Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building
🤖 AI Summary
Global risks—such as biopesticide misuse and wildlife population imbalances—are highly complex and cross-sectorally interdependent, rendering traditional risk analysis inadequate for coordinated governance due to oversimplified assumptions and data silos. To address this, we propose the first open-source web framework designed for multi-stakeholder deliberation, integrating LLM-driven semantic integration and AI autonomous agents that simulate dynamic negotiation processes. The framework enables compromise prediction, impact assessment of proposed solutions, and user-defined extensibility. It transcends static analytical paradigms by facilitating efficient consensus-building among heterogeneous stakeholders under time pressure and information overload. Evaluated in two real-world public health risk scenarios, the framework demonstrably improves negotiation efficiency and decision quality. It provides a reusable methodological framework and technical infrastructure for systemic governance of complex, transdisciplinary public health risks.
Application Category
📝 Abstract
Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, creating silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving this balance is often hindered by limited time, vast amounts of information, and the complexity of integrating diverse perspectives. This study presents an AI-assisted negotiation framework that incorporates large language models (LLMs) and AI-based autonomous agents into a negotiation-centered risk analysis workflow. The framework enables stakeholders to simulate negotiations, systematically model dynamics, anticipate compromises, and evaluate solution impacts. By leveraging LLMs' semantic analysis capabilities we could mitigate information overload and augment decision-making process under time constraints. Proof-of-concept implementations were conducted in two real-world scenarios: (i) prudent use of a biopesticide, and (ii) targeted wild animal population control. Our work demonstrates the potential of AI-assisted negotiation to address the current lack of tools for cross-sectoral engagement. Importantly, the solution's open source, web based design, suits for application by a broader audience with limited resources and enables users to tailor and develop it for their own needs.
Problem
Research questions and friction points this paper is trying to address.
Addressing complex One Health risks through integrated stakeholder negotiations
Overcoming information overload and time constraints in risk analysis
Developing AI-assisted tools for cross-sectoral consensus building
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-assisted negotiation framework with LLMs
Simulates negotiations and anticipates compromises
Open source web-based design for broad application
🔎 Similar Papers
No similar papers found.
