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In many applications, the goal is to promote pro-social behavior among agents, where network structure plays a pivotal role in shaping these interactions. This article introduces a hierarchical graph reinforcement learning (HGRL) framework that governs such systems through targeted interventions in the network structure. Operating within the constraints of limited managerial authority, the HGRL framework demonstrates superior performance across a range of environmental conditions, outperforming established baseline methods. Our findings highlight the critical influence of agent-to-agent learning (social learning) on system behavior: under low social learning, the HGRL manager preserves cooperation, forming robust core-periphery networks dominated by cooperators. In contrast, high social learning accelerates defection, leading to sparser, chain-like networks. Additionally, the study underscores the importance of the system manager\u2019s authority level in preventing system-wide failures, such as agent rebellion or collapse, positioning HGRL as a powerful tool for dynamic network-based governance.<\/jats:p>","DOI":"10.1115\/1.4068483","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T14:43:56Z","timestamp":1744987436000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach"],"prefix":"10.1115","volume":"25","author":[{"given":"Qiliang","family":"Chen","sequence":"first","affiliation":[{"name":"Northeastern University Department of Mechanical and Industrial Engineering, Institute of Experiential AI, and Network Science Institute, , , \u00a0","place":["Boston, MA, 02115"]}]},{"given":"Babak","family":"Heydari","sequence":"additional","affiliation":[{"name":"Northeastern University Department of Mechanical and Industrial Engineering, Institute of Experiential AI, and Network Science Institute, , , \u00a0","place":["Boston, MA, 02115"]}]}],"member":"33","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"issue":"1","key":"2025043022590396200_CIT0001","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ejor.2017.04.009","article-title":"Supply Chain Network Design Under Uncertainty: A Comprehensive Review and Future Research Directions","volume":"263","author":"Govindan","year":"2017","journal-title":"Eur. 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