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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-06-16
摘要
合作 at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior-in which intelligent actors in complex environments jointly improve their well-being-remains poorly understood. 複雜系統s science (CSS) provides a rich understanding of collective phenomena, the 演化 of 合作, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context-largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multi智能體 reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be 計算ly expensive and challenging
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