 Can we replicate real human behaviour using artificial neural networks?Georg Jäger and Daniel Reisinger Mathematical and Computer Modelling of Dynamical Systems, 2022, vol. 28, issue 1, 95-109 Abstract: Agent-based modelling is a powerful tool when simulating human systems, yet when human behaviour cannot be described by simple rules or maximizing one’s own profit, we quickly reach the limits of this methodology. Machine learning has the potential to bridge this gap by providing a link between what people observe and how they act in order to reach their goal. In this paper we use a framework for agent-based modelling that utilizes human values like fairness, conformity and altruism. Using this framework we simulate a public goods game and compare to experimental results. We can report good agreement between simulation and experiment and furthermore find that the presented framework outperforms strict reinforcement learning. Both the framework and the utility function are generic enough that they can be used for arbitrary systems, which makes this method a promising candidate for a foundation of a universal agent-based model. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:nmcmxx:v:28:y:2022:i:1:p:95-109 Ordering information: This journal article can be ordered from DOI: 10.1080/13873954.2022.2039717 Access Statistics for this article Mathematical and Computer Modelling of Dynamical Systems is currently edited by I. Troch More articles in Mathematical and Computer Modelling of Dynamical Systems from Taylor & Francis Journals |
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