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This paper introduces a framework for generating socio-technical scenarios using AI-simulated expert panels to address limitations in conventional scenario generation. The framework uses AI models to simulate domain experts, employs probabilistic Cross-Impact Balance analysis for pathway generation, and incorporates AI stakeholder panels for decision analysis. Applied to Germany's energy transition, the approach demonstrates scalable scenario generation, policy stress-testing, and structured expert elicitation.
Forget expensive expert panels - this AI-driven framework generates diverse, internally consistent socio-technical scenarios, stress-tests them, and even picks the best pathway.
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.