AI-Simulated Expert Panels for Socio-Technical Scenarios and Decision Guidance
社会技術シナリオと意思決定ガイダンスのためのAIシミュレーション専門家パネル (AI 翻訳)
Andrew G. Ross, Allan Ross
🤖 gxceed AI 要約
日本語
本論文は、ネットゼロ移行などの社会技術シナリオを生成するためのAIベースの合成専門家パネルを提案する。従来の資源集約的な手法に代わり、確率的衝撃や多基準意思決定分析を用いて、内部的に一貫した経路を生成し、ドイツのエネルギー移行に適用して実証した。このフレームワークは、政策ストレステストや意思決定支援にも拡張可能である。
English
This paper introduces an AI-simulated expert panel to generate socio-technical scenarios for net-zero transitions. It uses probabilistic cross-impact balance analysis and multi-criteria decision analysis to create consistent pathways, applied to Germany's energy transition as a proof of concept. The framework is scalable and supports policy stress-testing and decision guidance.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも2050年カーボンニュートラル目標に向けたシナリオ策定が進む中、本手法はAIを活用した効率的なシナリオ生成と政策ストレステストを可能にし、日本のGX政策立案や企業戦略策定に応用できる可能性がある。
In the global GX context
This method offers a scalable, AI-driven approach to scenario generation that can complement traditional methods in global net-zero transitions. It is particularly useful for regions and countries (like Germany in the case study) seeking robust decision guidance under uncertainty, and can be adapted to other contexts such as ISSB or TCFD-aligned scenario analysis.
👥 読者別の含意
🔬研究者:Researchers in scenario methodology and energy system modeling will find a novel AI-based framework that enhances consistency and diversity in socio-technical pathway generation.
🏢実務担当者:Corporate sustainability teams can use this approach to stress-test net-zero strategies and generate model-ready inputs for transition planning.
🏛政策担当者:Policymakers can leverage the Virtual AI-Led Decision Laboratories for exploratory policy stress-testing and rapid expert elicitation.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.semanticscholar.org/paper/7edb19d99f36815c63531066e65df009a467f4e2first seen 2026-05-15 17:50:36
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。