  Agent-based modelling is a powerful tool when simulating human systems, yet
when human behaviour cannot be described by simple rules or maximising 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.
