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Abstract

An agent-based model for human behavior in the well-known public goods game (PGG) is developed making
use of bounded rationality, but without invoking mechanisms of learning. The underlying Markov decision
process is driven by a path integral formulation of reward maximization. The parameters of the model can
be related to human preferences accessible to measurement. Fitting simulated game trajectories to available
experimental data, we demonstrate that our agents are capable of modeling human behavior in PGG quite well,
including aspects of cooperation emerging from the game. We find that only two fitting parameters are relevant
to account for the variations in playing behavior observed in 16 cities from all over the world. We thereby find
that learning is not a necessary ingredient to account for empirical data.