A reputation-motivated multi-agent RL model reproduces human groups' cooperation under identifiability and its collapse under anonymity in the Clean Up public goods game.
The Importance of Credo in Multiagent Learning
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abstract
We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i.e., teams). Our model of credo regulates how agents optimize their behavior for the groups they belong to. We evaluate credo in the context of challenging social dilemmas with reinforcement learning agents. Our results indicate that the interests of teammates, or the entire system, are not required to be fully aligned for achieving globally beneficial outcomes. We identify two scenarios without full common interest that achieve high equality and significantly higher mean population rewards compared to when the interests of all agents are aligned.
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Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
A reputation-motivated multi-agent RL model reproduces human groups' cooperation under identifiability and its collapse under anonymity in the Clean Up public goods game.