Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies

Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies

The Anh Han, Simon Lynch, Long Tran-Thanh, Francisco C. Santos

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 289-295. https://doi.org/10.24963/ijcai.2018/40

We study the situation of an exogenous decision-maker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost. The primary goal is therefore to reach an efficient trade-off between pushing the agents to achieve the desired configuration while minimising the total investment. To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns.  Our results show that taking into account the neighbourhood's local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. As such, we argue that local interference strategies are more efficient than global ones in fostering cooperation in a population of autonomous agents.
Keywords:
Agent-based and Multi-agent Systems: Agent Societies
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence