathematical models -- Periodicals; Computer simulation -- Periodicals; 515.39
• DOI: 10.1080/13873954.2021.1889609
• Languages: English
• ISSNs: 1387-3954

Abstract

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.