Nature-based solutions (NBSs) are often managed according to hydro-environmental characteristics that disregard the complex interactions between decision-makers, society, and the environment. Numerous barriers to NBS adoption have been identified as stemming from human behavior (e.g., community buy-in, political will, culture), yet we lack an understanding of how such factors interrelate to inform policy design. The identification of synergies and trade-offs among diverse management strategies is necessary to generate optimal results from limited institutional resources. System dynamics modeling (SDM) has been used within the environmental community to aid decision-making by bringing together diverse stakeholders and defining their shared understanding of complex behavior. While these approaches have enhanced collaboration efforts and have increased awareness of complexity, SDM models often result in numerous feedback loops that are difficult to disentangle without further, data-intensive modeling. When investigating the complexities of human decision-making, we often lack robust empirical datasets for SDM quantification. An alternative to SDM is fuzzy cognitive mapping (FCM), which combines the strengths of stakeholder knowledge with network theory to produce semi-quantitative scenarios of system change. However, sole reliance upon computer-simulated outputs may obscure our understanding of the underlying system behavior. Therefore, the aim of this study is to assess the applicability and strength of combining SDM and FCM to both identify areas of policy coherence from stakeholder engagement and also to explain the emergence of synergies and trade-offs according to causal loop logic. This framework is demonstrated through a case study of NBS policy-making and socio-institutional feedbacks in Houston, Texas, USA.