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

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.

Figures (5)

• Figure 1: The two DMDU processes covered in this paper.
• Figure 2: The LLM-assisted workflow for communication with LLMs for initial problem conceptualization. In this diagram, nodes represent discrete steps in the workflow. Red text denotes the information transferred between steps, whereas blue text denotes iterative human validation, verification and refinement of the responses.
• Figure 3: The overall experimental design. In our experiments, we considered two case studies, the lake problem and the electricity market problem. For the former, we only considered the sub-case with a comprehensive problem description. For the latter, we considered both the sub-case with a comprehensive problem description and the sub-case with a brief problem description. For each sub-case, we ran the initial problem conceptualization process several times following the proposed workflow.
• Figure 4: Average time series of lake pollution for both perspectives, with a fixed sequence of pollution emission $\bar{a_t}$ (decisions from Perspective 1: local community), and varying pollution removal levels $r_t$ (decisions from Perspective 2: environmental regulator). To generate each of these figures, we ran the Python implementation produced in each experimental run ten times, collecting a total of 40 time series. Figure \ref{['fig:lake1']} shows the impact of Perspective 1 on the system alone, while Figure \ref{['fig:lake2']}–\ref{['fig:lake4']} show the combined impact of Perspective 1 and the increasing influence of Perspective 2 on the system.
• Figure 5: Average time series of clearing prices, actual dispatched wind energy and wind-power revenue in both sub-cases of the electricity market problem for both perspectives, with a fixed sequence of bids for the wind-power producer $(\bar{b_{wt}}, \bar{p_{wt}}) = (300, 50)$ (decisions from Perspective 1: wind-power producer), and varying shortfall penalty coefficients $q_{u}$ (decisions from Perspective 2: system regulator). To generate each of these figures, we ran the Python implementation produced in each experimental run ten times, collecting a total of 80 time series. Figure \ref{['fig:market1']}-\ref{['fig:market3']} show the impact of Perspective 1 on the system alone, while Figure \ref{['fig:market4']} shows the combined impact of Perspective 1 and the increasing influence of Perspective 2 on the system.