To Promote Full Cooperation in Social Dilemmas, Agents Need to Unlearn Loyalty

To Promote Full Cooperation in Social Dilemmas, Agents Need to Unlearn Loyalty

Chin-wing Leung, Tom Lenaerts, Paolo Turrini

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 111-119. https://doi.org/10.24963/ijcai.2024/13

If given the choice, what strategy should agents use to switch partners in strategic social interactions? While many analyses have been performed on specific switching heuristics, showing how and when these lead to more cooperation, no insights have been provided into which rule will actually be learnt by agents when given the freedom to do so. Starting from a baseline model that has demonstrated the potential of rewiring for cooperation, we provide answers to this question over the full spectrum of social dilemmas. Multi-agent Q-learning with Boltzmann exploration is used to learn when to sever or maintain an association. In both the Prisoner's Dilemma and the Stag Hunt games we observe that the Out-for-Tat rewiring rule, breaking ties with other agents choosing socially undesirable actions, becomes dominant, confirming at the same time that cooperation flourishes when rewiring is fast enough relative to imitation. Nonetheless, in the transitory region before full cooperation, a Stay strategy, keeping a connection at all costs, remains present, which shows that loyalty needs to be overcome for full cooperation to emerge. In conclusion, individuals learn cooperation-promoting rewiring rules but need to overcome a kind of loyalty to achieve full cooperation in the full spectrum of social dilemmas.
Keywords:
Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence
Agent-based and Multi-agent Systems: MAS: Agent societies
Agent-based and Multi-agent Systems: MAS: Coordination and cooperation
Agent-based and Multi-agent Systems: MAS: Multi-agent learning