AI-Simulated Expert Panels for Socio-Technical Scenarios and Decision Guidance
This offers a scalable alternative for scenario generation and decision support in socio-technical domains like energy policy, though it appears incremental as an AI-based enhancement to existing methods.
The paper tackles the resource-intensive and limited diversity of conventional socio-technical scenario generation by introducing an AI-simulated expert panel framework, which generates internally consistent pathways and provides decision support, demonstrated in a proof of concept for Germany's energy transition.
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.