Researchers have developed a novel framework that integrates Large Language Model (LLM)-based agents into a human-supervised negotiation process for risk assessment and consensus-building within the One Health context. This approach uses retrieval-augmented generation to create agent descriptions from stakeholder materials and employs these agents to access domain-specific computational models and web sources. A simulated negotiation game, evaluated with domain experts on scenarios involving food safety and pest management, demonstrated that participants could reach consensus within allotted timeframes after modifying proposed deals. AI
IMPACT This framework could enhance efficiency and decision quality in complex, multi-stakeholder risk negotiations across various domains.
RANK_REASON Academic paper detailing a novel methodology for using LLMs in risk negotiation. [lever_c_demoted from research: ic=1 ai=1.0]
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