Limited work has examined the strategic behaviors of relational networked learning agents under social dilemmas, and has overlooked the intricate social dynamics of complex systems. We address the challenge with Socio-Relational Intrinsic Motivation (SRIM), which endows agents with diverse preferences over sub-graphical social structures in order to study the impact of agents' personal preferences over their sub-graphical relations on their strategic decision-making under sequential social dilemmas. Our results in the Harvest and Cleanup environments demonstrate that preferences over different subgraph structures (degree-, clique-, and critical connection-based) lead to distinct variations in agents' reward gathering and strategic behavior: individual aggressiveness in Harvest and individual contribution effort in Cleanup. Moreover, agents with different subgraphical structural positions consistently exhibit similar strategic behavioral shifts. Our proposed BCI metric captures structural variation within the population, and the relative ordering of BCI across social preferences is consistent in Harvest and Cleanup games for the same topology, suggesting the subgraphical structural impact is robust across environments. These results provide a new lens for examining agents' behavior in social dilemmas and insight for designing effective multi-agent ecosystems composed of heterogeneous social agents.

翻译：关于关系型网络学习主体在社会困境下的策略行为研究有限，且忽视了复杂系统内精细的社会动力学。我们通过社会关系内在动机（SRIM）应对这一挑战，该机制赋予主体对子图社会结构的多样化偏好，旨在研究主体对子图关系的个人偏好如何影响其在序贯社会困境下的策略决策。我们在Harvest和Cleanup环境中的实验表明：对不同子图结构（基于度数、团簇和关键连接）的偏好会导致主体奖励获取与策略行为的显著差异——Harvest中的个体攻击性及Cleanup中的个体贡献努力程度。此外，处于不同子图结构位置的主体始终表现出相似的策略行为转变。我们提出的BCI指标能够捕获种群内的结构变异，且相同拓扑结构下不同社会偏好的BCI相对排序在Harvest与Cleanup游戏中具有一致性，表明子图结构影响具有跨环境稳健性。这些成果为审视社会困境中的主体行为提供了新视角，并为设计由异构社会主体构成的有效多智能体生态系统提供了洞见。