Fang, Chenyu

Collective decision-making in rural land management crucially demands equitable and inclusive participation, yet the stakeholders’ diversity often impedes true representation and active involvement. Advances in large language models (LLMs) have enabled more authentic simulations of stakeholder roles and behaviors, enhancing the transparency and accuracy of collective decision-making processes in rural settings. This study utilizes an agent-based modeling approach to investigate collective decision-making in the marketization of collectively owned rural land in China. By integrating insights from interviews with diverse local stakeholders—local farmers, corporate managers, and village officials—the model reflects the complexities of rural communities where stakeholder diversity often leads to conflicting interests and priorities. The simulation result, obtained through three voting rounds achieving a consensus with a two-thirds majority, demonstrates how large language model-driven simulations can effectively address these conflicts by ensuring diverse perspectives are adequately represented and considered. Additionally, the analysis highlights a notable shift in focus on keywords such as 'trust', 'transparency', and 'sustainability' across the voting rounds, illustrating how these evolving priorities play a pivotal role in achieving consensus by the final round. This approach illustrates the potential of agent-based models in facilitating more informed and equitable decision-making processes, thereby offering valuable perspectives for policy development and community planning in similar socio-economic settings.