LLM-empowered Agents Simulation Framework for Scenario Generation in Service Ecosystem Governance

• URL: http://arxiv.org/abs/2509.01441v1
• Date: Mon, 01 Sep 2025 12:55:02 GMT
• Title: LLM-empowered Agents Simulation Framework for Scenario Generation in Service Ecosystem Governance
• Authors: Deyu Zhou, Yuqi Hou, Xiao Xue, Xudong Lu, Qingzhong Li, Lizhen Cui,
• Abstract summary: We propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents.<n>The Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions.
• Score: 45.203064499446775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the social environment is growing more complex and collaboration is deepening, factors affecting the healthy development of service ecosystem are constantly changing and diverse, making its governance a crucial research issue. Applying the scenario analysis method and conducting scenario rehearsals by constructing an experimental system before managers make decisions, losses caused by wrong decisions can be largely avoided. However, it relies on predefined rules to construct scenarios and faces challenges such as limited information, a large number of influencing factors, and the difficulty of measuring social elements. These challenges limit the quality and efficiency of generating social and uncertain scenarios for the service ecosystem. Therefore, we propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents that autonomously optimize experimental schemes to construct an experimental system and generate high quality scenarios. Specifically, the Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions. These agents work in coordination, with the PA adjusting the experimental scheme in real time by perceiving the states of each agent and these generating scenarios. Experiments on the ProgrammableWeb dataset illustrate our method generates more accurate scenarios more efficiently, and innovatively provides an effective way for service ecosystem governance related experimental system construction.

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