多智能体系统中具有环境反馈的主动激励调控
Proactive Incentive Regulation in Multi-Agent Systems with Environmental Feedback
- College of Science, Northwest A & F University（西北农林科技大学理学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出多智能体环境反馈博弈框架，证明资源丰富时主动惩罚可促合作，资源稀缺时惩罚导致复杂动力学，强调激励调控时机的重要性。
AI中文摘要：
在环境反馈系统中，理性智能体的自利行为常常破坏合作与环境可持续性。尽管惩罚性激励机制被广泛认为是解决此类社会困境的有效手段，但在不同环境条件下其实施时机仍未得到充分理解。本文构建了一个多智能体环境反馈博弈框架，其中激励强度与资源状态相耦合。我们研究了在资源丰富和资源稀缺两种条件下，系统动力学如何随激励强度变化。理论分析表明，在资源丰富状态下施加惩罚，可以将系统从公地悲剧转变为支持完全合作的双稳态机制。相反，在资源稀缺状态下施加惩罚，则会导致复杂的动力学行为，包括内部稳定性、异宿环和Hopf分岔，这些行为阻碍了稳定合作的出现。这些结果凸显了激励调控时机的重要性。在有利环境条件下进行主动干预，比在环境退化后延迟执行更有效地维持合作。
英文摘要：
In environmental feedback systems, self-interested behaviors of rational agents often undermine cooperation and environmental sustainability. Although punitive incentive mechanisms are widely recognized as effective in addressing such social dilemmas, the timing of their implementation under different environmental conditions remains insufficiently understood. In this paper, we develop a multi-agent environmental feedback game framework with coupled incentive intensities across resource states. We investigate how system dynamics vary with incentive intensity under both resource-abundant and resource-scarce conditions. Theoretical analysis shows that imposing penalties in resource-abundant states can transform the system from a tragedy of the commons into a bistable regime that supports full cooperation. In contrast, imposing penalties in resource-scarce states leads to complex dynamical behaviors, including interior stability, heteroclinic cycles, and Hopf bifurcations, which hinder the emergence of stable cooperation. These results highlight the importance of timing in incentive regulation. Proactive intervention under favorable environmental conditions is more effective in sustaining cooperation than delayed enforcement after environmental degradation.
