This blog post introduces decision intelligence (DI) as a promising approach to addressing "wicked problems" in conservation and public policy, complex challenges where stakeholders have conflicting values, information is incomplete, and solutions create unpredictable ripple effects throughout interconnected systems. I explain how DI uses causal decision diagrams to map out cause-and-effect relationships and feedback loops before implementing interventions. Unlike traditional trial-and-error methods or machine learning models that only show correlation, DI combines human stakeholders with causal thinking and simulation tools to create decision-support systems that learn and improve over time. The post explains that while wicked problems like homelessness, antibiotic resistance, and species conservation can't be definitively "solved," decision intelligence offers a structured way to navigate their inherent complexity and make progressively better decisions.