  In this study, we develop a multiple-generative agent system to simulate
community decision-making for the redevelopment of Kendall Square's Volpe
building. Drawing on interviews with local stakeholders, our simulations
incorporated varying degrees of communication, demographic data, and life
values in the agent prompts. The results revealed that communication among
agents improved collective reasoning, while the inclusion of demographic and
life values led to more distinct opinions. These findings highlight the
potential application of AI in understanding complex social interactions and
decision-making processes, offering valuable insights for urban planning and
community engagement in diverse settings like Kendall Square.
