Multi-agent social dilemmas, such as the tragedy of the commons, capture settings where individual incentives conflict with collective well-being, making these systems highly vulnerable to collapse under disruptions. In this context, this work studies cooperative resilience, understood as the system-level ability to maintain collective well-being under perturbations through adaptive agent behavior. We propose a framework for learning incentive structures aligned with collective well-being in multi-agent reinforcement learning systems, where reward functions shape individual decision-making and collective behavior. A resilience metric is used to score and rank agent trajectories, allowing the inference of reward functions that promote resilient collective behavior. These inferred reward functions are integrated into the multi-agent reinforcement learning process to shape agent interactions in social dilemma settings. The approach is evaluated in resource-sharing environments subject to disruptions, using three incentive structures: individual incentives, resilience-aligned incentives, and a hybrid incentive structure that combines both individual and collective components. The results show that the hybrid incentive structure promotes sustained collective behavior, reduces collapse events associated with resource depletion, and preserves system performance under disruption. These findings highlight the role of incentive design as a mechanism for promoting resilient collective behavior and provide a computational framework for multi-agent social dilemmas under disruptions.

翻译：多智能体社会困境（如公地悲剧）刻画了个人激励与集体福祉相冲突的场景，此类系统在干扰下极易崩溃。本研究聚焦合作韧性，即系统通过智能体自适应行为维持集体福祉的能力。我们提出一个框架，用于学习与集体福祉对齐的激励结构，在多智能体强化学习系统中，奖励函数塑造个体决策与集体行为。采用韧性指标对智能体轨迹进行评分与排序，进而推断能促进韧性集体行为的奖励函数，并将其融入多智能体强化学习过程，以塑造社会困境环境中的智能体交互。该方法在面临干扰的资源共享环境中进行评估，采用三种激励结构：个体激励、韧性对齐激励，以及兼具个体与集体成分的混合激励结构。结果表明，混合激励结构能够促进持续的集体行为，减少资源枯竭导致的崩溃事件，并在干扰下保持系统性能。这些发现强调了激励设计作为促进韧性集体行为机制的作用，并为干扰下的多智能体社会困境提供了计算框架。