Simulating Multi-Stakeholder Decision-Making with Generative Agents in Urban Planning
Jin Gao, Hanyong Xu, Luc Dao
TL;DR
The paper addresses the challenge of achieving timely, equitable decisions in urban planning by simulating multi-stakeholder discussions with generative agents grounded in real-world data. It adopts an urban rezoning case (Kendall Square) and deploys eight LLM-driven agents with structured prompts reflecting roles, demographics, and life values, coordinated by a government admin and enhanced by human-in-the-loop inputs. Key findings show that agent communication improves the quality of collective reasoning and that incorporating demographic and life-value data increases diversity and stability of outputs, enabling better anticipation of stakeholder reactions. The work offers a predictive, iterative framework for policymakers to refine proposals before public release, potentially improving equity and reducing costs in urban planning interventions.
Abstract
Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the development of multi-generative agent systems, offering a promising approach to simulating discussions and interactions among diverse stakeholders on contentious topics. However, applying such systems also carries significant societal and ethical risks, including misrepresentation, privacy concerns, and biases stemming from opinion convergence among agents, hallucinations caused by insufficient or biased prompts, and the inherent limitations of foundation models. To evaluate the influence of these factors, we incorporate varying levels of real-world survey data and demographic detail to test agents' performance under two decision-making value frameworks: altruism-driven and interest-driven, using a real-world urban rezoning challenge. This approach evaluates the influence of demographic factors such as race, gender, and age on collective decision-making in the design of multi-generative agent systems. Our experimental results reveal that integrating demographic and life-value data enhances the diversity and stability of agent outputs. In addition, communication among generated agents improves the quality of collective reasoning. These findings provide a predictive framework for decision-makers to anticipate stakeholder reactions, including concerns, objections, and support. By enabling iterative refinement of proposals before public release, the simulated approach fosters more equitable and cost-effective decisions in urban planning.