Synthesizing and Structuring Behavioral Solutions in Complex Systems through a Pattern Language

Addressing existential threats such as climate change requires changing human behavior. Attempts to do so often generalize lessons-learned from sample populations in lab or field settings and apply these generalized solutions to new contexts. However, when new contexts vary from previous conditions, generalized solutions fail to function as intended or achieve impact at scale. This suggests that researchers and policymakers could benefit from another approach that synthesizes and structures behavioral solutions at multiple scales and contexts. A pattern language approach, which deconstructs solutions into their component parts, supplies greater flexibility and contextual awareness than simply transferring generalized solutions to new contexts. To test the feasibility of this approach, this research developed a pattern language from real-world cases (N=86) applying behavioral science to environmental challenges. Analysis of these cases revealed 22 patterns that formed a pattern language, which described patterns and their relationships at the individual, institutional, and systems scales. These findings suggest that a pattern language approach can support policymakers and researchers working to understand and change human behaviors within complex systems.