Social Networks and Learning for Sustainability in Regional Planning

One of the most important goals of collaborative planning processes is to promote successful learning by and amongst networked groups of actors. "Learning" is the process by which stakeholders collectively hone their abilities to address complex problems, develop common understandings of issues to be addressed, and produce effective policy solutions. Despite the importance of learning to sustainable planning efforts, there is a paucity of research on how and why learning occurs. This dissertation uses social networks as the primary organizing concept through which to study learning in the context of regional land use and transportation planning. Data are collected using a survey instrument from stakeholders involved in five regional planning processes in California. The survey measures belief systems, various social network structures, and individual involvement in collaborative efforts. Exponential random graph models will be used to explore the determinants of network structure and identify signatures of learning. This analysis will be supplemented with agent-based computer simulations to investigate how planning institutions can be better designed to promote desirable network structures and enable learning. Ultimately, this research will contribute to a coherent theory of how collaborative institutions can promote more sustainable transportation and land use planning through learning.