Realizing the Potential of Digital Twins Through Community-led, Science-driven Participatory Modeling: A Case in Green Infrastructure Planning
Abstract
Recent research, professional, and funding agendas have re-surfaced the importance of knowledge co-production and ethical participation to address urban tensions worldwide: urbanization and rapid climate change, disproportionately impacting socially vulnerable populations. Despite the potential of data-driven technologies to address these tensions, they have fallen short from their promise. We present a participatory modeling (PM) platform, fora.ai, to build on existing strengths of DT and overcome their most prevalent limitations. This platform is organized around the iterative steps in PM: problem definition and goal setting, preference elicitation, collaborative scenario-building, simulation, tradeoff deliberation, and solution-building. We demonstrate the platform’s effectiveness when embedded in a stakeholder-led process that integrates diverse knowledge, data sources, and values in pursuit of equitable green infrastructure (GI) planning to address flooding.
Professor of Public Policy and Urban Affairs; Director of Participatory Modeling and Data Science;
Co-Director of NULab for Digital Humanities and Computational Social Science