Decision Intelligence: A Better Way to Tackle Wicked Problems
Complex policy decisions often fail not from lack of effort or data, but because the frameworks used to make them are mismatched to the problems they're trying to solve. Decision intelligence (DI) is an approach designed specifically for this challenge, one that can model both the complexity of these systems and the uncertainty of our assumptions and predictions within them.
Applying Decision Intelligence to Conservation's Toughest Challenges
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.