Preprints
https://doi.org/10.5194/egusphere-2024-2661
https://doi.org/10.5194/egusphere-2024-2661
21 Oct 2024
 | 21 Oct 2024
Status: this preprint is open for discussion.

InsNet-CRAFTY v1.0: Integrating institutional network dynamics powered by large language models with land use change simulation

Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell

Abstract. Understanding and modelling environmental policy interventions can contribute to sustainable land use and management but is challenging because of the complex interactions among various decision-making actors. Key challenges include endowing modelled actors with autonomy, accurately representing their relational network structures, and managing the often-unstructured information exchange. Large language models (LLMs) offer new ways to address these challenges through the development of agents that are capable of mimicking reasoning, reflection, planning, and action. We present InsNet-CRAFTY (Institutional Network – Competition for Resources between Agent Functional Types) v1.0, a multi-LLM-agent model with a polycentric institutional framework coupled with an agent-based land system model. The numerical experiments simulate two competing policy priorities: increasing meat production versus expanding protected areas for nature conservation. The model includes a high-level policy-making institution, two lobbyist organisations, two operational institutions, and two advisory agents. Our findings indicate that while the high-level institution tends to avoid extreme budget imbalances and adopts incremental policy goals for the operational institutions, it leaves a budget deficit in one institution and a surplus in another unresolved. This is due to the competing influence of multiple stakeholders, which leads to the emergence of a path-dependent decision-making approach. Despite errors in information and behaviours by the LLM agents, the network maintains overall behavioural believability, demonstrating error tolerance. The results point to both the capabilities and challenges of using LLM agents to simulate policy decision-making processes of bounded rational human actors and complex institutional dynamics, such as LLM agents’ high flexibility and autonomy, alongside the complicatedness of agent workflow design and reliability in coupling with existing programmed land use systems. These insights contribute to advancing land system modelling and the broader field of institutional analysis, providing new tools and methodologies for researchers and policy-makers.

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Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell

Status: open (until 16 Dec 2024)

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Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell

Data sets

InsNet-CRAFTY v1.0 [data set] Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell https://doi.org/10.5281/zenodo.13944650

Model code and software

InsNet-CRAFTY v1.0 [code] Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell https://doi.org/10.5281/zenodo.13356487

Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell

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Short summary
Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.