Can we replicate real human behaviour using artificial neural networks?

• Published in: Mathematical and computer modelling of dynamical systems Vol. 28; no. 1; pp. 95 - 109
• Main Authors: Jäger, Georg, Reisinger, Daniel
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Agent-based modelling; Agent-based models; Artificial neural networks; decision making; game theory; Human behavior; human behaviour; Machine learning; Modelling; modelling framework; Simulation; social simulation
• ISSN: 1387-3954; 1744-5051
• DOI: 10.1080/13873954.2022.2039717

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.