用智能体大模型模拟多方协商，提升复杂决策的公平性与适应性。
Agentic LLM Framework for Adaptive Decision Discourse
- 构建多角色智能体框架，模拟不同利益相关方的对话
- 在德州洪水和中西部小镇极端洪灾场景中生成平衡多维目标的建议
- 适合高不确定性环境下需要协同决策的政策制定者或应急管理者
复杂系统中的有效决策需要整合多元视角以应对多维度挑战与不确定性。本研究提出一种基于智能体大语言模型（LLMs）的决策对话模拟框架，用于再现协作制定可行策略的思辨过程。不同于传统决策支持工具，该框架模拟具有独特优先级、专业背景与价值导向的多元利益相关方角色，在自我治理的讨论环境中强调权衡探索。实验结果表明，该框架可生成稳健且公平的建议：首个案例模拟2025年7月德克萨斯州洪水应对；第二个案例为中西部小镇在不同预报不确定性下的假设极端洪水情景。建议综合考量社会、经济与环境维度，为可扩展、情境感知的决策提供基础，革新数字环境中重大现实问题的应对方式。本研究探索了利用智能体大模型实现自适应、协作与公平推荐的新路径，适用于不确定性与复杂性交汇的多个领域。
原文摘要 · Abstract (English)
Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces an agentic Large Language Models (LLMs) framework for simulating decision discourse - the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, this framework simulates diverse stakeholder personas, each bringing unique priorities, expertise and value-driven reasoning to a dialogue that emphasizes trade-off exploration in a self-governed assembly. We present explorative results fostering robust and equitable recommendations, with two use cases: first, our framework simulates a response to the floods that occurred on July 2025 in Texas; second, a hypothetical extreme flooding in a Midwestern township under varying forecasting uncertainty. Recommendations made balance competing priorities considered through social, economic and environmental dimensions, setting a foundation for scalable and context-aware recommendations and transforming how decisions for real-world high-stake scenarios can be approached in digital environments. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendations, with implications across domains where uncertainty and complexity converge.
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