🤖 AI Summary
This study addresses the challenge of quantifying residents’ subjective preferences in large-scale planning communities, which hinders democratic strategic decision-making. We propose a community-level preference quantification framework grounded in utility theory and an exponential marginal diminishing utility model. Preferences are elicited via brief online surveys on public facility and infrastructure investment priorities, then integrated with usage forecasts, cost/risk sensitivity, and willingness-to-pay to enable group-level preference aggregation and automated multi-alternative ranking. To our knowledge, this is the first application of an exponential marginal diminishing utility model at the community scale for strategic planning, enabling end-to-end translation from subjective preferences to actionable investment options. Empirical evaluation demonstrates that the framework significantly improves ranking consistency and public acceptability, while accurately predicting resident support levels and actual contribution willingness.
📝 Abstract
This paper introduces a strategic planning tool for master-planned communities designed specifically to quantify residents' subjective preferences about large investments in amenities and infrastructure projects. Drawing on data obtained from brief online surveys, the tool ranks alternative plans by considering the aggregate anticipated utilization of each proposed amenity and cost sensitivity to it (or risk sensitivity for infrastructure plans). In addition, the tool estimates the percentage of households that favor the preferred plan and predicts whether residents would actually be willing to fund the project. The mathematical underpinnings of the tool are borrowed from utility theory, incorporating exponential functions to model diminishing marginal returns on quality, cost, and risk mitigation.