AI 中文总结
该研究提出智能体世界分析（AWA）方法，结合模拟建模与专家征询，实现生成式智能体世界引擎（WEGA），并以荷兰氮危机为例验证其功能，生成两条未来路径供评估。
AI 中文摘要
为应对日益紧迫的可持续性挑战，已开发多种方法用于预测可能的未来、识别失效模式、检测漏洞并测试潜在缓解措施。但环境系统高度复杂，尤其与人类过程耦合时，不确定性规模变得难以处理。为解决这一挑战，我们提出一种新方法——智能体世界分析（Agentic World Analysis，AWA），结合模拟建模与专家征询的优势。AWA的概念由三个属性定义：1）AWA使用智能体AI系统模拟研究世界的专家小组；2）AWA通过分析情景树迭代预测未来，并从该分析中学习以改进决策；3）AWA是可审计的。基于这些要求，我们实现了生成式智能体世界引擎（World Engine by Generative Agents，WEGA）作为AWA方法的可能应用，并通过一个真实案例研究——荷兰氮危机——展示其功能。WEGA自主构建了背景、识别关键利益相关者和不确定性、创建专家智能体并生成未来情景。结果提出了2026至2041年的两条路径，两者的共同假设是氮减排政策的社会接受度低，而差异在于根据氮数据监测的实施情况，恢复的成功程度不同。这些路径在多个维度接受评估，以评估其逻辑连贯性和质量。评估还主动暴露优势和劣势，为测试所提政策的有效性提供途径。我们讨论了扩大情景分析规模以实现大规模路径探索、使用AWA与其他方法的权衡，以及关于AI系统的常见担忧。
英文摘要
To address increasingly pressing sustainability challenges, various approaches have been developed to foresee possible futures, identify failure modes, detect vulnerabilities, and test potential mitigations. However, environmental systems are highly complex. Especially when coupled with human processes, the scale of uncertainties becomes intractable. To address this challenge, we propose a new approach - Agentic World Analysis (AWA)- combining the strengths of simulation modelling and expert elicitation. The concept of AWA is defined by three properties: 1) AWA uses an agentic AI system to mimic an expert panel that studies the world; 2) AWA projects futures iteratively through analysing scenario trees and learning from this analysis to improve decisions; 3) AWA is auditable. Based on these requirements, we implemented the World Engine by Generative Agents (WEGA) as a possible application of the AWA approach and demonstrated its functionality with a real-world case study: the Nitrogen Crisis in the Netherlands. WEGA autonomously constructed the context, identified key stakeholders and uncertainties, created expert agents, and generated future scenarios. As a result, two pathways from 2026 to 2041 were proposed, sharing a common assumption that social acceptance of nitrogen mitigation policies is low, while differing in how successful the restoration is according to the implementation of nitrogen data monitoring. The pathways are evaluated in multiple dimensions to assess their logical coherence and quality. The evaluation also actively exposes strengths and weaknesses to provide ways for testing the validity of the policies proposed. We discussed scaling up scenario analyses to enable massive pathway exploration, the trade-offs of using AWA and other approaches, and common concerns regarding AI systems.
