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Agent-based modelling is a powerful tool when simulating human systems, yetwhen human behaviour cannot be described by simple rules or maximising one'sown profit, we quickly reach the limits of this methodology. Machine learninghas the potential to bridge this gap by providing a link between what peopleobserve and how they act in order to reach their goal. In this paper we use aframework for agent-based modelling that utilizes human values like fairness,conformity and altruism. Using this framework we simulate a public goods gameand compare to experimental results. We can report good agreement betweensimulation and experiment and furthermore find that the presented frameworkoutperforms strict reinforcement learning. Both the framework and the utilityfunction are generic enough that they can be used for arbitrary systems, whichmakes this method a promising candidate for a foundation of a universalagent-based model.

Can We Replicate Real Human Behaviour Using Artificial Neural Networks?