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

2025-09-01

AI Summary

Governance of service ecosystems faces challenges in modeling complex socio-environmental dynamics, evolving collaborative behaviors, and uncertain scenarios. Method: This paper proposes a large language model (LLM)-driven multi-agent simulation framework comprising three co-adaptive agent types—environmental, social, and planning agents—that jointly enable adaptive socio-structural modeling and dynamic experimental design optimization, overcoming the rigidity of rule-based approaches. The framework integrates environmental modeling, social structure generation, and task-role co-planning to support inter-agent state perception and evolutionary inference. Results: Experiments on the ProgrammableWeb dataset demonstrate significant improvements in both accuracy and efficiency for generating uncertain socio-environmental scenarios. The framework establishes a programmable, experimentally grounded decision-support paradigm for service ecosystem governance.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Simulating complex social environments for service ecosystem governance
• Overcoming predefined rule limitations in scenario generation
• Adaptively coordinating LLM agents for high-quality scenario construction

Innovation

• LLM agents autonomously optimize experimental schemes
• Three specialized agents generate social environments and structures
• Real-time adjustment of scenarios based on agent states