InsNet-CRAFTY V1.0: Integrating Institutional Network Dynamics Powered by Large Language Models with Land Use Change Simulation

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.