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.
Highlights
The arguably most important question of our time is how humankind can devise a sustainable management of its ecological niche on planet Earth [1]
Since legislation can only change the interaction rules which apply in human encounters, there is a need for theoretical modeling which is capable to predict the collective behavior in human societies on the basis of these interaction rules [3]
This bears close similarity to the physics of phase transitions and critical phenomena, where one seeks to predict the collective behavior of a large number of similar subsystems solely from their known mutual interactions [4,5]
Summary
The arguably most important question of our time is how humankind can devise a sustainable management of its ecological niche on planet Earth [1]. Since legislation can only change the interaction rules which apply in human encounters, there is a need for theoretical modeling which is capable to predict the collective behavior in human societies on the basis of these interaction rules [3]. This bears close similarity to the physics of phase transitions and critical phenomena, where one seeks to predict the collective behavior of a large number of similar subsystems (such as molecules) solely from their known mutual interactions [4,5]. This paradigm has been applied successfully, e.g., in modeling the emergence of polarization in opinion dynamics [6,7,8], where collective behavior was found
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