Socio-technical scenarios for net-zero and other transformation pathways combine qualitative storylines with quantitative models, embedding them in plausible societal contexts for model assessment. Conventional scenario generation is resource-intensive, can be limited in internal consistency and diversity of expert and stakeholder perspectives, and is rarely stress-tested. This paper introduces a synthetic, AI-based expert panel to address these bottlenecks. An AI model first simulates domain experts who agree on descriptors, states, and their interactions. A probabilistic Cross-Impact Balance analysis then generates internally consistent pathways, using stochastic shocks to assess robustness and pathway diversity. An AI stakeholder panel uses multi-criteria decision analysis to select a preferred pathway; an AI expert panel translates it into model-ready quantitative inputs. Although scalable and applicable to any other country or region, the framework is applied to Germany's energy transition as a proof of concept, and offers an alternative and/or supplement to scenario generation. Furthermore, it enables Virtual AI-Led Decision Laboratories for exploratory policy stress-testing and provides an approach for rapid, structured expert elicitation and decision support in other domains.

翻译：为实现净零及其他转型路径的社会技术情景，需将定性叙事与定量模型相结合，并将其嵌入合理的社会背景中进行模型评估。传统情景生成方法资源消耗大，专家与利益相关者视角的内在一致性与多样性受限，且鲜少经受压力测试。本文提出一种基于人工智能的合成专家小组方法以突破这些瓶颈。首先，AI模型模拟领域专家对描述因子、状态及其交互关系达成共识；随后，通过概率性交叉影响平衡分析生成具有内在一致性的路径，并利用随机冲击评估路径鲁棒性与多样性。AI利益相关者小组采用多准则决策分析法选择最优路径，AI专家小组则将其转化为模型可用的量化输入。尽管该框架具有可扩展性并适用于任何国家或地区，本研究以德国能源转型为概念验证进行应用，为情景生成提供了替代/补充方案。此外，该方法可构建探索性政策压力测试的虚拟AI驱动决策实验室，并为其他领域快速、结构化的专家启发与决策支持提供新范式。