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State and federal governments use governance platforms to achieve central policy goals through distributed action at the local level. For example, California’s 2014 Sustainable Groundwater Management Act (SGMA) mandates local policy actors to work together to create new groundwater management institutions and plans. We argue that governance platforms entail a principal-agent problem where local decisions may deviate from central goals. We apply this argument to SGMA implementation, where local plans may respond more to local political economic conditions rather than address the groundwater problems prioritized by the state. Using a Structured Topic Model (STM) to analyze the content of 117 basin management plans, we regress each plan’s focus on core management reform priorities on local socio-economic and social-ecological indicators expected to shape how different communities respond to state requirements. Our results suggest that the focus of local plans diverges from problem conditions on issues like environmental justice and drinking water quality. This highlights how principal-agent logics of divergent preferences and information asymmetry can affect the design and implementation of governance platforms. 

Abstract Although considerable research over the past two decades has examined collective learning in environmental governance, much of this scholarship has focused on cases where learning occurred, limiting our understanding of the drivers and barriers to learning. To advance knowledge of what we call the “collective learning continuum,” we compare cases of learning to cases where learning was not found to occur or its effects were “blocked.” Through semi‐structured interviews with key stakeholders in science‐policy forums in the Colorado River Basin, a large and complex river basin in western North America, we examine differences and patterns that explain moments of learning, blocked learning, or non‐learning, drawing insights from the collective learning framework. Our results find various factors that influence learning, blocked learning, and non‐learning. We discover technical and social factors as common drivers of both learning and blocked learning. In contrast, we find more structural factors associated with non‐learning. At the same time, the cases reveal insights about the role of political factors, such as timing, legal constraints, and priorities, which are underdeveloped in the collective learning framework. Overall, these findings advance theoretical knowledge of the collective learning continuum and offer practical insights that may strengthen the coordination of science and management for effective governance within the Basin.