Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game

Understand

Multi-agent reinforcement learning algorithms are useful for simulating social behavior in settings that are too complex for other theoretical approaches like game theory.

• However, they have not yet been empirically supported by laboratory experiments with real human participants.
• In this work we demonstrate how multi-agent reinforcement learning can model group behavior in a spatially and temporally complex public good provision game called Clean Up.
• We show that human groups succeed in Clean Up when they can see who is who and track reputations over time but fail under conditions of anonymity.