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

📄 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.

🔍 Key Points

Introduction of a novel LLM-empowered agents framework designed specifically for scenario generation in service ecosystem governance, addressing the challenges posed by traditional methods reliant on predefined rules.
The coordination of three specialized agents — the Environment Agent (EA), Social Agent (SA), and Planner Agent (PA) — which together generate high-quality scenarios through real-time adaptive optimization.
Demonstrated capability of the proposed framework to generate extreme scenarios, enhancing the governance strategies related to ‘black swan’ events that are typically difficult to anticipate and model in complex adaptive systems.
The framework was validated through significant computational experiments using the ProgrammableWeb dataset, showing that it produces more accurate and efficient scenarios compared to existing methodologies.
Provides a closed-loop mechanism for adaptive scenario generation, which enhances the understanding of social interactions and improves the experimental operationalization in service ecosystems.

💡 Why This Paper Matters

The paper presents a groundbreaking approach to managing and simulating scenarios in complex service ecosystems through LLM-empowered agents. By addressing existing methodological gaps, particularly in generating extreme and uncertain scenarios, this research not only advances the theoretical framework of service ecosystem governance but also offers practical tools for enhancing decision-making in dynamic environments. The positive evaluations from computational experiments further affirm its robustness and applicability, marking a significant contribution to the fields of AI, governance, and ecosystem research.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers may find this paper particularly compelling as it highlights innovative applications of LLMs in generating scenarios that consider various social and environmental factors. The adaptive nature of the framework could lead to better risk assessment models, particularly in identifying vulnerabilities in service ecosystems. Moreover, understanding the emergent behavior in complex systems through such simulation frameworks can aid in fortifying AI against emergent threats, making this work a crucial point of reference for optimizing security protocols in dynamic environments.

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

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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