[2603.17021] Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertainty

Computer Science > Artificial Intelligence arXiv:2603.17021 (cs) [Submitted on 17 Mar 2026 (v1), last revised 19 Mar 2026 (this version, v2)] Title:Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertainty Authors:Zhihao Pei, Nir Lipovetzky, Angela M. Rojas-Arevalo, Fjalar J. de Haan, Enayat A. Moallemi View a PDF of the paper titled Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertainty, by Zhihao Pei and 4 other authors View PDF HTML (experimental) Abstract:Socio-environmental planning under deep uncertainty requires researchers to identify and conceptualize problems before exploring policies and deploying plans. In practice and model-based planning approaches, this problem conceptualization process often relies on participatory modeling to translate stakeholders' natural-language descriptions into a quantitative model, making this process complex and time-consuming. To facilitate this process, we propose a templated workflow that uses large language models for an initial conceptualization process. During the workflow, researchers can use large language models to identify the essential model components from stakeholders' intuitive problem descriptions, explore their diverse perspectives approaching the problem, assemble these components into a unified model, and eventually implement the model in Python through iterative communication. These results will facilitate the subsequent soci...