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Networks are an important part of the environmental policy process as they influence the ability of actors to synthesize information and learn to manage complex risks. According to the Advocacy Coalition Framework, the dynamics of policy network formation lead to structures exhibiting belief‐oriented segregation—that is, a high correspondence between shared beliefs and policy‐relevant interactions. These structures may be produced through at least two pathways: belief homophily, where actors actively seek out connections with others sharing their belief system, and policy‐oriented learning, where connected actors become more similar in their beliefs. The cross‐sectional design of much policy research does not allow an examination of these potentially complementary forces that shape belief systems and networks. These dynamics are studied using data on policy beliefs and networking in the U.S. environmental risk policy subsystem across two time periods, 1985 and 2000 (N = 223). Results indicate strong homophily effects, but relatively weak learning effects in the evolution of this policy network. This study helps pave the way for additional research on the dynamics shaping policy networks and beliefs, and helps to clarify the differences between individual versus organizational contributions to policy network evolution.

Research on public views of biotechnology has centered on genetically modified (GM) foods. However, as the breadth of biotechnology applications grows, a better understanding of public concerns about non-agricultural biotechnology products is needed in order to develop proactive strategies to address these concerns. Here, we explore the perceived benefits and risks associated with five biotechnology products and how those perceptions translate into public opinion about the use and regulation of biotechnology in the United States. While we found greater support for non-agricultural biotechnology product, 70% of individuals surveyed showed no or little variation in their support across the products, indicating opinions about early GM products may be influencing the acceptance of emerging biotechnologies. We identified five common patterns of opinions about biotechnology and used machine learning models to integrate a wide range of factors and predict a respondent’s opinion group. While the model was particularly good at identifying individuals supportive of biotechnology, differentiating between individuals from the non- and conditionally-supportive opinion groups was more challenging, emphasizing the complexity of public opinions of emerging biotechnology products.