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

2026-03-17

AI Summary

This study addresses the challenges of participatory modeling in socio-environmental planning under deep uncertainty, where conventional approaches rely on labor-intensive, expert-mediated translation of stakeholders’ natural language into quantitative models—a process that is both cumbersome and inaccessible. To overcome these limitations, the authors propose a templated workflow integrating large language models (e.g., GPT-4) to enable iterative human–AI collaboration. This approach automatically extracts key model components from unstructured problem descriptions, synthesizes diverse stakeholder perspectives, and generates executable computational models in Python. By introducing generative AI into the initial conceptualization phase of participatory modeling for the first time, the method substantially enhances both efficiency and accessibility. Its feasibility and practical utility are demonstrated through two case studies—lake governance and electricity markets—where valid models were produced with minimal iterations and limited expert validation.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 socio-environmental planning under deep uncertainty steps. Using ChatGPT 5.2 Instant, we demonstrated this workflow on the lake problem and an electricity market problem, both of which demonstrate socio-environmental planning problems. In both cases, acceptable outputs were obtained after a few iterations with human verification and refinement. These experiments indicated that large language models can serve as an effective tool for facilitating participatory modeling in the problem conceptualization process in socio-environmental planning.

Problem

• participatory modeling
• socio-environmental planning
• deep uncertainty
• problem conceptualization
• generative AI

Innovation

• Generative AI
• Participatory Modeling
• Large Language Models
• Socio-Environmental Planning
• Deep Uncertainty